Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 470 appraisals
Journal of critical care
Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials
BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet their clinical impact remains uncertain, as most studies focus on model development rather than prospective evaluation. OBJECTIVES: To identify randomized controlled trials (RCTs) evaluating AI-based or CDSS interventions intended to influence real-time decision-making in adult intensive care units (ICUs) and to assess their effects on process and patient-centered outcomes. METHODS: We conducted a systematic review of randomized controlled trials (RCTs). PubMed, Embase, Cochrane CENTRAL, ScienceDirect, and IEEE Xplore were searched from inception to November 2025. Eligible studies evaluated AI-based or CDSS interventions in adult ICU patients. Study quality was assessed using RoB 2 and CONSORT-AI criteria. RESULTS: Ten RCTs were included, enrolling approximately 100,000 adult ICU patients. Five trials evaluated AI-based interventions and five evaluated CDSS. Eight trials showed improvements in at least one process measure, including earlier recognition of deterioration, improved protocol adherence, and physiological stability. However, patient-centered benefits were uncommon. Two trials reported reductions in mortality (a sepsis prediction model and a machine-learning-based early-warning system), while most studies showed no consistent effects on clinical outcomes. Reporting of AI-specific elements-dataset provenance, algorithm versioning, and human-AI interaction-was frequently incomplete. CONCLUSIONS: AI and CDSS interventions in adult ICUs are associated with improvements in process-related outcomes but show limited and inconsistent effects on patient-centered endpoints. These findings highlight a persistent gap between algorithmic innovation and clinical validation and underscore the need for pragmatic randomized trials with improved reporting and integration into clinical workflows.
3 Aug 2026
Read appraisal →Journal of pediatric surgery
Interpretable deep learning model for pediatric strangulated small bowel obstruction on CT: A multicenter study
OBJECTIVE: To develop and validate a deep learning-based multi-instance learning (MIL) model that integrates CT imaging and clinical data to improve the accuracy of discriminating between strangulated small bowel obstruction (StSBO) from simple small bowel obstruction (SiSBO) in pediatric patients. MATERIALS AND METHODS: This multicenter retrospective study, conducted between January 2018 and June 2024, enrolled hospitalized pediatric patients aged 1-14 years with a diagnosis of small bowel obstruction. We developed the clinical, multi-instance learning, and combined models based on CT and clinical features. Model performance was evaluated using receiver operating characteristic (ROC) analysis, while SHapley Additive exPlanations (SHAP) interpreted feature contributions. We further assessed whether MIL-assisted diagnosis could enhance physician accuracy in diagnosing StSBO. RESULTS: The study sample comprised 168 patients (mean age, 6.36 ± 3.97, 118 men). Ascites and closed-loop sign were identified as independent predictors of StSBO on multivariate analysis (both p< 0.05). The MIL model achieved the area under the curve (AUC) of 0.86 (95%CI 0.70-1.00), p= 0.01 in the external test cohort. The combined model showed the highest diagnostic performance (AUC 0.87, 95%CI 0.72-1.00, p= 0.01) in the external test cohort, with MIL-derived features showing predominant importance in SHAP analysis. Both junior and experienced radiologists and surgeon demonstrated improved diagnostic performance with MIL assistance, showing AUC increases of 16%, 2%, and 20%, respectively. CONCLUSIONS: The MIL model performed well in diagnosing StSBO, and clinical data integration improved its performance. As a decision support tool, the model may aid risk stratification and facilitate timely escalation of care in pediatric StSBO management.
3 Aug 2026
Read appraisal →Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Application of Telehealth and Artificial Intelligence in Military Health Care: A Promising Combination for the Future
BACKGROUND: Military and peace-support operations increasingly depend on medical capability delivered under difficult circumstances: distance, limited number of specialists, disrupted connectivity, and mass-casualty risk. Consequently, military health care systems operate during missions in uniquely constrained and high-risk environments, ranging from remote bases and naval platforms to active combat zones and humanitarian missions. These mission contexts demand rapid, reliable, and scalable medical solutions that can function despite limited personnel, infrastructure, and connectivity. METHOD: By reviewing existing literature and considering several different programs for military health care using telehealth and artificial intelligence (AI), the key performance indicators are explored to evaluate the synergy of telehealth and AI, following its implementation. RESULTS: Telehealth has become an increasingly important component of modern health care and holds the promise of increasing access to (special) health care without moving the patient but the information. Telehealth also enables continuity of care on missions and has already demonstrated its value in extending medical expertise across distance, while AI is evolving rapidly as a powerful enabler of decision support, automation, and predictive analytics in the medical field. As AI capabilities mature, several medical specialties may benefit, particularly in medical imaging, through faster triage, more consistent interpretation, and better prioritization of scarce specialist time. It also contributes to "decision support "while making proposals based on big data, machine learning and AI (e.g., in a Trauma registry). Telehealth provides the communication backbone that preserves these interactions and enables safe use of AI-enabled support in dispersed operations. CONCLUSIONS: The convergence of telehealth and AI in military health care will lead to a strategic capability with increased efficiency and enhance readiness, resilience, access to medical care, and quality of care for military personnel, including faster and more accurate diagnoses and better patient monitoring. Both technologies (AI and telehealth) are expected to continue to advance and play an even larger role in optimizing health care in the military.
3 Aug 2026
Read appraisal →Seminars in musculoskeletal radiology
Artificial Intelligence (Pattern Recognition) in Musculoskeletal Imaging: The Future or Hype?
Artificial intelligence and automated pattern recognition, in particular, have been described as the next frontier in musculoskeletal imaging. However, as the initial hype phase transitions into clinical reality, an essential evaluation of these technologies is required. This narrative review examines the dichotomy between a potential future in which artificial intelligence offers unprecedented efficiency in automatizing multiple tasks in musculoskeletal radiology, from fracture detection, automated segmentation, to automated reporting, versus the hype, characterized by deep learning models that lack generalizability across different scanner vendors and patient populations. We explore the black box nature of deep learning and the ethical implications of automation. By analyzing current barriers to deployment, including workflow integration and regulatory hurdles, this article argues that although artificial intelligence holds transformative potential for musculoskeletal radiology, its success depends on moving beyond narrow diagnostic tasks toward robust multi-institutional validation, to evolve from a speculative trend into an essential clinical copilot tool.
2 Aug 2026
Read appraisal →Brain and behavior
Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. METHODS: We systematically searched PubMed, Embase, Web of Science, and Cochrane Library up to September 2025. Studies developing and validating ML models for predicting HE, poor functional outcome (modified Rankin Scale 3-6), or mortality in adults with spontaneous ICH were included. Pooled concordance index (C-index), sensitivity, and specificity were calculated using a random-effects or bivariate model. RESULTS: Eighty-three studies (involving at least 136,840 patients) were included. Meta-analysis of model performance, derived predominantly from internal validation set, demonstrated that models integrating both clinical and radiomics features achieved the highest discriminative performance across key prognostic prediction tasks: predicting HE (pooled C-index 0.822, 95% confidence interval [CI] 0.789-0.855), poor functional outcome (C-index 0.850, 95% CI 0.830-0.869), and mortality (C-index 0.860, 95% CI 0.809-0.911). These findings should be interpreted with caution, as true external validation remains sparse. Logistic regression exhibited performance comparable to more complex ML algorithms. CONCLUSIONS: ML models, particularly integrated clinical-radiomics models, demonstrate strong performance for the prediction of outcomes in ICH. They hold significant potential to enhance risk stratification and guide personalized management, pending further validation in diverse cohorts.
1 Aug 2026
Read appraisal →International urology and nephrology
Acute kidney injury prediction and prognostication using machine learning
Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the application of artificial intelligence (AI) and machine learning (ML) to overcome these limitations by enabling earlier and more accurate prediction and prognostication of AKI. Many studies on AI and ML models used to predict and prognosticate AKI were included in the review. The focus was on models that analyze complex datasets, including real-time data streams like novel biomarkers and continuous vital signs, to achieve earlier and more accurate predictions than conventional methods. ML models show high predictive accuracy for AKI onset and outcomes across various clinical settings, including intensive care units, sepsis, and postoperative and postcontrast situations, with key findings like the successful integration of real-time data to reflect the evolving nature of kidney injury. AI and ML offer a powerful, proactive solution for AKI management. By leveraging diverse data, these technologies can significantly improve patient outcomes and reduce healthcare costs. While their promising performance warrants further exploration, successful clinical integration will require user-friendly platforms and continued validation.
1 Aug 2026
Read appraisal →RMD open
Anakinra for intravenous immunoglobulin-resistant Kawasaki disease: a systematic literature review
BACKGROUND: Approximately 10%-20% of patients with Kawasaki disease (KD) are resistant to intravenous immunoglobulins (IVIG) and are at increased risk of developing coronary artery aneurysms (CAA). A potential therapeutic role of anakinra (ANK) in refractory KD has been suggested. The aim of this work is to systematically review and critically appraise the available clinical evidence on the use of ANK in the treatment of IVIG-resistant KD, focusing on treatment indications, timing, dosage, efficacy on fever, inflammation, CAA and safety. METHODS: A systematic literature search was conducted across PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier and Google Scholar from inception to 9 October 2025, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included all study designs reporting outcomes of patients with KD treated with ANK. RESULTS: Thirty-two publications (31 identified through database search and one by cross-referencing) describing 81 patients were included. Most reports were single cases or small series, with two early-phase clinical trials. Treatment with ANK was associated with resolution of fever and systemic inflammation in 94.9% of patients. Complete CAA resolution was reported in 32% and stabilisation/dimensional reduction in 56.3%. No serious safety concerns were identified. However, these findings derive from heterogeneous and low-quality evidence and should be interpreted with caution. CONCLUSIONS: In IVIG-resistant KD, ANK was effective in controlling systemic inflammation, although more data are needed to assess its efficacy on coronary outcomes. While these findings are encouraging in supporting consideration of ANK for refractory KD, larger randomised clinical studies are warranted to define optimal ANK timing of introduction and dosing, and to evaluate its long-term efficacy on cardiac outcomes. PROSPERO REGISTRATION NUMBER: CRD420252129435.
31 July 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Non-operative management of traumatic occipitocervical distraction injuries in children: a systematic review
PURPOSE: Existing guidelines for the management of atlanto-occipital dislocation (AOD) recommend occipitocervical fusion (OCF) for these low frequency, high-consequence injuries. Recent literature has questioned this guidance and provided case examples of successful non-operative management of a more inclusive and broadly defined spectrum of injuries to the occipitocervical junction (OCJ) termed occipitocervical distraction injuries (OCDI). A systematic review of existing literature on this topic would inform efforts to investigate the safety and efficacy of non-operative management of these injuries. METHODS: We performed a systematic review based on preferred reporting items for systematic reviews (PRISMA) guidelines of English language publications to define and map literature related to non-operative management of pediatric traumatic OCDI. RESULTS: We identified 40 patients in 21 manuscripts meeting defined inclusion and exclusion criteria. 82.5% of patients were successfully managed with non-operative management alone. No patient declined neurologically during orthotic treatment or in relation to crossover to surgical management through a minimum of 3-month follow-up. CONCLUSIONS: This review supports the contention that non-operative management is both safe and effective in the management of OCDI. Further research to develop standardized diagnostic and treatment protocols for non-operative management of OCDI through registries and multi-center research efforts is needed.
31 July 2026
Read appraisal →Current psychiatry reports
Postpartum Suicidality beyond Depression: a Systematic Review of Risk Profiles and Prevention Gaps
PURPOSE OF REVIEW: Suicidal ideation and behavior during the postpartum period are increasingly recognized as important contributors to maternal morbidity and mortality, particularly in high-income countries. Although postpartum depression is a well-established risk factor, emerging evidence suggests that suicidality may also occur independently and therefore remain underrecognized in clinical practice. This systematic review aimed to synthesize current evidence regarding the prevalence of suicidal ideation and behavior in the postpartum period and to identify associated risk factors. RECENT FINDINGS: A systematic search of PubMed, Web of Science, EMBASE, and PsycINFO was conducted up to March 2025 following PRISMA guidelines. Twenty studies met inclusion criteria, encompassing 352,726 postpartum women across diverse international settings. Reported prevalence of suicidal ideation ranged from 2.2% to over 50%, depending on the population studied and assessment methods used. The most frequently identified risk factors included postpartum depression, anxiety, trauma history, intimate partner violence, unplanned pregnancy, low socioeconomic status, low social support, and adverse childhood experiences. Notably, several studies reported suicidal ideation even in the absence of clinical depression. Only a limited number of studies addressed preventive interventions. Postpartum suicidality appears to be a multifactorial phenomenon that cannot be explained solely by depressive symptomatology. The findings highlight the need for broader screening strategies that incorporate psychosocial and trauma-related factors in addition to depression. Integrated and trauma-informed approaches to screening and prevention may improve early identification of at-risk individuals. Future research should prioritize longitudinal designs and culturally sensitive investigations to better inform prevention and intervention strategies.
28 July 2026
Read appraisal →Medicine
Comparative safety and effectiveness of stents in drainage of pancreatic fluid collections guided by endoscopic ultrasonography: A systematic review and network meta-analysis
BACKGROUND: Endoscopic ultrasonography-guided drainage is an effective treatment for pancreatic fluid collections (PFCs). However, the optimal stent strategy for endoscopic ultrasonography-guided drainage of PFCs is still unclear. This study aims to compare the clinical outcomes of different stent types to identify the best option. METHODS: Databases including PubMed, EMBASE, and Cochrane were searched up to August 2024. The analysis focused on comparisons between bi-flanged metal stents (BFMS), double-pigtail plastic stents (DPPS), fully covered self-expanding metal stents (FCSEMS), lumen-apposing metal stents (LAMS), and the combination of LAMS and DPPS (LAMS + DPPS). RESULTS: A total of 38 studies, involving 4265 patients with PFCs, were analyzed. In all of these patients, LAMS (risk ratio [RR]: 1.07, 95% confidence interval [CI]: 1.02-1.13) and BFMS (RR: 1.08, 95% CI: 1.01-1.15) showed higher clinical success rates compared with DPPS. LAMS + DPPS reduced infection rates compared with BFMS (RR: 0.32, 95% CI: 0.10-0.99), DPPS (RR: 0.19, 95% CI: 0.06-0.62), and FCSEMS (RR: 0.23, 95% CI: 0.07-0.76). Within the pancreatic pseudocysts subgroup, LAMS (RR: 1.08, 95% CI: 1.01-1.16) and LAMS + DPPS (RR: 1.24, 95% CI: 1.03-1.48) demonstrated higher clinical success rates compared with DPPS. Similarly, in the walled-off necrosis subgroup, LAMS (RR: 1.10, 95% CI: 1.02-1.17), LAMS + DPPS (RR: 1.19, 95% CI: 1.01-1.41), FCSEMS (RR: 1.14, 95% CI: 1.03-1.26), and BFMS (RR: 1.11, 95% CI: 1.03-1.20) all showed higher success rates compared with DPPS, with BFMS exhibiting a lower bleeding rate than DPPS (RR: 0.32, 95% CI: 0.18-0.99). CONCLUSION: Newer metal stents, including BFMS, LAMS, and LAMS + DPPS, offer advantages over plastic stents like DPPS alone. BFMS is preferable when bleeding risk is a concern, while LAMS + DPPS is more effective in reducing infection rates in PFCs.
26 July 2026
Read appraisal →Medicine
Role of antazoline in cardioversion of atrial fibrillation: A PRISMA-compliant systematic review and meta-analysis
BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Antazoline, a first-generation antihistamine with antiarrhythmic properties, has shown efficacy in the rapid conversion of recent-onset AF to sinus rhythm; however, direct comparative evidence against standard antiarrhythmic agents remains limited. This study aims to systematically evaluate the efficacy and safety of antazoline for pharmacological cardioversion of AF, in comparison with standard antiarrhythmic agents such as amiodarone and propafenone. METHODS: The methodology of this systematic review and meta-analysis adhered to the preferred reporting items for systematic reviews and meta-analyses guidelines. A systematic search was conducted in ScienceDirect, Embase, PubMed, and Cochrane Library from inception to April 2025, including citation and reference screening. Statistical analyses were performed using R version 2025.05.0 + 496. RESULTS: From 172 records, 5 studies comparing antazoline with propafenone (n = 5) or amiodarone (n = 3) for AF were included. For antazoline vs propafenone, conversion to sinus rhythm favored antazoline (risk ratio [RR] 1.13 [95% confidence interval (CI): 1.04-1.23], P = .017). No significant difference was observed in time to conversion (mean difference -10.98 [95% CI: -124.93-102.98], P = .436), bradycardia (RR 0.84 [95% CI: 0.35-2.03], P = .478), hypotension (RR 0.40 [95% CI: 0.14-1.12], P = .056), or other adverse events (RR 1.24 [95% CI: 0.01-247.13], P = .694). For antazoline vs amiodarone, conversion to sinus rhythm favored antazoline (RR 1.22 [95% CI: 1.08-1.36], P = .018, I2 = 0%), with no significant differences in bradycardia (RR 1.36 [95% CI: 0-1072357.09], P = .823) or hypotension (RR 1.05 [95% CI: 0.04-26.16], P = .877). CONCLUSION: Antazoline might be more effective than propafenone and amiodarone in achieving sinus rhythm in AF, with no significant differences in safety outcomes.
26 July 2026
Read appraisal →Emergency medicine journal : EMJ
Are rapid diagnostic tests reliable for the detection of malaria in the emergency department?
A short systematic review assessed the diagnostic accuracy of malaria rapid diagnostic tests in febrile adults from malaria-endemic regions. A database search of MEDLINE, EMBASE, Cochrane and Google Scholar returned 138 papers. Six papers met the inclusion criteria and were included in our analysis.
23 July 2026
Read appraisal →Current heart failure reports
The Future of Imaging in Heart Failure: Toward Precision Phenotyping, Integration, and Intelligence.
PURPOSE OF REVIEW: Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requiring aetiological clarification, in which cardiac imaging is central. As the opening article of this journal's 'Imaging in Heart Failure' section, this review surveys the technologies currently reshaping HF imaging and sets out the section's scope and priorities, framing the shift from a descriptive, modality-siloed practice toward an integrated, predictive, patient-specific discipline. RECENT FINDINGS: Artificial intelligence (AI) now delivers expert-level echocardiography automation, guides image acquisition by novices in resource-limited settings, detects aetiologies such as transthyretin amyloid cardiomyopathy from a single acquisition and enables deep phenotyping through radiomics and vendor-agnostic strain analysis. Handheld, AI-enabled point-of-care ultrasound extends imaging-guided triage beyond the echocardiography laboratory. Cardiovascular magnetic resonance (CMR) advances - parametric mapping, four-dimensional flow, diffusion tensor imaging, spectroscopy, and accelerated reconstruction - broaden tissue and metabolic characterisation, including patients with implanted devices. Molecular imaging with novel positron emission tomography tracers and hyperpolarised magnetic resonance is moving from depicting the structural consequences of disease to imaging active pathobiology, while photon-counting computed tomography and image-derived digital twins support one-stop structural assessment and in-silico prediction of therapy response. The convergence of AI, molecular imaging and advanced precision is transforming HF imaging from better pictures into smarter, integrated, personalised data that directly inform care. Realising this promise will require rigorous validation, attention to algorithmic bias and generalisability, demonstrated cost-effectiveness, curricular reform, and equitable access. This section aims to critically appraise these innovations and their translation into practice.
23 July 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
The association between pancreatic enzyme serum concentrations and blunt pancreatic trauma: a systematic review.
PURPOSE: Blunt pancreatic trauma is uncommon. When suspected, serum amylase and lipase are measured, but their diagnostic value remains uncertain. This systematic review evaluated the diagnostic performance of serum amylase and serum lipase in detecting blunt pancreatic trauma in adults. METHODS: MEDLINE, Embase, Scopus and Cochrane Central were searched for studies evaluating serum amylase and/or lipase in adults with blunt pancreatic trauma. Observational studies reporting diagnostic accuracy or clinical outcomes were included. Two reviewers independently screened studies and assessed risk of bias using the Newcastle-Ottawa Scale. RESULTS: Four observational studies met inclusion criteria. A total of 4937 patients with blunt abdominal trauma, including 150 with pancreatic injury were investigated. Two studies evaluated serum lipase alone and two compared amylase and lipase. Pancreatic injuries were graded according to the American or Japanese Association for the Surgery of Trauma. Early serum amylase measurement (< 90 min post-injury) demonstrated low sensitivity. In contrast, testing performed more than 3 h after injury has shown improved diagnostic accuracy with one study suggesting combined serum amylase and lipase testing can achieve a sensitivity of 85% and specificity of 100%. CONCLUSION: Early serum pancreatic enzymes measurement does not appear to aid initial management decisions following blunt abdominal trauma. Delayed testing (> 3-6 h), particularly using lipase or combined lipase and amylase showed improved diagnostic performance. Prospective multicentre validation is needed to define their role in guiding further imaging after equivocal or negative initial CT.
22 July 2026
Read appraisal →Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Natural language processing tool for extracting information about opioid overdoses in the USA from case narratives in the violent death reporting system.
BACKGROUND: Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We aimed to develop a prototype tool using the principles of natural language processing that can extract information from the death records of drug overdose victims. METHODS: Data were obtained from the Violent Death Reporting System on drug overdose deaths. Narratives were manually labelled for 12 attributes of interest, totalling 82 labels about the circumstances of the overdose. Narratives were passed through the 'Excel Extractor' to identify and extract a target phrase and subsequently map the extracted phrase to predetermined code values. The output from the Excel Extractor was compared with manually labelled data to determine accuracy. Performance was compared against multiple machine learning models. RESULTS: The Excel Extractor performed well across the attributes of interest, achieving an F1 Score over 0.8 on nine of the 12 attributes. The Excel Extractor was the highest performing model on seven of the 12 attributes. The Excel Extractor achieved an F1 Score of 0.8 or higher on 46 of 82 (56%) of the labels, and a score of 0.9 or higher on nearly one-third (25 out of 82) of the labels. CONCLUSION: This work demonstrates it is feasible to develop a spreadsheet-formula-based natural language processing tool to accurately extract information about drug overdose deaths from narratives; for most attributes, a rule-based search performs well or better than deep learning. The Excel Extractor has the potential to streamline data abstraction for epidemiologists gathering data about drug overdose deaths.
21 July 2026
Read appraisal →Neurosurgical review
Prognostic value of automated pupillometry in traumatic brain injury: a systematic review and meta-analysis
Traumatic brain injury (TBI) remains a major global cause of mortality and long-term disability. Early prognostication is critical for guiding management decisions. Quantitative pupillometry (QP), using automated devices such as the NeurOptics NPi-200/300, enables bedside measurement of pupillary reactivity through indices like the Neurological Pupil Index (NPi), constriction velocity (CV), and dilation velocity (DV). We conducted a PRISMA-compliant systematic review and meta-analysis to evaluate the prognostic value of QP in adults with TBI. Observational studies of quantitative pupillometry within 72 h of traumatic brain injury in adult ICU patients were included. Studies limited to mild TBI were excluded, although mixed-severity cohorts were eligible. Primary outcomes were mortality and poor functional outcome at 3-6 months (GOS ≤ 3 or mRS ≥ 3). Secondary analyses assessed differences in pupillometry metrics according to intracranial hypertension defined by direct ICP measurements (> 20-25 mmHg) or proxy markers reflecting ICP-directed interventions. Odds ratios were pooled for dichotomous outcomes and mean differences for continuous outcomes using random-effects models, with heterogeneity quantified by I². Four studies (n = 354) reported mortality data. Abnormal quantitative pupillary metrics (most commonly NPi < 3 or absent reflexes) were associated with increased odds of in-hospital death (OR 1.84, 95% CI 1.01-3.35; I² = 84%). Five studies (n = 384) assessed functional outcomes, showing significantly greater odds of poor outcome among patients with abnormal pupillary metrics (OR 3.64, 95% CI 1.63-8.10; I² = 39%). Three studies (n = 190) evaluated intracranial pressure, demonstrating lower NPi values in patients with intracranial hypertension compared with those without elevated ICP, though this difference did not reach statistical significance (MD - 1.05, 95% CI - 2.65 to 0.54). Early quantitative pupillometry is associated with increased mortality and worse functional outcomes in TBI in observational studies. Lower quantitative pupillary metrics were also observed in the presence of intracranial hypertension. Despite substantial heterogeneity, these findings support its potential as a prognostic tool. Further standardization, validation and controlled studies are needed to refine its clinical application.
21 July 2026
Read appraisal →Scientific reports
Walking stability prediction for pedestrians using gait energy images and hybrid deep and few-shot learning models
The prediction and recognition of unstable human walking patterns are of high importance for active video surveillance, smart environments, and assistive healthcare, particularly for fall detection in the elderly. This research investigates the utility of Gait Energy Images (GEIs) combined with deep learning, vision transformers, and few-shot learning architectures to enhance the classification of stable and unstable pedestrian walking patterns. We evaluate and compare twelve methodologies: six classical or feature-based machine learning models (Linear SVM, HOG + SVM, LBP + RBF-SVM, Random Forest, XGBoost, and an adapted GaitSet baseline), three deep learning models (MobileNet, Vision Transformer (ViT), and YOLOv8-cls), and three episodic few-shot learning techniques (Prototypical, Matching, and Relation Networks) under data-scarcity regimes. To facilitate this evaluation, we introduce the Unstable and Stable Walking Pedestrian (USWP) dataset, constructed by fusing and harmonizing sequences from seven public action recognition databases, containing 3250 unique GEIs with a subject-independent evaluation protocol to prevent identity-based domain leakage. Our experiments demonstrate that the YOLOv8-cls model achieves an overall accuracy of 96.92% (97.14% on Loss-of-Balance anomalies and 94.67% on Active Motion anomalies), significantly outperforming the conventional Linear SVM baseline (75.38%) and MobileNet (91.08%). Conversely, Relation Networks exhibit lower few-shot performance (71.08%) due to optimization complexities in learning similarity metrics from sparse data. Leave-One-Dataset-Out (LODO) cross-validation reveals an average generalization accuracy of 83.28%, indicating significant domain bias across source databases and underscoring that within-dataset evaluations overestimate real-world generalization. Computational complexity analysis shows that MobileNet provides an optimal trade-off for real-time edge deployment (4.2 ms latency), while preprocessing ablation studies demonstrate that integrating the Segment Anything Model (SAM) with MediaPipe-derived Regions of Interest (ROIs) yields a 12.50% absolute improvement in accuracy by eliminating background noise.
19 July 2026
Read appraisal →Renal failure
Effects on mortality of different blood purification techniques in sepsis patients: an umbrella review of systematic reviews and meta-analyses
Sepsis remains a leading cause of mortality worldwide, and extracorporeal blood purification has been widely implemented despite ongoing uncertainty regarding its survival benefit. We conducted an umbrella review of 42 systematic reviews and meta-analyses evaluating mortality across major extracorporeal blood purification modalities. Mortality estimates generally favored extracorporeal blood purification over conventional care, but effect sizes and consistency varied substantially by modality, and the overall certainty of evidence for mortality was low. At the review level, polymyxin B hemoperfusion showed a relatively consistent direction toward lower mortality, whereas renal replacement therapy-based strategies were typically closer to the null. However, most included reviews were of low or critically low methodological quality, with downgrading driven by risk of bias, inconsistency, and potential publication bias. Therefore, these findings should be interpreted cautiously and should not be considered definitive evidence of survival benefit. They highlight persistent evidentiary fragility and underscore the need for rigorously designed, phenotype-informed trials to clarify modality-specific effects in sepsis. What is known: Extracorporeal blood purification has been widely used as an adjunctive therapy for sepsis, but its mortality benefit remains uncertain across different modalities.What this study adds: This umbrella review synthesizes evidence from 42 systematic reviews and meta-analyses and shows that mortality estimates generally favor extracorporeal blood purification, although effects vary substantially by modality and the overall certainty of evidence remains low.Potential impact: These findings support cautious, modality- and phenotype-informed interpretation of extracorporeal blood purification in sepsis and highlight the need for rigorously designed trials with standardized protocols.
19 July 2026
Read appraisal →Studies in health technology and informatics
Digital Twins for Decision Making: Combining Ambulance and ED Facility Resources
Emergency department (ED) walk-ins and ambulance arrivals are often modelled independently, overlooking their interdependence and the impact of fixed treatment space (TS) capacity. An established discrete event simulation algorithm has been tailored as the basis of a digital twin (DT). Driven by regular streams of incoming ED presentations, patient off-stretcher time (POST), ambulance bay and TS occupation across a 24-hour period contingent on patient flow and ED TS capacity was simulated. The simulation was calibrated using 19,570 ambulance records and data from a 250-bed hospital (2021-2022). Scenarios of five ambulance bays with 43, 38, and 34 TS capacities were investigated, revealing patterns of TS depletion and recovery, including cases where recovery failed within 24 hours, underscoring the sensitivity of patient flow to resource constraints and the value of DT-based simulation for capacity planning.
17 July 2026
Read appraisal →Pulmonology
Systematic review of prognostic scores and individual predictor variables for short-term mortality after acute pulmonary embolism.
BACKGROUND: For patients with acute pulmonary embolism (PE), assessment of prognosis helps with risk stratification, triage for level of care, management strategy, and communication among healthcare workers and patients. We sought to identify prognostic models and individual factors associated with short-term outcomes after acute symptomatic PE. METHODS: We performed a systematic review of prognostic factors for PE, searching MEDLINE, Embase, and Web of Science for records up to 1 June 2024. Studies of any design evaluating potential prognostic models or individual variables (not contained in the models) associated with short-term mortality after acute PE were included. RESULTS: We identified 314 studies that included 2,495,115 patients. Of these, 225 studies included 2,267,952 patients and evaluated 24 prognostic models for patients with acute PE. The most frequently used validated models were the simplified Pulmonary Embolism Severity Index (sPESI) (127 studies), the original PESI (79 studies), and the European Society of Cardiology risk schema (34 studies). Each model-development study had a c-index ≥ 0.7. Individual factors associated with prognosis included older age, presence of coexisting conditions, abnormal clinical signs and symptoms, clot burden, markers of right‑ventricle dilatation/dysfunction and myocardial injury, altered laboratory results indicating impaired haemodynamics, and tests that assess for systemic inflammation. Pooled odds ratios for variables not contained in any eligible prognostic model ranged from 1.43 (for D-dimer) to 2.65 (for right heart thrombi). CONCLUSIONS: This systematic review identified 24 prognostic models and 18 individual variables distinct from the prognostic models associated with short-term mortality after acute PE.
17 July 2026
Read appraisal →Critical care explorations
Drug Clearance and Dosing During CytoSorb Hemoadsorption: A Systematic Review
OBJECTIVE: CytoSorb (CS) hemoadsorption is increasingly used in critical care and cardiac surgery to remove inflammatory mediators and select drugs. This systematic review aimed to summarize available evidence on intentional drug removal as well as unintentional drug removal during CS therapy. DATA SOURCES: PubMed, congress abstracts, and CytoSorbents online literature database. STUDY SELECTION: Eligible studies included benchtop, animal, and human pharmacokinetic investigations of drug removal during CS therapy as well as clinical reports describing CS use for drug overdose. DATA EXTRACTION: Data from the eligible studies were extracted independently by two authors into Microsoft Excel. DATA SYNTHESIS: Data were identified for drugs across multiple therapeutic classes, including analgesics, antiarrhythmics, anticonvulsants, antidepressants, antihypertensives, anti-infectives, antithrombotics, anxiolytics, and immunosuppressants. Drugs were categorized into low (< 30%), moderate (30-60%), or high (> 60%) removal rate, or by the relative increase in clearance attributable to CS: negligible (< 25%), low (25-100%), moderate (100-400%), or high (> 400%). Based on the assembled data and considering drug-, patient-, and circuit-specific factors, general dosing recommendations for concomitant medications were developed. CONCLUSIONS: CS therapy can alter drug concentrations for select agents with variable clinical relevance. CS can reduce perioperative bleeding risk through antithrombotic removal and treat certain drug overdoses. Unintentional drug removal during CS therapy may occur and practical guidance is provided for drugs with known susceptibility to removal.
16 July 2026
Read appraisal →Biosensors & bioelectronics
Functional wearable hydrogel microneedle platform for continuous ketone monitoring: Translating from rodents to humans.
Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes, driven by excessive ketone production; it is most common in type 1 diabetes. Continuous ketone monitoring (CKM) could enable earlier detection and prevention of DKA, yet no wearable CKM solution is clinically available. We present a wearable CKM patch that couples hydrogel microneedles (HMNs) for painless interstitial fluid (ISF) access with an enzymatic, electrochemical biosensor for on-patch detection of ketone bodies. The HMN patch and ketone biosensor were individually characterized and optimized in vitro, then integrated into a single platform. We validated the performance of the integrated CKM sensing patch across species: in vivo testing in healthy and diabetic rats, translation to a swine model, and a pilot evaluation in human participants. The CKM sensing patch reliably tracked dynamic ketone fluctuations in all models. To our knowledge, this is the first HMN-based biosensor tested in humans. These results establish a minimally invasive, wearable approach for continuous ketone tracking with the potential to transform outpatient DKA monitoring and early intervention.
16 July 2026
Read appraisal →PloS one
Edge-intelligent safelink-V2X: A low-latency cooperative framework for real-time vulnerable road user protection.
The protection of Vulnerable Road Users (VRUs) remains a major challenge in modern transportation safety, as onboard line-of-sight and adverse weather conditions limit conventional onboard sensors. Existing systems that rely solely on vehicle-based sensing or on isolated communication struggle to provide timely, accurate alerts in dynamic urban environments. To address these shortcomings, this paper introduces SafeLink-V2X, a comprehensive Vehicle-to-Everything Cooperative Warning Framework designed to enhance safety for pedestrians, cyclists, and scooter riders. SafeLink-V2X employs Cellular Vehicle-to-Everything (C-V2X) and Dedicated Short-Range Communications (DSRC) protocols to enable direct data exchange of location, velocity, and heading between connected vehicles, smart infrastructure, and VRUs via smartphones or wearable tags. By applying sensor fusion and machine learning-based conflict prediction, the system identifies potential collision points and issues real-time, context-aware warnings through vehicle HMIs and VRU devices, promoting immediate evasive action. Evaluation on urban intersection simulations (detailed in Section 5) demonstrates that SafeLink-V2X reduces simulated collision probability by up to 91.4%, increases situational awareness measures by 44%, and lowers end-to-end alert latency by 30% compared to baseline onboard-only and communication-only systems under the same conditions.
12 July 2026
Read appraisal →PloS one
A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography
Continuous respiratory monitoring enables early detection of physiological deterioration, yet conventional capnography remains impractical for prolonged use. Photoplethysmography (PPG) offers a non-invasive alternative that encodes respiratory information through baseline wander (respiratory-induced intensity variation; RIIV), amplitude modulation (respiratory-induced amplitude variation; RIAV), and frequency modulation (respiratory-induced frequency variation; RIFV) of the pulsatile waveform. Existing PPG-based deep learning approaches, whether operating on the raw signal or on these physiological modulations, are limited to single-task architectures that estimate either respiratory rate or reconstruct the respiratory waveform in isolation, without jointly addressing both outputs. We propose a late fusion multi-task framework in which dedicated encoder branches independently process each modulation before fusion, and dual decoders simultaneously reconstruct the respiratory waveform and estimate the respiratory rate. The framework was evaluated on the CapnoBase (n = 42) and BIDMC (n = 52) benchmarks across multiple training strategies. For respiratory-rate estimation, the best transfer-learning configurations achieved a mean absolute error (MAE) of 2.27 bpm on CapnoBase and 1.33 bpm on BIDMC. For waveform reconstruction, the corresponding MAE values were 19.00% and 20.90%, with moderate correlations (r = 0.662 and r = 0.591, respectively). Sequential transfer learning consistently outperformed all other strategies, whereas pooled training degraded both outputs, demonstrating that capnography-derived and impedance-derived waveforms are not interchangeable training targets. These findings establish that short-window PPG can simultaneously support respiratory-rate estimation and waveform reconstruction, when reference signal compatibility is explicitly addressed in multi-task training.
12 July 2026
Read appraisal →International journal of qualitative studies on health and well-being
Between duty and constraint: a qualitative systematic review of healthcare providers' ethical challenges and moral stressors in caring for undocumented migrants
PURPOSE: Healthcare providers play a critical role in delivering care to undocumented migrants, who face systemic barriers to healthcare access. While research has documented undocumented migrants' legal and policy barriers, less is known about the ethical challenges and moral stressors experienced by providers. This systematic review synthesizes qualitative studies on providers' experiences when delivering care to undocumented migrants. METHODS: The review followed PRISMA guidelines. PubMed, Embase, CINAHL, and the Cochrane Library were searched for relevant qualitative studies. Studies were screened by title, abstract, and full text using predefined inclusion and exclusion criteria. Quality assessment was conducted using the CASP checklist. Data were synthesized using the Qualitative Analysis Guide of Leuven (QUAGOL), integrating Graneheim and Lundman's approach to qualitative content analysis. RESULTS: The systematic search identified 37 qualitative studies. Analysis revealed 58 themes and subthemes, organized under five key concepts: experiences, perceptions, attitudes, practices and coping mechanisms, and ethical challenges. Providers reported moral distress, emotional exhaustion, and professional dilemmas arising from legal constraints, resource limitations, and conflicting obligations. Ethical tensions centered on beneficence vs. non-maleficence, professional duty vs. legal compliance, and the moral dilemma of deservingness. Across these findings, the synthesis identifies ethical burden-shifting as a central analytical contribution: restrictive systems transfer the moral and practical consequences of exclusionary arrangements onto providers and undocumented migrants at the point of care. CONCLUSION: This review shows that providers' ethical challenges are not only individual clinical dilemmas but structurally generated moral stressors. Addressing these challenges requires structural interventions, including policy reforms that reconcile professional ethics with legal constraints and institutional support to mitigate moral distress.
10 July 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
Initial surgical management of injuries to the upper extremities for patients with multiple and/or severe injuries - a systematic review and clinical practice guideline update.
PURPOSE: Our objective was to update the evidence-based and consensus-based recommendations for the initial surgical management of upper extremity injuries in patients with suspected multiple and/or severe injuries based on current evidence. This guideline topic is part of the 2025 update of the German Guideline on the Treatment of Patients with Multiple and/or Severe Injuries. METHODS: MEDLINE and Embase were systematically searched to September 2024. Further literature reports were obtained from clinical experts. Randomised controlled trials (RCTs) or observational studies reporting risk-adjusted outcomes were included if they compared early versus delayed surgical treatment for fractures, vascular injuries, or nerve injuries affecting the upper extremities in patients with multiple and/or severe injuries. Studies comparing limb salvage versus amputation or comparing amputation criteria for the upper extremities were also included. We considered patient-relevant outcomes such as mortality and limb salvage, as well as the sensitivity and specificity of scores for predicting upper extremity amputation. Risk of bias was assessed at the outcome level using ROBINS-I for observational studies and AMSTAR-2 for systematic reviews. We used available meta-analyses if possible; alternatively, we synthesised the evidence narratively. We used GRADE to rate the certainty of evidence. Expert consensus was used to develop recommendations and determine their strength. RESULTS: Among 2498 records screened for eligibility, four studies were included. Observational studies addressed the timing of humeral fixation, the timing of revascularisation, or compared limb salvage with amputation. A systematic review evaluated scoring systems for mangled upper limb salvage. Four recommendations were modified, and one consensus-based recommendation was added. All five recommendations achieved strong consensus. CONCLUSION: The timing of surgical intervention for upper extremity injuries in polytrauma patients should be individualized, based on the patient's physiological status, injury pattern, and the risks and benefits of early versus delayed surgery. Although early surgery may improve functional outcomes and reduce complications, adequate patient stabilization is essential before proceeding. Limb salvage should be attempted for patients with a MESS score of less than seven, provided the patient is sufficiently stable and depending on the condition of the amputated limb.Although the guideline addresses patients with multiple and/or severe injuries, the scarce evidence, heterogeneity of the included studies and the limited representation of true polytrauma cohorts may restrict the applicability of the findings to the broader critically injured trauma population.
10 July 2026
Read appraisal →Metabolomics : Official journal of the Metabolomic Society
Clinical lipidomics in bacterial sepsis: a systematic review of serum and plasma evaluations
BACKGROUND: Lipid metabolism plays a central role in host-pathogen interactions and immune regulation during bacterial sepsis, with its dysregulation contributing to organ failure and mortality. Blood-based lipidomics has emerged as a promising approach for identifying diagnostic and prognostic biomarkers in sepsis. However, discrepancies in lipid profiles between serum and plasma remain a major challenge for clinical translation. This systematic review synthesizes lipidomic evidence from serum and plasma in bacterial sepsis patients to identify reproducible lipid biomarkers, evaluate methodological heterogeneity and assess associations with clinical outcomes. METHODS: A systematic search of five databases (Scopus, Pubmed, Ovid MEDLINE, Web of Science and Google Scholar) identified nine eligible studies published between 2016 and 2025 that applied untargeted or targeted lipidomics on human serum and plasma samples from adult bacterial sepsis patients (PROSPERO: CRD420251086177). RESULTS: Across both matrices, reproducible candidate lipid biomarkers included triacylglycerols (TAG), phosphatidylcholines (PC), lysophosphatidylcholines (LPC), sphingomyelins (SM) and ceramides. Synthesized findings revealed characteristically distinct directional trajectories: consistent elevation of TAG, bidirectional shifts of PC, depletion of LPC, bidirectional shifts of SM and elevation of SM. Preliminary trend within the literature indicates that serum-based studies more frequently captured prognostic alterations, whereas plasma-based studies often focused on diagnostic discrimination. Substantial heterogeneity was observed in sample handling, lipid extraction protocols and analytical platforms. CONCLUSIONS: Overall, both matrices offer valuable clinical insights, though these apparent differences in diagnostic or prognostic utility may reflect primary study designs rather than intrinsic matrix superiorities. Methodological heterogeneity limits reproducibility, highlighting the need for standardized workflows to enable translation of sepsis lipid biomarkers.
9 July 2026
Read appraisal →Transactions of the Royal Society of Tropical Medicine and Hygiene
Predicting challenges and opportunities in improving snakebite envenoming management in Ghana
BACKGROUND: Snakebite envenoming (SBE) is a neglected tropical disease and a major public health crisis in Africa. We integrated a maximum difference experimental design and machine learning (ML) models to predict challenges and opportunities for improving SBE management in Ghana. METHODS: This cross-sectional study included 137 healthcare workers, identified through multistage sampling, from August to December 2024 in two districts of the Eastern Region, Ghana. We employed five-fold cross-validation and analysed the dataset using 12 ML models. RESULTS: The most significant obstacle to managing SBE was 'delayed health-seeking behaviour by patients', followed by 'high cost of treatment' and 'inadequacy/shortage of antivenom'. On the opportunity side, 'increasing public awareness and knowledge about snakebite prevention and initial management before reaching a healthcare facility' was deemed crucial. Other opportunities such as 'conducting research into alternative management for snakebite to complement or replace antivenoms' and 'increasing awareness and knowledge about snakebite management among healthcare workers' were also recognized as trade-offs. CONCLUSIONS: This study significantly contributes to the existing research on artificial intelligence/ML, as previous studies have not quantified the challenges and opportunities in managing SBE. Policymakers and healthcare providers can use these findings to implement strategies such as promoting better health-seeking behaviours, subsidizing treatment costs, ensuring access to effective antivenom and raising public awareness about snakebite prevention and initial care.
9 July 2026
Read appraisal →JMIR infodemiology
Self-Reported Tianeptine Experiences on Reddit: Natural Language Processing-Assisted Qualitative Study
This study, using natural language processing and manual thematic analysis of Reddit posts, revealed a rapid rise in discussions about tianeptine, with posts frequently reporting dependence, withdrawal, and coingestion with other unregulated substances, highlighting tianeptine as an emerging public health concern.
8 July 2026
Read appraisal →Psychological medicine
Extreme heat and mental health: systematic review and qualitative investigation of risk and protective factors
BACKGROUND: Extreme hot weather poses increasing risks to mental health. Yet, factors affecting vulnerability are under-researched. This mixed-method study integrates a systematic review and qualitative investigation to identify risk and protective factors for heat-related mental health issues, leading to the co-development of a screening tool. This could inform future research and, pending validation in clinical settings, support mental health professionals in assessing vulnerability among service users. METHODS: We searched PubMed and Web of Science for publications on extreme heat, mental health, and risk/protective factors. In addition, we conducted six focus groups with 21 people with lived experience of heat and/or mental illness and 12 healthcare professionals. Transcripts were analyzed using thematic content analysis and informed the co-development of the screening tool. RESULTS: Out of 764 articles identified by the systematic review, 47 were included. Evidence emerged for age, sex, existing mental illness, ethnicity, and socioeconomic status as risk factors. However, findings were inconsistent between studies, likely due to differences in study population and methodology. Protective effects included good physical health, social support, and exposure to green spaces. Our qualitative investigation identified additional risk and protective factors related to: (1) behavioral adaptability, (2) personal heat sensitivity, and (3) disparities in heat exposure. The resulting screening tool, HEAT-MH (Heat Exposure Assessment Tool for Mental Health), contains 15 questions on previous experiences of heat, general health, and lifestyle. CONCLUSIONS: The mental health impacts of extreme heat depend on a range of risk and protective factors, including demographic, socioeconomic, health, and lifestyle characteristics.
8 July 2026
Read appraisal →JMIR medical informatics
Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study
BACKGROUND: Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses problems such as unequal sampling time intervals and frequencies, making it difficult to align the data without losing information for input into models. Meanwhile, due to information loss in the dataset, it is also difficult for the model to effectively analyze the overall data variation and predict the complex and dynamic conditions of emergency patients in the future. OBJECTIVE: This study aimed to address the impact of issues such as missing values and irregularly sampled time intervals on prediction results. This paper proposes a method that converts time-series data into a graph network structure and develops a prediction model for graph data to better support complex tasks. METHODS: In this paper, feature channels and measurement times in time-series data are constructed as nodes, and feature measurements at time points are constructed as edge weights. The point feature space and edge weight space are extracted by a graph convolutional neural network and a gated convolutional attention mechanism network, respectively, and feature fusion is carried out under the action of the learnable head. RESULTS: The proposed method was tested on 4 datasets and compared with both irregularly sampled time-series predictors and state-of-the-art methods for complete data. In addition, a dedicated analysis was conducted leveraging data from 5 key clinical treatment phases. In single-target classification and single-target regression tasks, the method proposed in this study outperforms other comparative methods in most experiments (accuracy=0.74-0.88, area under the curve=0.70-0.87, and mean absolute error=0.038-0.061). Meanwhile, this study constructs clinical phenotypes during patients' hospitalization as multitarget prediction tasks, and the proposed model also outperforms comparative methods in multitarget classification tasks (mean accuracy=0.75-0.91 and mean area under the curve=0.70-0.75). Ablation experiments verify the effectiveness of fusion between the node feature space and the edge feature space. The interpretability analysis of time points identifies key features that require focused attention for patients across different target tasks and time periods. CONCLUSIONS: The proposed model can effectively analyze medical data with missing values and uneven sampling. It achieves high prediction accuracy in both regression prediction tasks and classification tasks. The graph network-based model structure endows the model with better interpretability, enabling effective exploration of factors that trigger changes.
5 July 2026
Read appraisal →JMIR research protocols
Evaluation and Comparison of Latent Health Risk Prediction Models for Clinical Triage: Protocol for a Mixed Methods Study
BACKGROUND: Clinical triage requires integrating multiple information sources to identify patients at risk of deterioration. Tools capturing global health assessments beyond disease-specific scores are being developed using either bottom-up aggregation of simple indicators or top-down machine learning from large datasets. Their alignment with expert clinical judgment remains poorly characterized. OBJECTIVE: This study evaluates 2 latent health measurement approaches: Frailty Index-laboratory, a transparent bottom-up tool aggregating laboratory abnormalities via deficit accumulation theory, and ETHOS-ARES (Enhanced Transformer for Health Outcome Simulation-Adaptive Risk Estimation System), a transformer-based foundation model generating multidimensional patient representations from electronic health records. We assess whether each tool's severity rankings align with clinical consensus and whether they offer utility in triage decisions. METHODS: In this 3-phase mixed methods study, at least 30 clinicians across hospital specialties reviewed 20 emergency department presentations derived from Medical Information Mart for Intensive Care IV-Emergency Department. Phase 1 compared unaided clinician severity and urgency judgments against model outputs using Spearman rank correlation, with a Turing-inspired indistinguishability test assessing whether model rankings fell within the distribution of clinician assessments. Phase 2 allocated clinicians to receive Frailty Index-laboratory or ETHOS-ARES outputs, measuring anchoring effects via within-person pre-post comparisons and exploring clinical utility through semistructured interviews analyzed using the Framework Method. RESULTS: Ethics approval was granted in June 2025 (KCL Research Ethics Office; MRSP-24/25-48707). Recruitment began in October 2025 (32 clinicians recruited as of manuscript submission), with data collection expected to be completed in January 2026 and analysis planned for March or April 2026. CONCLUSIONS: This study will quantify model-clinician agreement, measure anchoring effects, and generate qualitative insights on utility, trust, and adoption. The findings will inform the implementation of latent health measurement tools in clinical practice and provide a framework for the early-stage evaluation of artificial intelligence-based clinical decision support systems.
5 July 2026
Read appraisal →Lancet (London, England)
Diagnosis and treatment of postpartum haemorrhage: a race against time.
Postpartum haemorrhage (PPH) is common, affecting an estimated 13% of women having vaginal birth and 31% of women having caesarean birth. Successful management of PPH requires early and accurate diagnosis and effective treatment. A systematic review found that subjective visual estimation of blood loss misses 52% of PPH diagnoses at vaginal birth (pooled sensitivity 48%, 95% CI 44-53), and probably more at caesarean birth. The WHO-International Federation of Gynecology and Obstetrics-International Confederation of Midwives consolidated guidelines on PPH therefore recommend objective quantification of blood loss with products such as a calibrated blood collection drape. When supported by a robust implementation strategy and a first-response treatment bundle, objective measurement of blood loss and monitoring of vital signs has been shown to diagnose PPH accurately and early, and improve clinical outcomes. Refractory PPH can progress to life-threatening PPH, which should be managed by a multidisciplinary team providing aggressive resuscitation and targeted treatment. Saving the life of a woman with excessive postpartum bleeding is a race against time. The six delays to avoid are: (1) in the diagnosis (by use of objective cumulative blood loss measurement and early trigger criteria), (2) in the first-response treatment (by authorising midwives to administer all components of a standardised bundle of interventions), (3) in the escalation (by use of explicit escalation criteria and red flags), (4) in the use of temporising measures (eg, non-pneumatic anti-shock garment), (5) in the identification and targeted management of any specific causes of bleeding, and (6) in the provision of blood and blood products. Quick actions to avoid these delays can mean the difference between life and death for a woman with PPH.
4 July 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
Engineering biomarker representations of vital signs data enhances deep learning mortality prediction
OBJECTIVES: We evaluated bidirectional long short-term memory models for predicting inpatient mortality using different approaches to processing vital signs data collected during the initial 24 h of intensive care unit (ICU) admissions. MATERIALS AND METHODS: We compared 3 vital-sign representations: (1) raw data recorded every 5 min, (2) preprocessed data averaged hourly, and (3) preprocessed data using biomarker representations that extends a digital oximetry biomarker toolbox of PhysioZoo software, applied to blood pressure, heart rate, temperature, respiratory rate, and SpO2. RESULTS: Across 2 large ICU datasets, HiRID and eICU, models trained on the frequency-normalized representation achieved higher discrimination and lower Brier scores than those trained on raw 5-min and hourly averaged data. DISCUSSION: The use of biomarker representations of vital signs yielded the largest improvements in discrimination and overall probabilistic performance reflected by lower Brier scores for predicting inpatient mortality by deep learning. CONCLUSION: Thus, we recommend using a similar approach to vital signs preprocessing for time-series predictive models.
3 July 2026
Read appraisal →Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Utilization of Telemedicine Among Offshore Health Care Workers and Its Influencing Factors
INTRODUCTION: Telemedicine has become an essential tool for providing health care to offshore workers. Despite its advancements, there is still a lack of understanding about its optimal use and the factors affecting it among health care providers in offshore settings. The objective of this work was to study and determine the level of telemedicine utilization among offshore health care workers and its associated factors in Malaysia. METHODS: In this cross-sectional study, online questionnaires were distributed to health care workers involved in offshore medical services. The questionnaire includes sociodemographic information and the validated Malay version of the Telemedicine Acceptance Model (TAM) Questionnaire. A total of 73 offshore health care workers were recruited from a local company using universal sampling. Data analysis involved descriptive statistics as well as simple and multiple logistic regression. RESULTS: A total of 65.8% of offshore health care workers reported using telemedicine in their practice. Multiple logistic regression analysis indicated that Chinese and Indian workers were significantly more likely to use telemedicine compared with Malay workers, with adjusted odds ratios of 32.11 (p = 0.045) and 33.51 (p = 0.037), respectively. Workers with a good attitude toward telemedicine had 11.84 times higher odds of utilization (p = 0.004), while those with high behavioral intention were 25.80 times more likely to use telemedicine compared with their counterparts with low behavioral intention (p < 0.001). CONCLUSIONS: A majority of offshore health care workers utilize telemedicine, with Chinese and Indian ethnicity, good attitude, and high behavioral intention significantly predicting its utilization.
3 July 2026
Read appraisal →Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Application of Telemedicine for Mission Support-Importance of a Cost-Benefit Analysis
BACKGROUND: Digital health is more relevant now than ever before, and interventions have a clear potential to improve the quality of care while reducing health care costs. Telemedicine has emerged as a transformative approach to health care delivery, particularly accelerated by the COVID-19 pandemic. In mission environments, telemedicine increasingly supports the management of acute injuries, chronic conditions, predeployment screening, and follow-up assessments, often using low-bandwidth store-and-forward modalities. METHOD: By reviewing existing literature and considering several different (heterogeneous) programs for "telemedicine for mission support," the key performance indicators are explored to evaluate telemedicine in missions, following its implementation. Both acute and chronic care use cases, as well as operational, clinical, and technical determinants of feasibility, were considered. RESULTS: This article presents the clinical, operational, and economic benefits of "telemedicine in missions" and the metrics for a comprehensive cost-effectiveness analysis or cost-benefit analysis, considering its economic and clinical impacts. CONCLUSIONS: Telemedicine in missions shows considerable differences from other telemedicine applications depending on the actors and the resulting circumstances. Considering the heterogeneity of the metrics provided, even within the field of "telemedicine in missions," the analyses have to be conducted in accordance with the encountered conditions. Nevertheless, a set of metrics can be applied to nearly all use cases across the different applications and actors. A mission-adaptable minimum data set is proposed to support standardized evaluation across diverse operational contexts.
3 July 2026
Read appraisal →Child and adolescent psychiatric clinics of North America
Technology-Enabled Crisis Care for Youth: Bridging the Gap
The article discusses technological advancements in mental health care for youth in crisis, addressing workforce shortages and enhancing care delivery. Technologies like remote patient monitoring, telehealth, mobile health applications, virtual reality therapy, and artificial intelligence improve symptom tracking, communication, and treatment accessibility. Clinical decision support systems and therapy chatbots enhance crisis assessment and management. Various settings employ telepsychiatry and extend reach, while digital therapeutics and monitoring ensure ongoing support. Challenges include regulatory compliance, data privacy, and technological limitations. Future directions emphasize interoperability, safeguards for new and adapting technologies, patient-centered care, and integrated digital health systems.
3 July 2026
Read appraisal →Psychiatric services (Washington, D.C.)
Psychiatric Risk Associated With Large Language Model Chatbots: An Emerging Concern
Large language model chatbots such as ChatGPT have been adopted by millions of users, yet their psychiatric implications are poorly understood. Recent media reports describe 26 apparent instances of psychosis and two instances of depression and suicide in the context of heavy chatbot use, often marked by delusions seemingly reinforced by the model. Because chatbots simulate conversation but are statistical systems optimized for agreement, they may amplify distorted beliefs. Research is needed to determine whether these reports represent a genuine pattern of risk and to identify vulnerable populations. Clinicians should screen for chatbot use and discuss these systems with patients.
2 July 2026
Read appraisal →International journal of medical informatics
The role of artificial intelligence in virtual emergency care: a systematic review
BACKGROUND: The integration of artificial intelligence (AI) into virtual emergency care represents a potentially transformative approach to healthcare delivery, yet the evidence base remains poorly characterized. This systematic review comprehensively evaluates the current state of AI applications in virtual emergency care settings. METHODS: We systematically searched eight databases (Embase, PsycINFO, MEDLINE, PubMed, Scopus, Web of Science, CINAHL, Cochrane Library) from inception through March 2025. Of 7,098 records identified and 4,935 screened after deduplication using Covidence, 8 studies met inclusion criteria following exclusion of one study lacking AI components. Studies were assessed using PROBAST + AI for risk of bias and quality assessment, TRIPOD + AI for reporting quality, and GRADE for certainty of evidence. RESULTS: The eight included studies (total participants: approximately 0.5 million) evaluated diverse AI applications including decision trees, machine learning ensembles, and graph neural networks across multiple virtual emergency contexts. Performance varied widely (accuracy 77.5-100%, sensitivity 63-100%, specificity 60% in single study reporting). All clinical studies demonstrated serious risk of bias. TRIPOD + AI compliance averaged only 36.9% (range 30.9-48.1%). GRADE assessment revealed very low to low certainty evidence across all outcomes, with no studies measuring actual clinical outcomes. CONCLUSIONS: Current evidence is insufficient to support widespread clinical implementation of AI in virtual emergency care. While preliminary results suggest potential benefits in triage accuracy and resource efficiency, critical gaps exist in validation, clinical outcome assessment, and reporting standards. Future research must prioritize prospective controlled trials with real patient data, clinical outcome measurements, and adherence to reporting guidelines.
2 July 2026
Read appraisal →Diagnostic and interventional imaging
Artificial intelligence in emergency musculoskeletal imaging: A critical review of current applications.
Artificial intelligence (AI) is increasingly shaping emergency musculoskeletal imaging, where rapid and accurate diagnosis is often challenged by high imaging volumes and time pressure. These constraints increase the risk of missed injuries and underscore the need for tools that support faster and reliable assessments. AI systems show promise in improving workflow efficiency by prioritizing urgent studies, guiding modality selection, and reducing reporting delays. Deep learning models can enhance abnormality detection by identifying fractures, soft tissue injuries, and infectious processes, and can provide structured classifications that support clinical decision making. Pediatric-focused AI systems address age-specific developmental considerations and offer valuable support for clinicians with varying levels of pediatric musculoskeletal expertise. Large language models further expand the role of AI by improving report clarity, generating structured impressions, and facilitating communication with clinicians, patients, and families. Despite these advances, challenges remain, including limited external validation, dataset bias, and medicolegal considerations. This review summarizes current AI applications across these domains and highlights key strengths, limitations, and future directions for safe and effective integration into emergency musculoskeletal imaging.
2 July 2026
Read appraisal →IEEE transactions on bio-medical engineering
EMI Cancellation for Shielding-Free Ultra-Low-Field MRI
OBJECTIVE: Ultra-Low-Field Magnetic Resonance Imaging (ULF MRI) offers low cost and portability but suffers from electromagnetic interference (EMI) in unshielded environments. This study developed a deep learning-based active EMI suppression method to overcome these limitations. METHODS: Using a 68mT ULF MRI system, human body coupling was identified as a primary EMI pathway. We proposed EMIC-Net, a U-Net architecture incorporating Transformer and hybrid attention mechanisms, to learn the data-driven nonlinear mapping from sensing coil signals to radio-frequency (RF) receiver coil interference. Acquired data underwent phase and gain compensation prior to model training. The model's efficacy was validated through in vivo human brain imaging, comparing its performance with EDITER and standard CNN methods, and by assessing the impact of varying EMI coil numbers and training data volumes. RESULTS: EMIC-Net effectively suppressed complex dynamic EMI. It restored image SNR from 2.35 dB to 17.63 dB, with significant PSNR and SSIM improvements. Image quality neared shielded acquisitions and surpassed comparative methods. Three EMI coils provided optimal balance, and the model showed data efficiency, requiring a small dataset for effective training. CONCLUSION: The EMIC-Net method accurately predicts and efficiently removes EMI in unshielded ULF MRI, offering superior performance and practicality. SIGNIFICANCE: This research promotes portable, low-cost ULF MRI for primary healthcare and bedside diagnosis. It also offers insights for mitigating complex EMI issues in other RF sensing domains.
2 July 2026
Read appraisal →IEEE transactions on pattern analysis and machine intelligence
50 Years of Automated Face Recognition
Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, and in many cases exceed, human performance. This paper traces the historical and technological evolution of FR, encompassing early algorithmic paradigms through to contemporary neural systems trained on extensive real and synthetically generated datasets. We examine pivotal innovations that have driven this progression, including advances in dataset construction, loss function formulation, network architecture design, and feature fusion strategies. Furthermore, we analyze the relationship between data scale, diversity, and model generalization, highlighting how dataset expansion correlates with benchmark performance gains. Recent systems have achieved near-perfect large-scale identification accuracy, with the leading algorithm in the latest NIST FRTE 1:N benchmark reporting a False Negative Identification Rate (FNIR) of 0.15 percent at False Positive Identification Rate (FPIR) of 0.001 on a gallery of over 10 million identities. Larger galleries increase false positive rates and deployments at greater scales will see higher error rates. We delineate key open problems and emerging directions, including scalable training, multi-modal fusion, synthetic data, and interpretable recognition frameworks.
1 July 2026
Read appraisal →Emergency medicine journal : EMJ
Interventions to reduce imaging in children with upper or lower extremity injuries: a systematic review and meta-analysis
BACKGROUND: Radiation exposure, transition delays and costs associated with unnecessary imaging in children have stimulated research into clinical decision rules and other interventions to reduce imaging in the emergency department (ED). The objective of this systematic review is to examine the effectiveness of implementing interventions to reduce imaging in children with upper/lower extremity injuries in the ED. METHODS: Seven databases and the grey literature were searched up to May 2024. Comparative studies assessing interventions to reduce imaging in children with upper/lower extremity injuries implemented in the ED were eligible. Two independent reviewers screened for study eligibility, quality assessment and data extraction, with disagreements settled via third-party adjudication. Changes in imaging are reported as ORs with 95% CIs, using a random effects model. RESULTS: From 9387 citations, eight unique studies enrolling 7793 children were included with the majority using a before-after design. Potential concerns for bias were documented due to a lack of reporting of key quality domains. Decision rules for ankle injuries successfully reduced radiography (OR=0.11; 95% CI 0.07 to 0.16, I2=38%). A decision rule for wrist injuries reduced imaging (OR=0.06; 95% CI 0.03 to 0.11); however, eight injuries were missed. Two studies implementing clinical guidelines reported decreases in radiographs per patient (p<0.001). One trial reported increased imaging in children assessed by triage nurses using an established clinical decision rule (OR=5.44; 95% CI 2.96 to 10.02), with 16 missed injuries identified. CONCLUSIONS: Guidelines incorporating clinical decision rules, particularly decision rules for ankle injuries, can reduce radiography for children with extremity injuries in the ED. Further investigations are warranted to identify other extremity injuries, the components of the intervention and the most efficient clinicians to target. PROSPERO REGISTRATION NUMBER: CRD42016042875.
1 July 2026
Read appraisal →The Cochrane database of systematic reviews
Prognostic models for predicting intensive care unit admission or mortality in critically ill adults not yet been admitted to the intensive care unit
OBJECTIVES: This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: To determine whether prognostic models can be used for predicting the occurrence of ICU admission and mortality within the index hospital admission, and post-discharge survival at reported follow-up time points, up to one year, in critically ill adults not yet admitted to the ICU. The objective in PICOTS format is as follows. POPULATION: critically ill adults not yet admitted to the ICU Index prognostic model: available prognostic models with or without external validation Comparator: not applicable Outcomes: ICU admission, ICU and hospital mortality, and post-discharge survival Timing: for ICU admission or transfer, ICU mortality, and hospital mortality outcomes, the timing will be in hospital. For post-discharge survival, the timing will be up to 28 days, 1 to 3 months, 6 months, and 1 year after hospital discharge. SETTING: acute hospital wards and emergency care departments.
30 June 2026
Read appraisal →Medicine
Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study
This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (ICU) admission among patients with comorbid Chronic Obstructive Pulmonary Disease (COPD) and depression. Data of patients diagnosed with both COPD and depression were extracted from the MIMIC-IV version 3.1 database. A total of 1121 patients with first-time hospitalization and comorbid COPD and depression were included. Feature selection was performed using the Boruta algorithm. Five ML algorithms were employed to construct prediction models. Model performance was evaluated using the receiver operating characteristic curve, area under the curve, calibration curves, and decision curve analysis. Shapley additive explanations (SHAP) plots were used to interpret the contribution of each feature to the model's predictions. Sixteen variables identified by the Boruta algorithm were used to build the predictive models, including chloride, prothrombin time, phosphorus, bicarbonate, international normalized ratio, mean corpuscular hemoglobin (MCH) concentration (MCHC), mean corpuscular hemoglobin, hemoglobin, hematocrit (HCT), total calcium, paraplegia, cerebrovascular disease, smoker status, invasivevent, sepsis, and acute kidney injury (AKI). The Random Forest model performed the best: specificity (0.948), positive predictive value (0.924), precision (0.924), F1 score (0.887), balanced accuracy (0.901), sensitivity (0.853), negative predictive value (0.898), and recall (0.853). SHAP analysis indicated that AKI (0.212), sepsis (0.092), and invasivevent (0.073) were the most influential predictors, followed by anemia-related features such as hemoglobin (0.023), HCT (0.023), and MCHC (0.022). ML analysis revealed that AKI, sepsis, invasivevent, and anemia-related indicators are key risk factors for ICU admission among COPD patients with depression. However, it is important to note that the findings lack external validation, which warrants further investigation in diverse patient populations.
29 June 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
Telemedicine Consultation: Lessons Learned from the Pararescue Experience
BACKGROUND: Telemedicine is a critical military medicine capability in austere, remote, and denied environments where prolonged casualty care and knowledge gaps exist. Despite significant military investment in telemedicine, the literature lacks detailed lessons from real-world cases. METHODS: U.S. Air Force Pararescue missions between 1 January 2010 and 31 December 2020 that included telemedicine consultation were reviewed. Participating flight surgeons (FS) and Pararescuemen (PJ) were interviewed, and available after-action reports were analyzed. Mission parameters, outcomes, and technical or human factors affecting communication were evaluated. RESULTS: Across 13 telemedicine consultations, the 28 patients experienced: cave entrapment (13), trauma (7), burns (4), and illness (4). Consultations were from PJ to FS (11), U.S. Coast Guard to FS (1), and FS to neurosurgery (1). Secondary consultations (7) were from FS to dental (1), dermatology, burn/critical care, orthopedics (2), pediatric anesthesiology, and thoracic surgery specialists. Missions used voice over mobile or satellite phone, text, and email. Recommendations commonly involved medication selection (6), advisement against mission launch (3), and burn care (2). Consultation effectiveness was impacted by unclear directions (3) and degraded communications (2). CONCLUSION: Telemedicine is valuable to PJs during complicated cases and rarely performed procedures, but cannot replace pre-deployment medical exposure and training. Rehearsals may optimize telemedicine, and training should focus on synchronizing a shared mental model of the patient using well-understood patient report and evaluation tools (ATMIST and MARCH PAWS). Telemedicine may reduce human error, give medics the confidence to take action, and limit the psychological impact of difficult care decisions.
29 June 2026
Read appraisal →Medicine
Artificial intelligence-guided clavicle fracture detection on plain radiographs: A retrospective diagnostic accuracy study.
Extensive research exists on artificial intelligence (AI)-assisted fracture detection in various anatomical regions, but clavicle fractures remain comparatively understudied. This study aimed to compare the diagnostic performance of AI-guided clavicle fracture detection on plain radiographs with that of an experienced orthopedic surgeon. We retrospectively analyzed all clavicle radiographs obtained at our institution between September 11,2023 and October 31,2024 following the clinical implementation of AI-guided fracture detection. Radiological findings confirmed by a senior radiologist served as the reference standard. AI performance was additionally compared with the assessments of an experienced, blinded orthopedic surgeon. Diagnostic performance metrics included accuracy, sensitivity, specificity, Cohen kappa, F1 score, and Youden index. A total of 367 clavicle radiographs were included, comprising 186 anteroposterior (AP) and 181 second-plane views. The mean patient age was 44.6 ± 26.2 years, and 59.4% were male. According to the reference standard, clavicle fractures were present in 96 AP radiographs (51.6%) and 95 second-plane radiographs (52.5%). On AP radiographs, the AI system achieved an accuracy of 97.31%, sensitivity of 95.83%, specificity of 98.89%, Cohen kappa of 0.95, and F1 score of 0.97. Diagnostic performance on second-plane radiographs was similarly high and overall comparable to that of the experienced orthopedic surgeon. This retrospective diagnostic accuracy study shows that AI-guided clavicle fracture detection is highly accurate and reliable, with performance approaching that of an experienced orthopedic surgeon. These findings support the potential role of AI as a diagnostic support tool in clavicle fracture detection, while prospective and multicenter validation remains warranted.
28 June 2026
Read appraisal →Cardiovascular research
Proteins targeting ischaemic-reperfusion injury and repair after myocardial infarction: a systematic literature review.
Following myocardial infarction (MI), reperfusion strategies ensure that blood flow is promptly restored to salvage the ischaemic myocardium. However, the sudden restoration of blood flow can inflict further damage to the ischaemic tissue, known as myocardial ischaemia/reperfusion injury (MIRI). Therapeutic strategies aimed at reducing MIRI and promoting cardiac repair remain an important clinical need. Protein-based therapies might exert benefits that limit damage and promote cardiac repair post-MI. To explore this further, we performed a systematic review of data from preclinical studies and clinical trial registries evaluating the cardioprotective effects of proteins post-MI. Medline and EMBASE were searched for preclinical studies that examined the potential cardioprotective effects of protein-based therapies to limit damage and/or mediate cardiac repair when administered after MI and/or reperfusion. Studies registered with ClinicalTrials.gov and EudraCT were also reviewed. In total, 84 studies were included in the final analysis, which included 46 different proteins. Overall, our findings support the concept that transiently applying recombinant or modified proteins after acute MI has the potential to promote lasting improvements in heart function via multiple pleiotropic mechanisms. Many of these proteins converge on a limited set of well-established signalling pathways involved in cardiac repair and remodelling after MI, with a few proteins demonstrating consistent and robust effects across multiple outcomes and models (small and large animals; reperfused and non-reperfused models). Despite this, the high potential cardioprotective benefit reported in preclinical studies has not translated into approved therapies for use in patients. Although the previous failure of protein therapies in clinical development does not invalidate the potential relevance of their downstream pathways, our research underlines the challenges in translating preclinical findings of protein-based therapies into the clinical setting. Future preclinical and clinical research should examine the optimal treatment effect and fully characterize the therapeutic potential of proteins involved in post-MI remodelling.
28 June 2026
Read appraisal →Brain impairment : a multidisciplinary journal of the Australian Society for the Study of Brain Impairment
Validating telehealth assessment of physical concussion symptoms
BACKGROUND: Telehealth could expand access to specialised concussion care; however, physiotherapists report low confidence in assessing physical symptoms remotely. This study adapted measures of vestibular, oculomotor, cervical and autonomic function for telehealth delivery, and evaluated their feasibility and comparability with in-person assessment. METHODS: Using a repeated-measures design, 16 participants with concussion and 20 controls completed assessment modalities in random order (3 × 4 block randomisation). Feasibility was evaluated through completion rates, technical issues, adverse events and clinician feedback. Concurrent validity was examined using percentage agreement, Cohen's kappa, and sensitivity and specificity analyses. RESULTS: Telehealth assessment was feasible; most concussion participants and all controls completed the protocols, there were minimal technical issues, no adverse events and shorter administration times. Clinicians reported high usability and efficiency. High agreement and sensitivity were observed across vestibular, ocular convergence, autonomic and cervical range of motion measures. Nuanced assessment of ocular smooth pursuits showed lower agreement, and a potential ceiling effect on autonomic assessment was identified. CONCLUSION: This study preliminarily supports the feasibility and comparability of telehealth compared with in-person physical concussion assessments. Assessment of smooth pursuits and autonomic function require protocol refinement to enhance accuracy. Replication in larger samples is required to establish comparability.
27 June 2026
Read appraisal →PloS one
Artificial Intelligence in emergency department triage: A scoping review
BACKGROUND: Triage in emergency departments (ED) is a critical process for prioritizing care and ensuring clinical safety. However, current triage systems often exhibit vulnerabilities that compromise the efficiency and quality of healthcare delivery. Artificial Intelligence (AI) has emerged as a promising innovation to support decision-making and optimize patient flow in these high-pressure environments. OBJECTIVE: To map the available evidence regarding the implementation and performance of artificial intelligence in emergency department triage. METHOD: This scoping review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidelines. A comprehensive search was conducted across 13 databases (CINAHL, Cochrane Library, PubMed Central, SciELO, Web of Science, SCOPUS, Science Direct, VHL, Embase, and several regional dissertation repositories), with no language or time restrictions. Two independent reviewers performed the selection process using the Rayyan platform, with discrepancies resolved by a third evaluator. Data were synthesized using the PAGER framework, categorizing findings into Patterns, Advances, Gaps, Evidence for practice, and Recommendations for research. RESULTS: Nineteen studies met the inclusion criteria. AI was primarily implemented through Machine Learning (ML) algorithms, including Deep Learning architectures. Natural Language Processing (NLP) was frequently employed to process unstructured clinical data, with recent studies exploring the potential of Large Language Models (LLMs). Overall, ML-based models consistently outperformed traditional triage systems in predictive accuracy. These techniques were mainly utilized for automated classification, predicting clinical severity, and enhancing patient prioritization by integrating both objective and subjective assessment data. CONCLUSIONS: The findings indicate that AI has significant potential to enhance emergency triage by streamlining service flows and providing robust clinical decision support. However, the current evidence remains heterogeneous and largely exploratory. Key challenges include variability in model performance, a lack of external validation, and studies often limited to specific populations. Consequently, many current tools still lack the necessary reliability for safe, large-scale clinical implementation.
27 June 2026
Read appraisal →Prehospital and disaster medicine
Non-Resuscitative Telecommunicator-Assisted First Aid: A Scoping Review
INTRODUCTION: The provision of instant telephone pre-arrival instructions (PAI) on first aid to laypeople by emergency services' telecommunicators for conditions beyond cardiac arrest, such as trauma or acute coronary syndrome, is practiced by emergency services around the world and is recognized by some prehospital systems as a standard of care. However, to the best of the authors' knowledge, no attempts have been made to date to systematically summarize the related research evidence. STUDY OBJECTIVE: The aim of this study was to systematically map and analyze published quantitative research on telecommunicator-assisted first aid (TAFA) for medical emergencies other than cardiac arrest. METHODS: Peer-reviewed journal papers reporting original quantitative data on experimental or real-world applications of TAFA were identified through PubMed, Scopus, and Google Scholar. Studies focusing solely on cardiac arrest management (cardiopulmonary resuscitation [CPR] or automated external defibrillation) were excluded. A scoping review of eligible papers was conducted in accordance with the PRISMA-ScR guidance. RESULTS: Twenty-two studies published from 2000 through 2024 met inclusion criteria. First authors represented nine high-income countries. The studies investigated hypothetical or actual application of TAFA for anaphylaxis, chest pain or myocardial infarction, stroke, trauma, bleeding, childbirth, foreign body airway obstruction, opioid overdose, and respiratory arrest; four studies did not specify the type of emergency. Eight studies (36.4%) analyzed real Emergency Medical Services (EMS) call data, another eight (36.4%) were simulation-based, five (22.7%) used surveys, and one (4.5%) represented a cost-effectiveness analysis. The studies described existing TAFA practices, evaluated expectations and perceptions of the service by the public, demonstrated the feasibility of experimental techniques, including video communication, and identified areas for future investigations and interventions. Along with some beneficial effects of TAFA, several studies have demonstrated downsides of the practice, including suboptimal dispatcher adherence to pre-arrival protocols. The relationships between the real-world application of TAFA and patient outcomes are unclear. CONCLUSION: According to the review of quantitative research, non-resuscitative TAFA remains an under-explored area of prehospital medicine. Considering the paucity and the inconclusiveness of available evidence, significantly more research is required to better understand its effects and practical implications. The evidence map generated by this scoping review may assist the professional community in advancing the development of non-resuscitative TAFA.
26 June 2026
Read appraisal →Acta dermato-venereologica
Recurrent Cutaneous Eosinophilic Vasculitis: A Systematic Review of Clinicopathologic Features and Treatment Outcomes
Recurrent cutaneous eosinophilic vasculitis is a rare, predominantly cutaneous vasculitis described mainly in isolated reports. We aimed to systematically synthesize published evidence on its clinico-pathological features, treatment and outcomes. We reviewed case reports and case series of recurrent cutaneous eosinophilic vasculitis and related entities identified in PubMed, Scopus and reference lists (last searched 16 February 2026), using structured report and case-level extraction, duplicate handling, Joanna Briggs Institute critical appraisal and descriptive synthesis. Twenty reports comprising 24 unique cases were included. Most patients presented with relapsing pruritic purpura of the lower extremities. Peripheral eosinophilia was frequent, antineutrophil cytoplasmic antibodies were negative in all reported cases and histopathology consistently showed eosinophil-rich vasculitis, usually with fibrinoid and/or necrotizing change. Systemic corticosteroids often induced improvement, but relapse during tapering or after withdrawal was common. The evidence was limited by small, heterogeneous reports and incomplete follow-up. Overall, the findings support a recognizable cutaneous-predominant, relapse-prone pattern, highlight the importance of assessing extracutaneous involvement in differential diagnosis and support provisional refinement of the previously proposed diagnostic framework.
26 June 2026
Read appraisal →Scientific reports
Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations
Clinical decision-making increasingly relies on deep neural networks (DNNs), yet their deployment in practice requires transparent and interpretable predictions. Explainable artificial intelligence (xAI) methods can identify input regions relevant to a model's decision, but their clinical interpretability remains limited. In this study, we investigated whether inductive transfer learning (TL) can reinforce domain-specific feature separation in xECGArch, a two-branch convolutional neural network for atrial fibrillation (AF) detection from electrocardiograms (ECGs). Each branch was pre-trained on a task aligned with its designated feature domain, P wave detection for morphology and RR interval variability prediction for rhythm, then fine-tuned on binary AF classification using an iterative layer freezing schedule. Deep Taylor decomposition (DTD) was applied to analyze explanations across all configurations. Fine-tuning accuracy ranged from 85.70% to 95.23%, remaining comparable to the original xECGArch architecture and previous TL-based approaches. However, DTD analysis demonstrated that morphology pre-training directed relevance toward P waves, whereas rhythm pre-training concentrated explanations on R peaks, with domain specificity increasing as more layers were frozen. These findings suggest that inductive TL can encourage domain-specific feature attribution in DNNs, improving the alignment of post-hoc explanations with clinically meaningful ECG regions.
26 June 2026
Read appraisal →Journal of medical Internet research
Policy Considerations for National Virtual Hospitals: Global Evidence and the Seha Virtual Hospital Model
Health systems worldwide face growing pressure from population aging, multimorbidity, and rising emergency admissions, prompting reconsideration of traditional inpatient care models. In response, digitally enabled models such as tele-intensive care unit (tele-ICU) programs, hospital-at-home services, virtual wards, and other remote specialist pathways have expanded, particularly after the COVID-19 pandemic accelerated telemedicine adoption and cross-site virtual staffing. However, nationally coordinated, multispecialty virtual hospitals remain uncommon worldwide, and robust evidence on their system-level effects is still limited. As a result, policy discussions about national virtual hospitals must often draw on evidence from related virtual-care models rather than from mature national implementations. This viewpoint synthesizes representative international evidence from tele-ICU systems, hospital-at-home programs, virtual wards, telestroke networks, and other condition-specific virtual-care pathways, and examines Saudi Arabia's Seha Virtual Hospital (SVH) as a national case study to identify policy lessons relevant to the design, governance, and evaluation of national virtual hospitals. Across settings, these models suggest that remote and digitally supported care can achieve outcomes comparable to in-person hospital care when patient selection is appropriate, escalation and transfer pathways are explicit, monitoring intensity matches clinical risk, and multidisciplinary teams are integrated into local workflows. Tele-ICU programs have reported reductions in intensive care mortality and length of stay under well-structured organizational models, while hospital-at-home and virtual-ward programs have shown comparable safety, reduced hospital usage, and improved patient experience among selected patient groups. Telestroke networks likewise demonstrate outcomes comparable to specialist in-person care in acute stroke pathways. Nevertheless, the evidence base remains heterogeneous and strongly context-dependent. Much of the literature is short-term, with limited consistent evidence on long-term outcomes, caregiver burden, cost-effectiveness, workforce implications, and digital equity. SVH illustrates the emerging implementation of a centralized national virtual hospital model. Launched in 2022 under Saudi Arabia's Vision 2030 Health Sector Transformation Program, SVH operates as a national telehealth hub embedded within the country's broader digital-health ecosystem and links hospitals across the Kingdom to specialized clinical expertise. Its service portfolio includes urgent and critical care consultations, specialized virtual clinics, multidisciplinary case discussions, and supportive diagnostic services. Early reports indicate rapid operational expansion, broad institutional participation, and national-scale feasibility. However, independent comparative evidence evaluating SVH's effects on mortality, readmissions, length of stay, cost-effectiveness, equity, and workforce sustainability remains limited. National virtual hospitals should therefore be understood as evidence-generating health-system innovations rather than fully validated care models. Sustainable scale-up requires embedding rigorous prospective evaluation within implementation, aligning financing mechanisms with substitution of inpatient care, establishing clear governance and regulatory frameworks, and addressing digital inclusion and workforce sustainability. These considerations can help guide policymakers and health-system leaders in the accountable, equitable, and evidence-informed development of national virtual hospital programs.
25 June 2026
Read appraisal →NEJM evidence
A Randomized Trial of Interval Likelihood Ratios for Clinical Decision-Making
BACKGROUND: Dichotomized reporting of diagnostic test results may oversimplify test characteristics. Interval likelihood ratios (ILRs), test characteristics across a range of values, offer a nuanced alternative, but their impact on decision-making is uncertain. We assessed whether ILRs improve clinical decisions compared to two traditional formats: single-threshold and binary test characteristics. METHODS: We conducted a multicenter randomized trial of internal medicine residents, attending physicians, and advanced practice providers. Participants were presented with two fictional vignettes (cases involving pulmonary embolism diagnosis and heart failure treatment), each with a clearly correct response centered on a key diagnostic test result. The vignettes were identical except for participants' random assignment to one of three test characteristic presentations: (1) single threshold, (2) binary test characteristics, or (3) ILRs. We compared the correct management decision rates between the ILR and threshold groups and between the ILR and binary groups. Secondary objectives included comparing threshold and binary groups and subgroup analyses by case vignette. RESULTS: The trial enrolled 611 participants. The correct decision rate was 93.4% in the ILR group compared with 82.2% in the threshold group (difference of 11.2 percentage points; 95% confidence interval [CI], 6.9-15.6, P<0.001) and compared with 75.1% in the binary group (difference of 18.3 percentage points; 95% CI, 13.5-23.1, P<0.001). In the pulmonary embolism case, the correct response rate was 91.9% in the ILR group, 70.9% in the threshold group, and 64.7% in the binary group. In the heart failure case, the correct response rate was 94.9% in the ILR group, 93.4% in the threshold group, and 85.6% in the binary group. CONCLUSIONS: Presenting diagnostic test information using ILRs improved vignette-based clinical decisions. The magnitude of improvement appeared to vary by case vignette.
24 June 2026
Read appraisal →European journal of emergency medicine : official journal of the European Society for Emergency Medicine
Effect of a liquid crystal vein locator on intravenous catheterization in young children: a randomized trial
BACKGROUND AND IMPORTANCE: Peripheral intravenous catheterization (PIVC) in young children is notoriously challenging because of factors like poor patient cooperation and small vessel diameter. Repeated attempts cause distress and delay critical emergency treatment. OBJECTIVES: This study aimed to evaluate the effect of a novel liquid crystal display-based near-infrared vein locator on PIVC performance in children aged under 3 years. DESIGN: This is a prospective, randomized controlled trial. SETTINGS AND PARTICIPANTS: A total of 105 children aged 0-3 years requiring PIVC were enrolled in a pediatric emergency department. INTERVENTION: Participants were randomly assigned to undergo PIVC using either a liquid crystal vein locator (study group, n = 50) or the traditional landmark-based method (control group, n = 55). OUTCOME MEASURES AND ANALYSIS: The primary endpoint was the first-attempt success rate. Secondary endpoints included the number of puncture attempts, vein localization time, and total insertion time. Multivariable logistic and Poisson regression models were used to adjust for covariates, such as vein difficulty and patient demographics. Analyses included effect sizes with 95% confidence intervals (CIs). MAIN RESULTS: The baseline characteristics of both groups were comparable. The first-attempt success rate was significantly higher in the study group compared to the control group (86.0% vs. 54.5%; absolute difference: 31.5%; 95% CI: 15.2-47.8%). The study group also demonstrated a significantly shorter median vein localization time (16.5 vs. 60.0 s) and median total insertion time (40.0 vs. 185.0 s). In addition, the device significantly reduced the median number of puncture attempts (adjusted rate ratio: 0.77; 95% CI: 0.66-0.90). Multivariable analysis revealed that device assistance was an independent predictor of first-attempt success (adjusted odds ratio: 3.35; 95% CI: 1.13-9.95). CONCLUSION: In this study, the use of a liquid crystal vein locator was associated with a significantly higher first-attempt success rate, fewer puncture attempts, and reduced procedural times for PIVC in young children.
24 June 2026
Read appraisal →European journal of emergency medicine : official journal of the European Society for Emergency Medicine
Association between patient-to-nurse ratios in emergency departments and patient, staff, and organizational outcomes: a systematic review.
Background In many settings, studies have shown that establishing patient-to-nurse ratios has benefits for both patients and staff. Whether this is transposable to emergency department (ED) is unclear. Objective The primary objective was to assess the association between patient-to-nurse ratios in EDs and mortality. Design In this systematic review, a search strategy identified studies published in 2004-2025 in MEDLINE, Cochrane, and CINAHL. Two independent reviewers performed selection and data extraction, followed by a narrative synthesis. Outcomes The primary outcome was in-hospital mortality. Secondary outcomes were other patient outcomes, staff, and organizational outcomes. Results Of 2214 identified references, 17 studies were included and all of which were observational. The primary outcome was assessed in only one cohort study ( n = 73 EDs), where an increase in the number of nurses per patient was associated with a reduction in ED mortality (adjusted odds ratio = 0.68; 95% confidence interval: 0.49; 0.94). An increase in the number of nurses per patient was also associated with a reduction in cardiac arrests (one study: relative risk = 1.33, 95% confidence interval: 1.05, 1.67 for ≥8.5 patients/nurse vs. <8.5) and medication errors (one of two studies), and an improvement in patient experience (two studies) and nurse satisfaction (one study). Mixed results were observed with regard to reducing length of stay (one of two studies), administration time (three of four studies) and the number of patients who left without being seen (three of four studies), while waiting time before diagnosis or treatment improved (three studies). Conclusion Although evidence is limited, several observational studies suggest that a higher nurse-to-patient ratio is associated with better patient, nurse, and ED organizational outcomes.
23 June 2026
Read appraisal →PloS one
Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review
Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threatening, DVT requires timely diagnosis to prevent complications such as pulmonary embolism and post-thrombotic syndrome. The growing demand for image interpretation has generated interest in applying artificial intelligence (AI) to automated DVT detection. This scoping review analyzes the performance of artificial intelligence in diagnosing DVT using computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). We conducted a search across seven databases from inception to May 2025 using terms related to deep vein thrombosis, artificial intelligence, and machine learning. Eligible studies were limited to those evaluating DVT diagnosis using CT, MRI, or ultrasound. Two independent reviewers selected eligible studies, and quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Eleven studies published between 2021 and 2025 met the inclusion criteria. Some of the AI algorithms included RetinaNet, Deep R-Belief Neural Networks, and Sooty Tern Optimization. US-based models were the most studied algorithms, with sensitivities and specificities ranging from 68 to 100% and 70-100%, respectively. The MRI-based model achieved sensitivities, specificities, and accuracies of 95% to 97%. One CT-based model demonstrated a sensitivity of 83%. Studies evaluated across multiple imaging datasets showed high sensitivities, specificities, and precision of 96% or higher. Future research should prioritize multicenter validation and integration of clinical factors. In addition, explainable frameworks capable of integrating multiple imaging datasets must be developed with attention to workflow efficiency and cost-effectiveness to support clinical translation. The results indicate that AI is best situated as a supplementary tool rather than a replacement for expert interpretation in DVT diagnosis.
23 June 2026
Read appraisal →Journal of chemical information and modeling
BioTD: An Online Database of Biotoxins
Biotoxins, mainly produced by venomous animals, plants, and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources related to biotoxins are insufficient, particularly because of a lack of accurate and extensive activity data. To fulfill this demand, we developed the Biotoxins Database (BioTD). BioTD is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species. The activity data in BioTD are categorized into five groups: Activity, Safety, Kinetics, Hemolysis, and other physiological indicators. Moreover, BioTD provides data on 1,532 mutants, refines the whole sequence and signal peptide sequences of toxins, and annotates disulfide-bond information. All of the data in the database can be downloaded for free. Given the importance of biotoxins and their associated data, this new database is expected to attract broad interest from diverse research fields in drug discovery. BioTD is freely accessible at http://biotoxin.net/.
23 June 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Association between timing of surgery and postoperative outcomes in older adults with distal femur fractures: a systematic review and meta-analysis
INTRODUCTION: The association between surgical timing and clinical outcomes in older adults with distal femur fractures remains unclear. We conducted a systematic review and meta-analysis to evaluate whether earlier surgery is associated with improved outcomes in this population. MATERIALS AND METHODS: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two independent reviewers searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from inception to October 8, 2024. The primary outcome was 30-day mortality, and secondary outcomes included longer-term mortality and postoperative complications. Risk of bias was assessed using the Quality in Prognosis Studies tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Of 6,101 records screened, 16 retrospective cohort studies comprising 31,213 patients met the inclusion criteria. Early surgery was not associated with reduced 30-day mortality (adjusted odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.56-1.05; low-certainty evidence) or with longer-term mortality at 90 days (crude OR: 0.91, 95% CI: 0.51-1.60), 180 days (crude OR: 0.46, 95% CI: 0.12-1.77), or 1 year (crude OR: 0.61, 95% CI: 0.34-1.09). Similar findings were observed for postoperative complications: cardiac complications (OR: 0.71, 95% CI: 0.48-1.07), pulmonary complications (OR: 1.07, 95% CI: 0.77-1.47), and pulmonary embolism (OR: 1.14, 95% CI: 0.62-2.16). Heterogeneity across outcomes was low to moderate, and definitions of early surgery varied among studies. Overall, this meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Sensitivity analyses consistently suggested lower mortality with earlier surgery, likely reflecting stricter definitions of early surgery and improved control of time-related bias. CONCLUSIONS: This meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Given the heterogeneity in definitions and study designs, high-quality prospective studies with standardized timing are warranted.
20 June 2026
Read appraisal →JMIR research protocols
Design, Development, and Validation of a Chatbot to Support Health Care Professionals Experiencing Workplace Aggression: Protocol for a Mixed Methods Study
BACKGROUND: Workplace violence against health care professionals has increased worldwide, leading to negative psychological, professional, and organizational outcomes. Despite existing prevention and reporting programs, underreporting and lack of accessible, confidential support persist. Digital health tools, including chatbots, may offer scalable support, guidance, and follow-up for affected professionals. OBJECTIVE: This study aims to design, develop, and validate a chatbot (Sanidad Segura) to assist health care professionals who experience workplace aggression and evaluate its usability, readability, and exploratory indicators of perceived usefulness and support in a pilot study. METHODS: This study will follow a mixed methods design conducted in two main phases: (1) design, development, and content validation of the chatbot based on literature review, institutional protocols, and expert consensus; and (2) pilot-testing, including usability and readability assessment using standardized instruments, as well as feasibility and acceptability evaluation among health care professionals working in emergency and critical care settings in Almería (Spain). The study is aligned with the Medical Research Council framework for complex interventions, incorporating development and feasibility stages. Quantitative data will be collected using the System Usability Scale and Inflesz readability scale. Qualitative data will be collected through semistructured interviews and analyzed using thematic analysis to explore user experience and identify barriers to and facilitators of use. RESULTS: The study has been funded for a 2-year period starting on December 18, 2024. Quantitative outcomes will include usability scores (System Usability Scale), readability scores (Inflesz), and participants' sociodemographic characteristics. Qualitative findings will identify themes related to usability, user experience, and suggestions for improvement. Integration of quantitative and qualitative findings will be conducted through triangulation to provide a comprehensive understanding of the usability, acceptability, and readability of the chatbot. CONCLUSIONS: This study addresses the increasing incidence of workplace violence against health care professionals through the development of a new chatbot (Sanidad Segura). This intervention seeks to facilitate the identification, support, and follow-up of affected individuals while minimizing the adverse effects of such events on their physical and psychological well-being, social interaction, and professional performance. Sanidad Segura will enable confidential case reporting and provide access to tailored medical, psychological, and legal resources, as well as information about institutional support services. This project represents a crucial step toward implementing an integrated digital framework for the detection, management, and prevention of workplace violence in health care settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92511.
19 June 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
Pattern of blast injuries. A systematic review: Part 2 - Landmines, unexploded ordnance and terrorism.
PURPOSE: This systematic review analyzes the patterns and distribution of blast injuries from landmine explosions, UXOs and terror attacks including suicide bombing, in civilians and military personnel. METHODS: A search was conducted in two databases on 26. March 2025 for observational studies reporting blast injuries in civilian and military populations of all ages. Studies were clustered by explosion typ. A narrative data synthesis was performed. The JBI tool for case series was used. PROSPERO: CRD42023391736. RESULTS: 126 studies were included in the overall systematic review. 45 studies on terror & suicide bombings and 12 studies on landmines & UXOs are analyzed in the present paper. Studies on landmines from 1980 to 2006 included 13,270 individuals (age: 24.0 years (Range 8.2 years - 32.2 years); mainly male (88.9%)) in post-conflict low- and middle-income countries. Lower extremity injuries including amputations were most frequent, followed by eye injuries. Studies on terrorist attacks with a total of 12,518 mainly civilian victims (average age: 33,1 years; 61.6% males) reported on incidents in post-conflict regions and high-income cities mainly from the early 2000s. The injury pattern was multidimensional, with extremity injuries most frequent (28%-30%), including fractures, lacerations, and traumatic amputations (18%-20%). CONCLUSION: This Systematic Review Part 2 on landmines and terror attacks found that extremity injuries, including traumatic amputations, are the most frequent and dominate injury. However, the injury pattern varies depending on the explosion mechanism. Landmines primarily cause lower extremity injuries, as well as eye injuries. Terror and suicide explosions exhibit multidimensional injury pattern depending on the distance and location of the explosion.
19 June 2026
Read appraisal →Journal of affective disorders
Prediction models for suicide reattempts by lasso regression through machine learning models: Single versus multiple suicide attempters.
The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also associated. Some studies found other indicators of great risk for suicide reattempts. Machine Learning algorithms offer the potential for systematic detection of features that carry greater risk for an event. This study sought to develop a classification algorithm distinguishing between Single Suicide Attempters (SSA) and Multiple Suicide Attempters (MSA) in a Spanish multicentre national cohort to explore prediction of subsequent attempts in suicidal patients. Two models including the same sociodemographic and clinical variables grouped in more specific (Model I) or broad (Model II) categories were developed to explore risk factors for suicide reattempts. A Least Absolute Shrinkage and Regression Operator logistic regression with a 10-fold cross-validation was adopted. 1443 adult patients from the SURVIVE cohort were included (582 SSA and 861 MSA). Both Model I (AUC = 0.696; BAC = 0.644) and Model II (AUC = 0.678; BAC = 0.621) outperformed naïve majority-class classification for SSA and MSA. Bipolar disorder type II, binge-eating disorder, and schizophrenia variables weighted heavier on Model I for suicide reattempt-related; while eating disorder diagnosis, Africa as birthplace, affective disorder diagnosis, being employed, schizophrenia-spectrum disorder and substance use disorder diagnoses were the most important suicide reattempt-related of Model II. Affective disorders, eating disorders and schizophrenia-spectrum disorders emerged as the most important variables in predicting reattempts. Both models showed similar sensitivity and specificity when discriminating between SSA and MSA. Identifying specific risk factors for reattempts could have a significant impact on tailoring prevention strategies and interventions.
16 June 2026
Read appraisal →Journal of affective disorders
Emotion regulation-targeted interventions initiated during hospitalization for suicidal crisis: a systematic review and exploratory meta-analysis
INTRODUCTION: Suicide is a leading cause of premature death, with risk peaking around psychiatric hospitalization. Emotion dysregulation (ED) is recognized as a key factor contributing to suicidal ideation and behavior across psychiatric diagnoses. The efficacy of interventions that target ED for suicidality reduction remains unclear, so we aimed to assess these interventions in psychiatric inpatients. METHODS: We conducted a PRISMA-compliant, PROSPERO-registered (CRD420251140949) systematic review and meta-analysis of randomized and non-randomized trials evaluating interventions targeting emotion regulation (ER) for psychiatric inpatients with suicidal ideation and/or behavior. Searches covered PubMed, Embase, and PsycINFO to September 18, 2025. Standardized mean differences (Hedges' g) were pooled under random-effects models and heterogeneity was assessed (Q, I2, τ2). Exploratory meta-regressions examined age, sex, and study quality; small-study effects were explored with funnel plots. RESULTS: Twelve studies met inclusion (n = 1708), spanning dialectical behavior therapy (DBT), mindfulness-based interventions, acceptance and commitment therapy (ACT), and other ER-related protocols. Across modalities, feasibility and acceptability were high and interventions consistently improved suicide-related outcomes in transdiagnostic adolescents and adult populations. Suicide attempts and ideation met outcome criteria for exploratory meta-analyses, and, for both, ER-targeted interventions outperformed usual care, despite heterogeneity and small samples. Exploratory meta-regressions were non-significant; funnel plots did not suggest marked publication bias, though power was limited. CONCLUSIONS: ER-targeted interventions delivered during psychiatric hospitalization are feasible and show promising benefits on suicide-related outcomes. Given heterogeneity and limited sample sizes, larger, well-powered randomized trials using standardized suicidality endpoints are needed to establish efficacy, refine inpatient protocols, and inform acute care pathways.
16 June 2026
Read appraisal →Journal of affective disorders
Multiple peripheral inflammatory markers in adolescents with major depressive disorder treated with repeated esketamine infusions: results from a randomized controlled trial
OBJECTIVE: While inflammatory dysregulation is well-documented in major depressive disorder (MDD), its response to esketamine treatment in adolescents remains poorly understood. We aimed to examine the effects of esketamine on the inflammatory cytokines in adolescent MDD. METHODS: This randomized, double-blind trial assigned 49 adolescents with MDD to receive three infusions of esketamine or midazolam over five days between December 2020 and April 2022. Suicidal ideation (measured by Columbia Suicide Severity Rating Scale, C-SSRS), depressive symptoms (measured by Montgomery-Åsberg Depression Rating Scale, MADRS) and plasma levels of 10 inflammatory markers were assessed at baseline, Day 6, and Day 12. RESULTS: Linear mixed model revealed significant time effects for all markers, but no significant drug main effect was observed. In esketamine group, alterations in IL-8 were associated with the reduction of C-SSRS Intensity at Day 6 (r = 0.444, p = 0.026), and alterations in IFN-γ were correlated with the reduction of MADRS (r = 0.399, p = 0.048) and C-SSRS Intensity (r = 0.427, p = 0.033) at Day 12. Baseline C4 negatively related to the reductions of MADRS and C-SSRS Ideation at Day 6 and 12 (all p < 0.005). CONCLUSIONS: Our study found no significant drug main effects for inflammatory markers, with most changes representing time effects observed in both groups. Exploratory results indicate that IL-8, IFN-γ, and C4 may warrant further investigation as potential biomarkers of esketamine response in adolescent MDD.
15 June 2026
Read appraisal →International journal of medical informatics
Accuracy and completeness of large language models in Epidemic keratoconjunctivitis Queries: A Comparative study
BACKGROUND: Large language models (LLMs) are increasingly applied in clinical contexts, yet their reliability in disease-specific ophthalmic domains remains insufficiently characterized. Epidemic keratoconjunctivitis (EKC), a highly contagious adenoviral ocular infection, represents a critical area where accurate diagnostic and therapeutic information is essential. This study aimed to assess and compare the accuracy and completeness of responses generated by two widely used LLMs, namely ChatGPT and Meta AI, to standardized ophthalmology questions related to EKC. METHODS: This cross-sectional expert evaluation study was conducted between January and March 2025. Thirty-four structured questions covering five EKC domains (etiology, clinical features, diagnosis, treatment, and prognosis) were developed using authoritative ophthalmology sources. Responses generated by ChatGPT (Plus subscription, web interface) and Meta AI (web interface) were independently scored by two fellowship-trained cornea specialists using a five-point Likert scale for accuracy and completeness. Inter-rater agreement was assessed using Cohen's κ and mean absolute error (MAE). RESULTS: Both models demonstrated strong overall performance, with mean accuracy and completeness scores of 4.87 and 4.90 for ChatGPT and 4.87 and 4.93 for Meta AI, respectively. Agreement between expert raters was substantial (κ range 0.78-0.88). Domain-specific analysis revealed consistently high scores across all categories, with Meta AI showing slightly higher completeness, particularly in diagnostic and symptom-related responses. CONCLUSION: LLMs exhibit consistently strong performance and domain consistency when addressing ophthalmic queries related to EKC. Their performance supports their potential use as adjunct informational and educational tools for ophthalmic conditions such as EKC, particularly for structured knowledge retrieval, treatment explanation, and patient counseling, provided that use remains under appropriate professional oversight.
15 June 2026
Read appraisal →Journal of affective disorders
MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.
BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a substantial public health burden. However, current diagnostic approaches largely rely on subjective assessments and lack the ability to integrate heterogeneous clinical and sociodemographic information. Recent advances in large language models (LLMs) offer new opportunities to support MDD diagnosis through reasoning over complex data, yet their clinical applicability is constrained by challenges related to interpretability, hallucinations, and reliance on synthetic data. METHODS: We propose MDD-Thinker, an LLM-based diagnostic system that integrates supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance reasoning and interpretability under the evaluated conditions. Using the UK Biobank dataset, we constructed 40,000 structured reasoning samples and incorporated an additional 10,000 records from publicly available mental health datasets. MDD-Thinker was trained on these heterogeneous textual data and evaluated against conventional machine learning models, deep learning methods, and representative LLM baselines in terms of diagnostic performance and interpretability. RESULTS: MDD-Thinker achieved high performance in MDD diagnosis, with an accuracy of 0.8268 and an F1-score of 0.8081, showing better performance than conventional machine learning models, deep learning approaches, and representative LLM baselines on the evaluated dataset. Beyond predictive accuracy, it consistently produced structured reasoning paths that were clinically coherent, enabling transparent interpretation of diagnostic decisions in the evaluated experiments. The integration of SFT and RL contributed to notable improvements in both diagnostic reliability and reasoning quality. CONCLUSION: MDD-Thinker demonstrates the potential of reasoning-enhanced LLMs for large-scale MDD diagnosis under the evaluated settings. By jointly optimizing accuracy, interpretability, and efficiency, the proposed system provides a scalable and explainable approach for intelligent psychiatric assessment within the scope of the study, highlighting the potential of reasoning-oriented LLMs in mental health care.
15 June 2026
Read appraisal →International journal of medical informatics
Artificial intelligence applications in emergency triage for suspected acute coronary syndrome: a systematic review
BACKGROUND: Acute coronary syndromes (ACS) are time-critical conditions requiring rapid and accurate triage in the emergency department. Traditional triage may lead to delays, whereas artificial intelligence (AI) has the potential to enhance triage accuracy and efficiency. OBJECTIVE: This study aimed to synthesize and evaluate the effectiveness of AI applications in emergency triage or early clinical decision-making for suspected ACS, focusing on their impact on diagnostic accuracy, time to treatment, and operational outcomes. METHODS: This systematic review followed PRISMA guidelines. PubMed, Scopus, CINAHL, and IEEE Xplore were searched for studies published between 2020 and 2025. Eligible articles examined AI applications for triage of suspected ACS in the emergency department. Data on study design, AI models, comparators, inputs, and outcomes were extracted, and study quality was assessed using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Fifteen studies from multiple countries and study designs were included. Convolutional neural networks and ensemble learning methods were the most used models. AI models generally demonstrated high diagnostic performance (AUROC 0.82-0.99), were associated with reductions in treatment times such as door-to-balloon and catheterization intervals, and showed potential to improve operational outcomes, including resource utilization and patient flow. However, findings varied across studies depending on model type, data inputs, and study design. CONCLUSIONS: AI-assisted triage for suspected ACS shows promise in supporting clinical decision-making and improving workflow efficiency. However, substantial heterogeneity and limited prospective validation suggest that findings should be interpreted cautiously. Further research is needed to confirm clinical effectiveness, generalizability, and safe implementation.
15 June 2026
Read appraisal →British journal of sports medicine
Randomised three-armed trial investigation of the Copenhagen Achilles tendon Rupture Treatment Algorithm (CARTA) for individualised treatment of acute Achilles tendon rupture
OBJECTIVE: To evaluate whether individualised treatment using the Copenhagen Achilles Rupture Treatment Algorithm (CARTA) is superior to default operative or non-operative strategies. METHODS: We conducted a multicentre, three-arm, randomised controlled trial. Adults with acute Achilles tendon rupture were randomised (1:1:1) to individualised CARTA, non-operative or operative treatment. In the CARTA arm, surgery was indicated if ultrasound showed<25% tendon overlap or ≥7% elongation. The primary outcome was the Heel-Rise Work Test (HRWT) at 12 months. Secondary outcomes included HRWT at 6 months, and at both 6 and 12 months, the Heel-Rise Height, Achilles tendon Total Rupture Score (ATRS), Tegner activity scale, Copenhagen Achilles tendon Length Measure, Achilles Tendon Resting Angle (ATRA) and complication recordings. RESULTS: Between May 2018 and June 2023, 970 patients were screened; 300 were randomised (male/female 76%/24%, mean age 41 (SD 1)). At 12 months, data were available for 98 CARTA (64% operated), 100 non-operative and 97 operative patients. There were no significant between-group differences in HRWT at 12 months. However, CARTA significantly reduced rerupture rates by 73% compared with non-operative treatment (3% (95% CI) (1 to 8) vs 11% (6 to 19), p=0.03), improved ATRS by 8 points (1 to 15, p=0.02) and improved ATRA by -2° (-4 to -1), p=0.01). No difference between CARTA and operative treatment was found except 36% less underwent surgery. CONCLUSION: Individualised CARTA did not improve functional outcomes based on the HRWT at 12 months, but among secondary outcomes, it reduced rerupture rates and improved patient-reported outcomes compared with non-operative treatment and with fewer surgeries than routine operative care. TRIAL REGISTRATION NUMBER: NCT03525964.
14 June 2026
Read appraisal →British journal of sports medicine
Multimodal symptom-targeted treatment for young people with persisting post-concussion symptoms: a randomised clinical trial
OBJECTIVE: To evaluate the effectiveness of Concussion Essentials (CE), a symptom-targeted, multidisciplinary treatment for persisting post-concussion symptoms (PPCS). METHODS: This single-site, open-label, assessor-blinded, parallel-group randomised clinical trial recruited 8-18-year-olds within 17 days of concussion from the emergency department of a tertiary paediatric hospital and from community referrals. Participants who endorsed ongoing PPCS on review at 14 (±3) days post-injury were invited to a baseline assessment at 3 weeks post-injury, where symptoms were re-assessed. Symptomatic participants were randomised to CE or usual care (UC). CE participants received individualised, multidisciplinary treatment modules (education, psychological, physiotherapy) for up to 8 weeks, while UC participants received weekly monitoring. Primary outcome was full recovery at intervention completion, defined as <1 symptom of increased severity compared with pre-injury on the Post-Concussion Symptom Inventory, parent version (PCSI-P). RESULTS: Participants were recruited between August 2019 and July 2024. Outcomes were analysed for 158 participants (CE=78; UC=80, mean age 13±2.5 years, 57.6% male). Pre-treatment PCSI-P total mean scores were: CE 36.7 (95% CI 31.0 to 42.4) and UC 33.5 (95% CI 28.4 to 38.7). Proportions with full resolution were CE 62.5% and UC 37.0% (adjusted risk difference=25.5%, 95% CI -10.0 to 41.0%). Post-treatment mean scores were CE 8.5 (5.9-11.2), UC 21.8 (95% CI 15.6 to 27.9) (p<0.0001). Seven adverse events, unrelated to trial factors, were recorded. CONCLUSION: Individualised, multidisciplinary treatment is associated with accelerated recovery in young people with PPCS. Implementation of such approaches should be considered in routine clinical care of concussion. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (number: ACTRN12617000418370).
14 June 2026
Read appraisal →Computer assisted surgery (Abingdon, England)
DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation
Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound guidance, the complex anatomical structure of the lumbar plexus poses significant challenges for anesthesiologists during the procedure. To accurately identify the lumbar plexus located in the posterior third of the psoas major in the Shamrock view, a deep learning-based segmentation model named DMRNet was proposed. This model is designed to precisely delineate muscles, nerves, and bony structures in Shamrock view ultrasound images. DMRNet integrates several innovative modules, including Adaptive Multi-Scale Dilated (AMD) Module that enhances the model's ability to capture multi-scale features; Dense Attention Residual (DAR) Module that adaptively selects salient feature regions; Attention-Enhanced Hybrid (AEH) Module that emphasizes critical features while suppressing irrelevant ones; and two attention mechanisms, Boundary-Aware Spatial Attention Mechanism (BASA) and the Enhanced Residual Multi-Head Attention Mechanism (ER-MHA), that improve the model's capacity to recognize complex contextual patterns. Experimental results demonstrated that DMRNet achieved a mean Intersection over Union of 0.863 and a mean Dice coefficient of 0.926 across all target structures, outperforming other state-of-the-art models. These findings suggest that DMRNet may serve as a useful assistive tool for sonoanatomical recognition in Shamrock view ultrasound images and may provide educational support for ultrasound-guided LPB training. A dynamic multi-scale residual network is proposed for lumbar lexus (LP) segmentation.Two attention mechanisms were improved, and three modules were proposed to improve downsampling, skip connection, and upsampling.We built our own Shamrock view ultrasound database containing the lumbar plexus nerves, and the Anesthesiologist physician of the anesthesiology department annotated it using Labelme.External validation was performed using the Ultrasonic Brachial Plexus Dataset (UBPD).
14 June 2026
Read appraisal →PloS one
Internet-based vestibular rehabilitation versus written instructions after acute vertigo: A randomised controlled trial
BACKGROUND: Acute vestibular syndrome (AVS) causes sudden and continuous vertigo, dizziness, and postural instability and is a common reason for emergency department visits, most commonly due to vestibular neuritis and, in rare cases, stroke. Vestibular rehabilitation (VR) is an evidence-based exercise therapy that facilitates vestibular compensation and is effective in chronic vestibular disorders. However, evidence for VR in acute vertigo remains limited and, despite guideline support, it remains underused in practice. Internet-based VR has demonstrated benefit in chronic dizziness but has not been evaluated in acute vertigo. This study aimed to evaluate the efficacy of an internet-based VR tool compared with standard care (written instructions) in reducing vestibular symptoms after acute onset vertigo. METHODS AND FINDINGS: A multicentre, randomised, evaluator-blinded superiority trial was conducted across nine hospitals in Sweden. Adults with ongoing AVS were recruited within 1-7 days after symptom onset and randomised (1:1) to six weeks of internet-based VR with personalised, progressively adjusted home exercises or to written instructions for home-based VR exercises. The primary outcome was the between-group difference in vestibular symptoms at six weeks measured using the Vertigo Symptom Scale Short Form (VSS-SF; range 0-60), with a difference of ≥3 points prespecified as clinically significant. Secondary outcomes included dizziness-related disability, walking speed, and balance. Of 184 randomised participants, 183 were included in the analyses (94 online VR; 89 written instructions; median age 56 years). In the intention-to-treat analysis, both groups improved at six weeks, with no significant between-group difference (adjusted mean difference -2.0 points, 95% CI -4.9 to 0.9; p = 0.18). Per-protocol analyses were consistent (-1.7 points, 95% CI -4.7 to 1.3; p = 0.27). Over 12 weeks, both groups showed reduced vestibular symptoms and improved disability, balance, and walking speed, without significant between-group differences. No serious adverse events were attributed to the interventions, and compliance was high in both groups. CONCLUSIONS: Internet-based VR was not superior to written instructions in reducing vestibular symptoms six weeks after acute onset vertigo. Both groups demonstrated improvement from baseline, with no statistically significant between-group differences. These findings suggest that ensuring access to vestibular rehabilitation exercises may be more important than the specific mode of delivery after acute onset vertigo, and internet-based tools represent viable alternatives for patients who prefer or may benefit from a digital format. TRIAL REGISTRATION: Clinicaltrials.gov NCT05056324. https://clinicaltrials.gov/study/NCT05056324. Registered on September 24, 2021.
14 June 2026
Read appraisal →PloS one
Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair
BACKGROUND: Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with risks such as endoleaks and late aneurysm rupture, highlighting the importance of long-term survival prediction. Despite recent advancements in machine learning (ML), predictive models utilizing time-to-event analysis remain limited for AAA patients undergoing EVAR. We aimed to develop a stacking ensemble ML model to predict long-term outcomes in EVAR-treated AAA patients. METHODS: From 2002 to 2019, a total of 12,312 patients underwent EVAR. The primary outcome was AAA-related mortality, with follow-up until December 31, 2019. Using 5 ML algorithms, we developed a model comprising 34 variables. Model performance was assessed using the time-dependent C-index and Brier score. Variable importance was evaluated through permutation-based and partial dependent plots. RESULTS: The stacking ensemble model showed the best predictive performance among the tested models (time-dependent C-index: 0.759 at 30 days, 0.716 at 365 days). The time-dependent Brier scores generally increased slightly over time but remained stable across all ML algorithms. Important predictors included age, smoking status, duration between diagnosis and surgery, household income, renal function, and blood pressure. Variable importance differed over time, and each predictor presented a nonlinear relationship with AAA-related mortality risk. CONCLUSION: The stacking ensemble ML model for time-to-event prediction identified dynamic, time-varying changes in predictor importance, providing improved risk stratification and phase-specific management after EVAR.
14 June 2026
Read appraisal →Journal of the American College of Surgeons
Improving Trauma Triage Accuracy with Large Language Models: A Comparison to Human Expert Decisions
BACKGROUND: Accurate prehospital trauma triage and communication determine morbidity, mortality, and system efficiency. Advancements in large language models (LLMs) offer an opportunity to improve triage and yet to be implemented in prehospital trauma triage. STUDY DESIGN: This retrospective cohort study evaluates LLM performance in trauma triage and accuracy of prehospital tele-communication. Of 410 pediatric activations at a Level I center (January 2023 to May 2025, IRB no. 00009569), 133 activations with emergency medical service recordings, human-generated trauma pages, and injury severity scores were analyzed. Audio was transcribed with OpenAI Whisper. Structured "Essential Transcripts" were generated with named entity recognition. Entity ablation tested redaction of triage parameters on accuracy. In a prospective arm, trauma surgeons reviewed emergency medical service transcripts and triaged activations pre- and post-LLM exposure. Cribari criteria defined over and undertriage. McNemar's test and 95% CIs assessed paired differences in accuracy. RESULTS: The primary endpoint: LLM undertriage demonstrated modest improvement; 4.8% (3 to 8.2) vs 5.1% (3.1 to 9.3, p = 0.73, Bonferroni p = 1). For secondary endpoints, LLM triage outperformed human clinicians: 83.5% accuracy (80.6 to 90.6) vs 78.9% (73.9 to 82.6, p < 0.01, Bonferroni p < 0.01), with overtriage 58.6% (51.4 to 73.7) vs 71.8% (p < 0.05, Bonferroni p < 0.09). "Essential Transcripts" reduced transcript length by 80.8% (p < 0.001) while preserving accuracy (81.9%, 76.6 to 87.5, p < 0.001, Bonferroni p < 0.05). Entity ablation had marginal effect on triage. In prospective evaluation, human triage accuracy improved after LLM exposure (73.7% [69.8 to 77.2]) to 75.8% ([71.9 to 79.2], p = 0.04, Bonferroni p = 0.12), significantly improving the odds of a correct triage decision (odds ratio 2.57, 95% CI 1.39 to 6.83, p < 0.01). CONCLUSIONS: LLMs achieve triage accuracy comparable to trauma staff in retrospective review of pediatric trauma. Further validation is needed to assess clinical outcomes, generalizability, and user acceptance before widespread deployment.
14 June 2026
Read appraisal →BMJ open
Difficult intravenous access (DIVA) in the adult population: an umbrella review.
OBJECTIVES: To synthesise evidence from systematic reviews on difficult intravenous access (DIVA) in adults, focusing on definitions, diagnostic criteria, risk factors and clinical interventions to guide clinical practice and health policy. DESIGN: Umbrella review of systematic reviews and meta-analyses. SETTING: Any clinical setting involving adult patients requiring peripheral venous access (including hospital, emergency and outpatient care). DATA SOURCES: A systematic search was performed in PubMed, CINAHL, Cochrane, Scopus and Web of Science in July 2025. ELIGIBILITY CRITERIA: Systematic reviews and meta-analyses published from 2014 to 2025 that addressed DIVA in the adult population were included. Primary studies and protocols were excluded. DATA EXTRACTION AND SYNTHESIS: Methodological quality was assessed using the Risk Of Bias In Systematic Reviews tool. Data extraction followed the Joanna Briggs Institute methodology for overviews and the Preferred Reporting Items for Overviews of Reviews reporting guideline. RESULTS: Seven reviews (six systematic reviews and one meta-analysis) were included. Three analytical dimensions emerged: (1) the conceptual and operational definition of DIVA, identifying common elements such as ≥2 failed attempts, lack of visible or palpable veins and a documented history of difficult access; (2) risk factors and clinical assessment, highlighting obesity, chronic diseases, prior chemotherapy, venous invisibility or non-palpability and the limited validation of diagnostic tools and (3) interventions, including organisational strategies (escalation protocols, specialised teams), technological resources (ultrasound guidance) and clinical measures (pain management and technique optimisation). CONCLUSIONS: DIVA is a multifactorial challenge that requires a standardised definition to improve clinical identification. Effective management relies on a combination of specialised training, the use of ultrasound technology and the implementation of escalation protocols to ensure patient safety and efficiency. PROSPERO REGISTRATION NUMBER: CRD420251084947.
14 June 2026
Read appraisal →Critical care explorations
Multiagent First-Person Perspective Analysis for Leadership Assessment in Pediatric Emergency Simulations: A Feasibility Study
BACKGROUND: Conventional leadership assessments provide structured ratings but offer limited observer-independent metrics. Because leadership and teamwork are interdependent, capturing team dynamics requires multiagent, time- and space-synchronized analysis. This pilot study examined the feasibility of applying multiagent, spatiotemporally synchronized first-person perspective analysis (FPA) in emergency simulations. METHODS: This exploratory simulation study was conducted in a real patient-care room within the PICU of our institution. Two physicians-a critical care specialist (L1) and a pediatric resident (L2)-each completed one scenario as team leader. Leadership behaviors were evaluated using the Ottawa Global Rating Scale, focusing on Situational Awareness and Communication. Wearable smart glasses recorded first-person video and gaze data, enabling identification of fixations within five predefined areas ("patient," "member," "device," "screen," and "unknown"). Eye-contact events were quantified using face detection based on You Only Look Once, version 8, a deep learning-based object detection model, combined with gaze-vector estimation, and analyzed in relation to the timing of verbal communication. RESULTS: Multiagent, time-synchronized FPA was implemented without device- or software-related interruption, and no critical data loss occurred. The mean analyzable session duration relative to total operating time was 77.1%, and the mean percentage of frames excluded from object tracking was 29.3%, primarily due to target objects being outside the field of view. As secondary, illustrative findings, Ottawa Global Rating Scale scores indicated that both leaders were rated within the competent range. FPA further showed that both leaders spent approximately 80% of the scenario fixating on predefined relevant areas (L1: 599/731 s; L2: 565/693 s). Greater patient-directed fixation and balanced gaze transitions were observed in L1, whereas L2 showed prolonged fixation within nonspecific areas. L1 also established eye contact with team members more frequently during direct orders or information sharing. CONCLUSIONS: This study demonstrates the feasibility of multiagent, time-synchronized FPA for extracting gaze-based behavioral information relevant to leadership assessment in emergency simulations.
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes.
BACKGROUND: We aimed to evaluate the impact of implementing an artificial intelligence (AI)-enabled acute ischaemic stroke triage system on workflow efficiency and transfer optimisation in a large academic healthcare network. METHODS: A prospectively maintained database was reviewed comparing equivalent time periods before and after AI-enabled triage platform implementation (January 2021-December 2022). The primary analysis compared workflow metrics between AI-enabled and non-AI spokes during the same calendar period (2022) to control for temporal confounding. Benjamini-Hochberg correction was applied for multiple comparisons, and analyses were adjusted for age and baseline National Institutes of Health Stroke Scale. Evaluated outcomes included door-in-door-out (DIDO) times, door-to-puncture (DTP) times, endovascular therapy (EVT) utilisation rates, cost analysis and clinical outcomes at discharge. RESULTS: The study included 4548 admissions with 844 EVT patients (394 pre-implementation, 450 post-implementation) across four hub centres. In the primary same-period analysis (2022), AI-enabled spokes demonstrated significantly shorter DIDO times compared with non-AI spokes (median 103 (92-118) vs 134 (103-162) min; adjusted difference -41.6 min (95% CI -60.9 to -24.1); p<0.001, Q<0.001) and shorter DTP times (21 (14-43) vs 40 (18-65) min; adjusted difference -10.9 min (95% CI -17.9 to -3.7); p=0.003, Q=0.009). A difference-in-differences analysis demonstrated that DIDO improvements were specific to AI-enabled spokes (-27 min; 95% CI -62 to -4; p=0.029). EVT utilisation was also significantly higher in AI-enabled versus non-AI spokes where AI-enabled spokes had increased EVT rates by +17.8% (39.3% to 57.1%) compared with +1.1% in non-AI spokes (41.3% to 42.4%, Pinteraction=0.006). DTP improvements were more pronounced at community hubs (86 (48-108) to 51 (22-77) min; adjusted difference -24.9 min; p=0.021, Q=0.041) compared with academic hubs (60 (23-87) to 55 (22-73) min; adjusted difference -15.5 min; p<0.001, Q=0.002). Subgroup analyses demonstrated consistent DIDO benefits across age, stroke severity and sex strata with no significant treatment effect heterogeneity (all P-interaction >0.05). Probabilistic cost analysis estimated savings of $3.6 million (95% CI $1.5M to $6.1M) per 1000 AI-enabled spoke transfers. Clinical outcomes, including functional status and mortality at discharge, were similar between groups (all Q>0.05). CONCLUSION: Implementation of an AI-enabled triage platform was associated with significant reductions in workflow times and increased EVT utilisation, with effects specific to AI-enabled spokes rather than secular trends alone. The proportion of transfers who did not proceed to EVT decreased in AI-enabled spokes, though counterfactual outcomes for non-transferred patients remain unknown. Clinical outcomes at discharge were unchanged.
14 June 2026
Read appraisal →Scientific reports
AIDx: a locally deployable AI system for physician clinical decision support
The dynamic environment of medicine, particularly in settings such as the Emergency Department, challenges physicians with an influx of patient data and the need for swift diagnosis and treatment under high-pressure conditions. Numerous AI models have been developed for medical applications, yet their integration into hospital systems remains limited because they often do not align with existing clinical workflows. To address these challenges, I developed AIDx, an AI-powered system designed to help physicians streamline clinical decision making, improve diagnostic support, and provide an integrated platform for AI-assisted analysis. Unlike many standalone systems, AIDx is designed for interoperability with electronic health record (EHR) systems and can leverage supplementary tools such as updated medical knowledge bases and auxiliary AI models. At its core, AIDx includes AIDx-Copilot, a large language model fine-tuned on de-identified EHR data and optionally grounded using retrieval-augmented generation (RAG) from open medical references. This study is a text-only benchmark assessment. I evaluated AIDx-Copilot using the MultiMedQA benchmark suite under a unified, deterministic, single-pass protocol (temperature=0; no voting). Across nine MultiMedQA subsets (MedQA, PubMedQA, MedMCQA, and medically focused MMLU subjects), AIDx-Copilot achieves a mean accuracy of 83.61% (SD 7.37). I report per-dataset Wilson 95% confidence intervals to quantify uncertainty from finite test set sizes. To isolate component contributions, I conducted ablation experiments comparing the base model, the fine-tuned model without retrieval, and the fine-tuned model with RAG enabled. EHR-based fine-tuning accounts for the majority of the performance gain (+17.8 percentage points over the base model on average), while RAG provides a modest additional benefit (+0.4 points on average) that varies by dataset. A qualitative error analysis of 200 incorrectly answered items identifies knowledge gaps (41.0%) and reasoning errors (38.0%) as the dominant failure modes. Comparative numbers for larger proprietary systems are provided only as context in the supplementary material because they may use different prompts and inference settings. The deployment configuration (quantized weights and on-premises serving) supports fast inference and local deployment. Under the quantized configuration, the system achieves a median latency of 0.84 seconds per query and fits within 28.1 GB of VRAM across two commodity GPUs. The primary results are based on public benchmark evaluations without RAG; I did not perform clinical, user-study, or real-world validations. In addition, while AIDx is designed to support fully local operation, specific deployments may choose to use third-party embedding or vector-search services; this paper documents the data-flow boundary and a fully local configuration path.
14 June 2026
Read appraisal →JMIR formative research
Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial
BACKGROUND: Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs. OBJECTIVE: This study aimed to evaluate the feasibility, engagement, and preliminary effectiveness of a multimodal Digital Health Monitoring and Intervention suite for Stress framework integrating VR simulation, psychoeducation, EMAs, and wearable biometrics. We examined (1) the impact of VR simulation and psychoeducation on stress outcomes and (2) associations between physiological and self-reported mental health outcomes. METHODS: Ninety-nine nurses (mean age 33.7, SD 8.9 yr, 87% female) were enrolled in 2023. We conducted a single-arm prospective cohort study (NCT05923398). Using convenience sampling, participants were recruited from social media advertisements, flyers, and email notices distributed through professional listservs. Participants completed ≥2-week baseline monitoring, a single VR session (2 runs separated by a brief psychoeducation intervention), and 12-week follow-up. In-VR stress was assessed using the Subjective Units of Distress Scale (SUDS) and 4-item Moral Injury Outcome Scale (MIOS-4), with synchronous heart rate variability. Longitudinal outcomes included weekly and biweekly EMAs alongside 70 wearable-derived features. Paired t tests, aligned rank transform ANOVA, and Pearson correlations informed study objectives, with P values adjusted for multiple comparisons. Qualitative content analysis classified emotional responses during and after VR. RESULTS: VR significantly increased subjective stress across checkpoints in both runs, with attenuation in Run B relative to Run A (all P<.001). No significant heart rate variability differences were observed between runs (P=.15). During VR, 92% (91/99) of participants felt stressed, 36% (36/99) reported anxiety or nervousness, and 51% (50/99)-78% (77/99) endorsed anger, guilt, shame, and/or betrayal. Most (59/99, 60%) HCWs returned to an emotional baseline post-VR, although 12% (12/99) reported lingering distress. Immediate reliable improvements in anger, guilt, shame, and/or betrayal occurred for 50% (50/99)-75% (74/99) of participants post intervention. Anxiety (mean -0.53, SD 2.34; P=.03) and stress (mean -3.05, SD 11.35; P=.01) decreased 2 weeks post intervention, but were not sustained at 12 weeks. Increased sleep restlessness was the only wearable feature showing significant changes (mean 2.46%, SD 5.43; Padj<.001). In-VR stress correlated with 12-week real-world stress (SUDS: r=0.57-0.58; MIOS-4: r=0.58-0.61; all P<.01). Data completion exceeded 90%, with 71% achieving full compliance. CONCLUSIONS: This study moves beyond single-tool interventions to demonstrate the feasibility and preliminary effectiveness of an integrated, multimodal stress platform within a single coordinated framework. This trial demonstrates high engagement, short-term symptom responsiveness, ecological validity, and emotional safety. The framework provides a scalable model for proactive stress identification, skills training, and implementation in high-risk occupational settings. Randomized controlled trials are needed to establish sustained efficacy and optimize deployment for real-world implementation.
13 June 2026
Read appraisal →Journal of epidemiology and community health
Role of socioeconomic status in modifying the relationship between alcohol use and injury events: evidence from the UK Biobank
BACKGROUND: Alcohol consumption is a well-established risk factor for injuries, yet the role of socioeconomic status (SES) in modifying this relationship remains underexplored. This study examines how SES influences the association between alcohol use and injury events, including both fatal and non-fatal injuries as well as the risk of repeated injuries requiring hospitalisation. METHODS: Using data from the UK Biobank, we applied Cox proportional hazards models to assess whether SES modifies the relationship between different dimensions of alcohol use, average level and heavy episodic drinking (HED), and injury events. For injuries requiring hospitalisation, we fitted negative binomial regression models to estimate incidence rate ratios (IRRs) for the total number of unique injury admissions per individual. RESULTS: Among 482 078 participants, 48 045 injuries were recorded during follow-up. A statistically significant interaction was observed between alcohol use and low education, with individuals in lower education groups showing elevated injury risk compared with the high education group, particularly among those consuming 20-40 g/day with HED (HR: 1.17, 95% CI 1.06 to 1.28) and those consuming over 40 g/day (HR: 1.13, 95% CI 1.04 to 1.23). For non-fatal injuries, similar statistically significant interactions were found (IRR 1.17, 95% CI 1.06 to 1.28; and 1.18, 95% CI 1.08 to 1.29, respectively). CONCLUSION: Our findings highlight that individuals with lower SES face higher risks of alcohol-related injuries. Future research should investigate specific mechanisms underlying these disparities to inform more targeted policies. Population-based pricing policies could be combined with community-based programmes and tailored health messaging to address SES-related vulnerabilities.
13 June 2026
Read appraisal →The Journal of clinical psychiatry
Placebo Effects in Alcohol, Cocaine, and Methamphetamine Use Disorder: A Systematic Review and Meta-Analysis
Objective: The placebo effect is established in psychiatry, yet its role in substance use disorders (SUDs) treatment remains unclear. The aim of this study was to evaluate changes in craving scores in placebo groups in randomized controlled trials (RCTs) across different SUDs in adults. Data sources: Eight different databases (PubMed, Embase, Web of Science, Cochrane Library, Emcare, MEDLINE, PsycINFO, and Academic Search) were systematically searched up to October 28, 2024, by 2 independent researchers. Study selection: Placebo-controlled, double-blind RCTs assessing craving in patients with SUDs were included based on predefined eligibility criteria, published in English, Dutch, Spanish, or German language. Search terms were a combination and synonyms of "craving," "addiction," and "placebo." From a total of 1945 unique records identified, 9 articles met our eligibility criteria. Data extraction: Data on demographic characteristics, study design, and outcome results were extracted. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated for pre-post craving scores within each placebo group; a positive difference point towards improvement in the postplacebo period. Results: A total of n=495 placebo-treated participants were included from nine RCTs in three different SUDs (n=300 in alcohol, n=88 in cocaine, and n=107 in methamphetamine use disorder). Craving scores significantly decreased over time (meaning symptom improvement) in the placebo treatment group in alcohol use disorder (5 studies, SMD=0.93 [95% CI=0.33 to 1.54]) but did not reach statistical significance in cocaine use disorder (2 studies, SMD=0.61 [95% CI=-1.41 to 2.62]) or in (meth)amphetamine use disorder (2 studies, SMD=0.44 [95% CI=-3.43 to 4.32]). Conclusions: Placebo treatment led to reductions in craving scores across all three SUDs, although statistical significance was achieved only in alcohol use disorder. Given the low number of studies for cocaine and (meth)amphetamine use disorder, these estimates were captured with much uncertainty. These findings may inform the design of future RCTs and support the development of more individualized treatment approaches in clinical settings.
13 June 2026
Read appraisal →JMIR medical informatics
Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review
BACKGROUND: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical translation face challenges. OBJECTIVE: This scoping review aims to systematically synthesize the current state of development, performance, and validation of ML models for predicting intraoperative bleeding, and to identify key barriers to their clinical implementation. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched 7 databases (PubMed, Web of Science, Embase, CINAHL, CNKI [China National Knowledge Infrastructure], Wanfang, and VIP [China Science and Technology Journal Database]) from their inception to April 2025. Moreover, 2 reviewers (SY and PZ) independently screened studies, extracted data using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and assessed the risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). A narrative synthesis was used for data analysis. RESULTS: Out of 2651 screened records, 23 studies were included (sample sizes ranging from 48 to 48,543). Tree-based ensemble models (eg, random forests and extreme gradient boosting) were the most frequently used (16/23, 70%), followed by logistic regression (13/23, 57%), and deep learning (11/23, 48%). Model discrimination varied widely (mean area under the curve [AUC] 0.82, SD 0.08, range 0.63-0.93). Integration of multimodal data (electronic health records+imaging) was associated with higher performance. However, model validation was often inadequate; only 6 studies (6/23, 26%) performed external validation, and performance often declined (eg, AUC decreased from 0.85 to 0.63 in 1 study). Reporting exhibited selective bias; AUC was commonly reported (19/23, 83%), whereas key classification metrics, such as calibration (10/23, 43%) and precision (4/23, 17%), were often omitted. PROBAST assessment indicated a high risk of bias in all included studies (23/23, 100%). CONCLUSIONS: While ML models demonstrate technical promise for predicting intraoperative bleeding, our PROBAST assessment revealed a universally high risk of bias across all included studies. This fundamental methodological limitation, coupled with a severe lack of external validation and poor transparency in reporting, severely constrains the current clinical reliability of these models. Future research must prioritize prospective multicenter validation, adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines, and enhanced model interpretability to bridge the gap toward clinical utility.
13 June 2026
Read appraisal →Journal of ethnopharmacology
Revealing the temporal metabolic trajectory and potential pharmacodynamic substances of Da-Cheng-Qi Decoction in treating patients with acute pancreatitis: A serum metabolomics-based study
ETHNOPHARMACOLOGICAL RELEVANCE: Acute Pancreatitis (AP) is a pancreatic inflammatory disease lacking specific therapeutics. Da-Cheng-Qi Decoction (DCQD) has been proven to alleviate the severity and mortality of AP. However, its underlying mechanisms remain to be fully elucidated. AIM OF THE STUDY: This study aimed to explore DCQD's dynamic metabolic regulatory effects on AP patients and potential pharmacodynamic substances via serum metabolomics and serum pharmacochemistry analysis, with validation in animal models. MATERIALS AND METHODS: AP patients were randomized into conventional therapy (Con) and Con + DCQD groups. Fasting venous blood samples were collected at four time points (admission, 24 h/96 h post-admission, pre-discharge) for biochemical assays and UHPLC-Q-Exactive-Orbitrap-MS analysis. Metabolomics was used to screen AP-related biomarkers, evaluate the two regimens' reversal effects, and compare inter-group differences in metabolic recovery trajectories. Prototypes and metabolites of DCQD were identified, with potential pharmacodynamic substances screened via correlation analysis with AP biomarkers. L-arginine-induced SAP mouse model was used for validation. RESULTS: Serum amylase normalization and C-reactive protein decline were accelerated in Con + DCQD group. Both regimens reversed AP-related energy, amino acid, and lipid metabolic disorders. Adjuvant DCQD therapy accelerated inflammation alleviation and energy homeostasis restoration in early stage, and improved lipid metabolism more comprehensively in middle-to-late stage, with efficacy validated in SAP mice. Forty-eight and 35 blood-migrated components were identified in patients and mice, respectively, with 8 key pharmacodynamic substances screened. CONCLUSIONS: Integrating serum metabolomics and multi-time-point monitoring, this study delineated AP's metabolic recovery trajectory and clarified DCQD's efficacy and dynamic metabolic regulation, providing guidance for its clinical application.
12 June 2026
Read appraisal →Annals of medicine
Analgesic effect of premixed nitrous oxide in postoperative rehabilitation for ankle fractures: a randomized controlled trial
INTRODUCTION: The global incidence of ankle fractures is on the rise, effective postoperative rehabilitation is a crucial aspect of surgical management. However, the occurrence of severe pain during the rehabilitation continues to pose a substantial clinical challenge. METHODS: The study utilizes a dual-arm, single-center, double-blind, randomized controlled trial design. A total of 100 participants were enrolled. Participants included patients experiencing acute pain (self-reported pain score ≥4) who underwent postoperative rehabilitation for ankle fractures. Participants undergoing rehabilitation training were randomized to receive either 65% nitrous oxide or 100% oxygen. The primary outcome measured was the pain score, while secondary outcomes encompassed anxiety scores, physiological indices, side effects, patient and therapist satisfaction, acceptance, and residual pain. RESULTS: Pain scores were found to be significantly lower in the nitrous oxide group than in the oxygen group (T1: median difference -3.0, [95% CI -3.4 to -2.6]; p < 0.001; T2: median difference -2.0, [95% CI -2.8 to -1.2]; p < 0.001). Compared with the oxygen group, both therapists (p < 0.001) and participants (p < 0.001) in the nitrous oxide group reported significantly higher satisfaction levels. Acceptance rate showed significant intergroup differences (p < 0.001). No severe adverse effects were observed in either group. Anxiety scores between the two groups did not show a significant difference (p = 0.31, η2 = 0.027). CONCLUSION: The safety and analgesic profile of nitrous oxide renders it a suitable option for pain management in postoperative ankle fractures rehabilitation, thus providing a safe and easily manageable alternative to the current available strategies. CLINICAL TRIAL REGISTRATION: We have registered at https://www.chictr.org.cn and the registration number is: ChiCTR2400089379. In comparison with oxygen gas, nitrous oxide has been demonstrated to reduce pain during rehabilitation training following ankle fracture surgery, offering a practical and effective alternative to oral analgesics.The use of nitrous oxide in postoperative rehabilitation training for ankle fractures is relatively safe, with few and mild adverse reactions.Patients and rehabilitation therapists reported high satisfaction with nitrous oxide intervention, and residual pain was significantly reduced.
12 June 2026
Read appraisal →Critical care explorations
Artificial Liver Support Effectively Removes Immunosuppressive Bile Acids From Circulation in Patients With Severe Liver Failure: A Proof of Concept Study
OBJECTIVE: Bile acids that induce immunosuppression through activation of the Takeda G protein-coupled receptor 5 (TGR5) have recently been identified in the circulation of patients with liver failure and sepsis. These bile acids are associated with increased susceptibility to infection and a high short-term mortality. Furthermore, TGR5 activation by bile acids has been implicated in the development of pruritus. However, therapies targeting TGR5-activating bile acids in patients have not yet been reported. This study investigates the effects of two artificial liver support systems, the Molecular Adsorbent Recirculating System (MARS) and the open albumin dialysis (OPAL). DESIGN: Proof of concept study, multicenter study. SETTING: University Hospital Jena, University Hospital Ulm, and University Hospital Münster, Germany. PATIENTS: Patients with severe liver failure, as defined by the Sequential Organ Failure Assessment hepatic sub-score (i.e., bilirubin > 12 mg/dL). INTERVENTIONS: Patients with severe liver failure were randomized to receive one session of MARS or OPAL for 8 hours, followed by crossover to the other treatment on the next day. MEASUREMENTS AND MAIN RESULTS: Bilirubin, circulating total and individual bile acids, albumin function, and TGR5 activation induced by circulating bile acids were assessed before and after each treatment session. Both OPAL and MARS significantly removed bilirubin, as well as individual and total bile acids from circulation. OPAL was superior to MARS in removing protein-bound lipophilic bile acids and restoring patients' albumin function (i.e., albumin-binding capacity, detoxification efficiency, and binding efficiency). TGR5 activation induced by circulating bile acids was reduced by 60% with OPAL compared with 39% with MARS (median values, p = 0.051). CONCLUSIONS: Artificial liver support systems effectively remove immunosuppressive bile acids from circulation. The clinical implications of bile acid reduction, however, require further study.
11 June 2026
Read appraisal →PloS one
Reinforcement learning for policymaking in epidemic control: A scoping review
BACKGROUND: Managing an epidemic demands policies that respond at the pace of the outbreak. Conventional rule‑based interventions struggle to keep up, prompting interest in reinforcement learning (RL) for designing non‑pharmaceutical interventions (NPIs). However, current evidence is fragmented across diverse models and reporting styles. OBJECTIVES: To systematically map how RL is applied for epidemic NPI design, describe modeling choices, algorithm architectures, evaluation practices, and identify trends and research gaps. METHODS: Peer-reviewed studies (2014-2025, English) that applied deep RL to select NPIs were retrieved from IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus, searched on December 23, 2025. Reference list scanning supplemented database results. Predefined data items (bibliographic details, epidemic and RL model characteristics, experiments, validation methods, outcomes) were charted and summarized descriptively. RESULTS: Of 512 retrieved records, 10 met the inclusion criteria, and three additional studies were identified via reference-list scanning, yielding 13. Five employed value‑based methods, four policy‑gradient, and four hybrid; one study additionally incorporated model-based planning. Six simulations relied on compartmental models, six on agent‑based models, and one on a hybrid model. Action spaces were predominantly discrete restriction levels. Five studies incorporated sequence-modeling techniques to include temporal context into a state space. Eleven studies designed reward functions as a trade-off between pandemic severity and socio-economic cost. According to the reviewed studies, RL policies across various settings outperform heuristic, rule-based, and historical baselines in reducing infections, deaths, or lockdown duration while limiting economic loss. CONCLUSIONS: RL shows promise for adaptive epidemic control. Comparison is hampered by simplified economic costs, inconsistent calibration rigor, varied evaluation metrics, and limited uncertainty or policy robustness analysis. Future work should establish common benchmark environments and reporting standards, incorporate empirically grounded economic and behavioral models, adopt uncertainty-aware and probabilistic RL, develop more sophisticated control spaces, investigate more advanced algorithms, and validate learned policies prospectively to enable real-world deployment.
9 June 2026
Read appraisal →Neurology
Acute Kidney Injury and Risk of Adverse Neurocognitive Outcomes: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Chronic kidney disease is a recognized risk factor for adverse neurocognitive outcomes, but the effect of acute kidney injury (AKI) on brain health remains less well defined. We conducted a systematic review and meta-analysis to evaluate associations between AKI and subsequent risk of stroke, delirium, and dementia. METHODS: Eligible studies were identified by searching Ovid MEDLINE and Embase from inception (Ovid: January 1946; Embase: January 1970) until April 2025. Studies were included if they reported quantitative estimates with measures of precision for the association between AKI and delirium, stroke, or dementia in adult populations. Two reviewers independently screened and extracted data, and study quality was assessed using standardized criteria. Study characteristics, participant demographics, and adjusted effect estimates (hazard ratios [HRs] or odds ratios [ORs]) with 95% CIs were extracted. Pooled HRs and ORs with 95% CIs were calculated using random-effects models. Heterogeneity was evaluated with the χ2 test and I2 statistic, and sources of heterogeneity were explored through prespecified subgroup analyses and meta-regression. RESULTS: We identified 49 studies comprising 11,253,825 participants with 1,279,145 events. Individuals with AKI were at increased risk of stroke (pooled adjusted HR 1.35, 95% CI 1.20-1.52), delirium (pooled adjusted OR 1.76; 1.42-2.17), and dementia (pooled adjusted HR 1.64, 1.41-1.89). A gradient of risk across increasing AKI stages was demonstrated for stroke (stage 1: HR 1.11; 1.00-1.23; combined stages 2 and 3: HR 1.57; 1.35-1.81). AKI was also associated with higher in-hospital and 90-day mortality poststroke (pooled HR 2.13, 1.56-2.90, and 4.81, 2.55-9.08, respectively) and with 90-day disability (pooled adjusted OR 1.47, 1.22-1.76). Associations between AKI and all outcomes were directionally consistent across sensitivity analyses and pooled propensity score-matched studies. DISCUSSION: In this systematic review and meta-analysis, AKI was consistently associated with increased short-term and long-term neurocognitive risk, including stroke, delirium, and dementia. These findings suggest that AKI may identify individuals vulnerable to both acute and chronic brain injury. Further studies are needed to clarify mechanisms linking AKI to brain injury and to identify strategies to mitigate neurocognitive risk in this high-risk population.
9 June 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Benchmarking spectroscopic-chemometric models for human bloodstain time since deposition in tropical outdoor microenvironments
Reliable estimation of the time since deposition (TSD) of bloodstains remains a key challenge in forensic reconstruction, particularly under realistic outdoor conditions. This study benchmarks visible (Vis) reflectance, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and micro-Raman spectroscopy, each coupled with partial least squares (PLS) regression and artificial neural networks (ANN), for TSD estimation of human bloodstains aged up to 100 days under naturally fluctuating tropical outdoor microenvironments. Blood from multiple donors was deposited on glass slides and aged under two conditions: open-air exposure and enclosed storage in sealed plastic bags. Spectra were acquired from the outer ring of each stain, preprocessed by standard normal variate transformation and partitioned by donor into training and independent test sets. Vis reflectance primarily tracked haemoglobin chromophore changes via Soret and α/β bands, whereas ATR-FTIR highlighted protein secondary structure and hydration through amide I-III and O-H/N-H stretching features. Micro-Raman spectra emphasised porphyrin breathing and CC stretching modes, together with aromatic amino-acid and amide bands that remained informative even at late ageing stages. Across all techniques, PLS models provided robust quantitative performance (R2P up to 0.98, RMSEP typically ∼5-10 days, RPD > 3), with micro-Raman-PLS giving the highest predictive accuracy and enclosed exposure consistently improving model stability relative to open air. ANN models did not provide systematic advantages over PLS and were more sensitive to condition-specific variance. In the context of previous outdoor ATR-FTIR studies, these results show that established Vis-, ATR-FTIR- and Raman-based chemometric models can deliver competitive TSD estimates when calibrated within clearly defined environmental domains, underscoring the importance of donor-independent and environmental-aware validation for forensic interpretation.
7 June 2026
Read appraisal →Bioorganic chemistry
Multi-relational knowledge graph for drug-drug interaction prediction via dual aggregation and collaborative optimization
Identifying potential drug-drug interactions is crucial in clinical care and new drug development, as mutual interference between drugs can lead to adverse reactions. Recently, computational methods have been widely employed for predicting DDIs. However, these approaches often encounter high computational complexity during the process of node feature representation and frequently overlook the interactivity and polysemy of entities. Effectively handling heterogeneous information remains a significant challenge. To cope with these limitations, we propose a dual aggregation and collaborative optimization learning framework，named MRACO，for DDIs prediction based on a multi-relational knowledge graph. MRACO utilizes structural information from these graphs, efficiently learning interactivity information across various relationship types. Additionally, MRACO employs dual aggregation operations to explicitly encode and aggregate multi-type information, capturing diverse semantic relationships of drug nodes. Moreover, MRACO simplifies complex computational processes by utilizing the collaborative loss optimization function, eliminating redundant information and enhancing model robustness. By integrating deep-level interactive information, MRACO leverages rich contextual data from knowledge graphs, enhancing prediction stability. Experiments demonstrate the effective of MRACO feature extraction capabilities in multi-relational networks, highlighting its understanding of the underlying mechanisms of DDIs.
7 June 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Association of intensive blood pressure lowering with health-related quality of life after endovascular thrombectomy: a post-hoc analysis of the ENCHANTED2/MT trial
BACKGROUND AND PURPOSE: Intensive blood pressure (BP) targets of less than 120 mm Hg have been shown to worsen functional outcomes after endovascular thrombectomy (EVT) for acute ischemic stroke. However, their effects on self-reported health-related quality of life (HRQoL) remain uncertain. METHODS: This post-hoc exploratory analysis used data from ENCHANTED2/MT, an open-label, blinded-endpoint randomized trial conducted in 44 Chinese hospitals. Adults with elevated BP within 3 h after successful reperfusion were randomized to intensive (< 120 mmHg) or less intensive (140-180 mmHg) systolic BP targets. HRQoL at 90 days was assessed using the EQ-5D-3 L and EQ visual analogue scale (EQ VAS). Functional outcome was measured by the modified Rankin Scale (mRS). Treatment effects were estimated using adjusted regression models, and causal mediation analysis was performed to explore indirect and direct pathways. RESULTS: Of 816 randomized patients, EQ-5D data were available in 685. At 90 days, both EQ-5D index (0.66 vs. 0.72) and EQ VAS (73.01 vs. 76.84) were lower in the more intensive BP group. Adjusted mean differences favored the less intensive treatment group: 0.06 (95% CI, 0.01 ~ 0.11; P = 0.017) for EQ-5D index and 3.49 (95% CI, 0.38 ~ 6.60; P = 0.028) for EQ VAS. Mediation analysis showed that the association of intensive BP lowering with worse HRQoL was mainly indirect via worse mRS outcomes. CONCLUSIONS: Intensive BP lowering after EVT was associated with modestly worse HRQoL at 90 days. The observed differences were small in magnitude, particularly for EQ VAS relative to commonly reported thresholds for clinical importance. These findings are consistent with less intensive BP targets after EVT and highlight the importance of incorporating patient-reported outcomes in acute stroke trials. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov number, NCT04140110 the Registration Date, 22/10/2019.
7 June 2026
Read appraisal →Environmental monitoring and assessment
YOLO-geochemical integration for real-time aeolian dust monitoring: a critical review of signatures, health risks, and policy implications
Aeolian dust is one of the most pervasive natural air pollutants, strongly influencing atmospheric chemistry, visibility, climate feedback, and human health. This review critically evaluates the integration of geochemical fingerprinting techniques and YOLO-based deep learning frameworks for real-time aeolian dust monitoring, with the overarching objective of advancing source attribution, health risk assessment, and evidence-based policy formulation in dust-prone regions. The Middle East, particularly the Qatar Peninsula, experiences recurrent dust storms that transport mineral particles and trace metals, contributing to degraded air quality, reduced solar energy efficiency, and elevated risks of respiratory and cardiovascular diseases. Traditional monitoring approaches including satellite observations, ground-based measurements, and geochemical analyses such as inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) have improved source apportionment and compositional understanding, yet they often lack real-time detection and high-resolution spatiotemporal coverage. Emerging artificial intelligence (AI) tools, particularly the You Only Look Once (YOLO) deep learning framework, offer transformative opportunities for atmospheric dust monitoring by enabling rapid, automated plume detection across multispectral and hyperspectral datasets. This review demonstrates that ICP-MS, SEM-EDS, and XRD, when applied in combination, provide robust multi-dimensional source fingerprinting: ICP-MS isotopic ratios discriminate crystal from anthropogenic contributions, SEM-EDS resolves particle-level morphology and elemental composition, and XRD quantifies mineralogical assemblages that vary systematically with source region and transport pathway. Concurrently, successive YOLO generations (YOLOv5 through YOLOv10) have achieved real-time dust plume detection with mAP values exceeding 88% on satellite and UAV imagery. The proposed multimodal YOLO-geochemical integration framework, in which YOLO-guided plume detection triggers targeted field sampling and geochemical labels iteratively retrain detection models, enables compositionally resolved, real-time exposure mapping that directly supports inhalation risk assessment against WHO PM2.5 and PM10 thresholds and metal-specific reference concentrations. Collectively, these findings establish a scalable, evidence-based monitoring architecture that strengthens early-warning systems, informs air quality regulation, and advances global strategies to mitigate the environmental and public health burdens of dust storms.
7 June 2026
Read appraisal →Journal of robotic surgery
Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control
Ultrasound is one of the most widely used medical imaging modalities in clinical practice. Robot-assisted ultrasound systems (RAUS) offer significant advantages in reducing physician workload and improving imaging standardization. However, the dynamic and complex nature of the human physiological environment severely hinders the transition of RAUS from automation to full autonomy. This paper presents a narrative review of the core research progress of RAUS between 2021 and early 2026. It comprehensively analyzes the current technological landscape of autonomous robotic ultrasound by focusing on three core pillars: perception, planning, and control. By evaluating cutting-edge breakthroughs and existing limitations across these three domains, this paper identifies the key technical bottlenecks that restrict fully autonomous RAUS. Finally, we propose an evolutionary roadmap driven by multimodal fusion, digital twins, planning-control co-design, and embodied artificial intelligence (AI) architectures. This roadmap provides a developmental reference for autonomous robotic ultrasound examinations and also lays a crucial foundation for future autonomous ultrasound-guided surgical interventions.
1 June 2026
Read appraisal →Neurosurgical review
Bilateral decompressive craniectomy in pediatric patients: A systematic review.
Traumatic brain injury (TBI) and other acute neurological insults remain leading causes of pediatric morbidity and mortality. In cases where elevated intracranial pressure (ICP) is refractory to maximal medical therapy, decompressive craniectomy (DC) may be considered as a salvage intervention. Although bilateral, bifrontal, bitemporal, and cruciate decompressive approaches are used in selected patients with diffuse cerebral edema or bihemispheric swelling, their role, timing, complications, and outcomes remain poorly defined in pediatric populations. Following PRISMA guidelines, PubMed, Scopus, Embase, and PubMed Central were searched for English-language studies reporting bifrontal, bitemporal, bilateral, or cruciate decompressive craniectomy in pediatric or young adult patients, defined a priori as age ≤ 21 years, with refractory intracranial hypertension from traumatic or non-traumatic etiologies. Studies were included only if the cohort met this age criterion or if pediatric/young adult outcomes could be separately extracted. Mixed-age studies without extractable pediatric/young adult data were excluded from the primary synthesis. Data regarding demographics, operative technique, timing, mortality, complications, ICP response, and functional outcomes were extracted and synthesized qualitatively because of clinical and methodological heterogeneity. Five studies met eligibility criteria for the primary pediatric/young adult synthesis, including one small randomized pilot trial and four observational cohorts. The available evidence was heterogeneous with respect to indication, ICP threshold, timing of surgery, operative technique, and outcome measurement. Bitemporal, bifrontal, bilateral, and cruciate decompressive strategies were reported primarily in selected patients with severe traumatic brain injury or diffuse cerebral swelling. Several larger pediatric decompressive craniectomy cohorts included only small bilateral or bifrontal subgroups, and outcomes were not always stratified by technique. Reported mortality, functional recovery, hydrocephalus, subgaleal collections, and need for reoperation varied across studies. Early decompression was associated with ICP reduction and favorable outcomes in limited reports, including one small randomized trial, but the evidence remains insufficient to determine comparative efficacy, optimal timing, or superiority of one bilateral decompressive approach. Bilateral, bifrontal, bitemporal, and cruciate decompressive craniectomy have been reported as feasible salvage strategies for carefully selected pediatric and young adult patients with refractory intracranial hypertension. However, current evidence is sparse, heterogeneous, and often not technique-specific. Therefore, definitive conclusions regarding efficacy, optimal timing, or superiority of one bilateral approach cannot be made. Prospective multicenter studies with standardized age definitions, ICP thresholds, operative taxonomy, complication reporting, and long-term neurocognitive outcomes are needed.
1 June 2026
Read appraisal →BMC health services research
Does an app make patients happy? Impact of a novel medical history app on patient satisfaction in urgent care consultations in Germany: cluster-randomized interventional trial 'DASI'.
BACKGROUND: Patient satisfaction is an important indicator of healthcare quality. However, time constraints in primary care limit effective communication and history-taking. Digital medical history systems have the potential to improve the quality of medical consultations by enabling patient preparation and providing physicians with comprehensive pre-consultation information. This study aimed to evaluate the impact of a novel medical history-taking app (DASI app) on patient satisfaction in out-of-hours practices (OOHP) in Germany. METHODS: We conducted a two-center, cluster-randomized trial over 12 months. Within each practice, weeks were randomized to either an intervention or control group, resulting in a cluster-randomized trial (CRT) with clustering in weeks within the same practice. Patients either used the DASI app before consultations (intervention group) or received standard care (control group). The DASI app is a patient-facing tool that guides patients through a dynamic questionnaire adapted to the selected complaints and previous answers. Patient satisfaction was measured using 17 items from the EUROPEP instrument covering relation and communication, medical care, and information and support. Additionally, we collected sociodemographic data. Statistical analyses included Mann-Whitney U tests for individual items and t-tests for domain scores. Analyses were performed with R, version 4.5.2. RESULTS: Among 1,460 approached patients, 1,040 (71%) were enrolled and 1,034 included in analyses. Patient median age was 31 years, with 60% female participants. Patients in the intervention group (n = 496) showed significantly better ratings in 12 of 17 EUROPEP items compared to controls (n = 538) and demonstrated significantly higher satisfaction across all three EUROPEP domains. Between 78 and 96% of patients rated care as excellent or very good, with intervention group patients more likely to select top-level evaluations in 9 of 17 items. CONCLUSIONS: Digital medical history taking significantly enhanced patient satisfaction in urgent care settings. The app likely improved satisfaction through multiple pathways including increased patient empowerment, better consultation preparation, and more efficient physician-patient interactions. These findings demonstrate that user-friendly digital tools can meaningfully enhance patient experience without disrupting existing workflows, supporting healthcare digitalization efforts while maintaining high-quality patient-centered care. TRAIL REGISTRATION: German register for clinical trials (DRKS00026659; date of registration: 2021-11-03).
1 June 2026
Read appraisal →BMJ paediatrics open
The efficacy of oral sucrose for relieving lumbar puncture pain in neonates: a randomised clinical trial
OBJECTIVE: To evaluate the analgesic efficacy and physiological effects of oral sucrose during lumbar puncture (LP) in term neonates. STUDY DESIGN: This double-blind, randomised trial enrolled 60 full-term neonates undergoing LP. Participants were randomly given 2 mL of 25% oral sucrose or sterile water 2 min before the procedure. The primary outcome was pain score change from baseline via the Neonatal Infant Pain Scale (NIPS). Secondary outcomes included changes in heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO₂). Measurements were taken at baseline, 1 min, and 3 min post-LP. RESULTS: The sucrose group showed significantly smaller changes in NIPS scores at 1 min (1.0 vs 5.0, p<0.001) and 3 min (0.0 vs 4.0, p<0.001), along with smaller HR increases (p=0.012) and SpO₂ decreases (p=0.023) at 1 min post-procedure. Clinically significant tachycardia (HR >160 bpm) occurred in 40% of controls vs 13.3% of sucrose-treated infants (p=0.045), and desaturation (SpO₂ <90%) in 26.7% of controls vs 6.7% of the sucrose group (p=0.038). CONCLUSION: A 2 mL dose of 25% oral sucrose administered 2 min prior to LP is a safe and effective intervention that significantly reduces behavioural and physiological markers of pain in term neonates during the immediate post-procedural period.
1 June 2026
Read appraisal →PloS one
Towards intelligent railway monitoring: A novel hybrid deep learning architecture for railway obstacle detection.
Railways are among the most efficient modes of transportation, capable of moving large quantities of goods and passengers over long distances at relatively low cost. However, accidents frequently occur due to objects or individuals present on the tracks, as trains are unable to swerve and require long braking distances. While the localization of objects within the track bed is a well-explored topic, the reliable and high-performance classification of such obstacles across all relevant categories remains an unresolved challenge. This study proposes an innovative hybrid architecture that leverages the specific visual characteristics of track bed imagery, setting a new benchmark in this domain. The hybrid design effectively leverages the strengths of ResNet50 and Swin Transformer V2, allowing the model to capture both local and global features. In addition, an Efficient Attention Module is integrated to further emphasize the most relevant features for robust obstacle classification. Using stratified five-fold cross-validation on a dataset of 2,003 images across six classes (iron bar, boulder, person, branch, canister, and barrel), the model achieved an average balanced accuracy of 99.46%. The results have implications for accident prevention, improving operational efficiency, and modernizing railway safety systems, thereby enabling the future application of automatic railway surveillance systems to ultimately enhance operational security.
1 June 2026
Read appraisal →PLoS medicine
Characterization of the VHH-Fc construct rimteravimab in healthy adults and patients hospitalized for mild-to-moderate COVID-19: Two Phase 1 randomized clinical trials
BACKGROUND: Variable Heavy domain of Heavy chains (VHH) are innovative tools to target unique epitopes, yet few have been developed as heavy chain-only antibodies for clinical use. Rimteravimab (referred to here as XVR011) is a humanized antibody developed for the treatment of mild-to-moderate coronavirus disease 2019 (COVID-19), consisting of two identical VHHs targeting the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, with a human immunoglobulin (Ig) G1 fragment constant of antibody (Fc), silenced for Fc effector functions. We conducted two Phase 1 studies in healthy volunteers or hospitalized COVID-19 patients to evaluate its safety, tolerability, pharmacokinetics and immunogenicity. METHODS AND FINDINGS: A randomized, double-blinded, single-center, placebo-controlled, single ascending dose study was performed in healthy volunteers (Phase 1a, EXEVIR0102, EudraCT 2021-003707-17), in parallel to an open-label, multi-center, single ascending dose study in patients hospitalized for mild to moderate COVID-19 (Phase 1b, EXEVIR0101, EudraCT 2020-005299-36, NCT04884295). Participants received a single intravenous infusion of 250, 500 or 1,000 mg of XVR011. The primary objective for both trials was the safety and tolerability of XVR011. Pharmacokinetics were evaluated as a secondary objective in Phase 1a and as an exploratory objective in Phase 1b. Efficacy (evaluated as respiratory parameters and COVID-19 clinical status) and antiviral activity in patients were evaluated as a secondary objective in Phase 1b. Immunogenicity was evaluated as an exploratory objective. Part 2 of the EXEVIR0101 study (initially a phase 1b/2 study) was not conducted due to the loss of XVR011 potency against SARS-CoV-2 Omicron BA.2. Demographics, safety, efficacy, and immunogenicity were analyzed using descriptive statistics, while pharmacokinetics were analyzed with noncompartmental pharmacokinetics (PK) modeling. In the Phase 1a study, there were no infusion-related reactions, serious treatment-emergent adverse events (TEAEs) or TEAEs grade ≥3. 22/30 volunteers (73.3%) reported 53 TEAEs (49 Grade 1, 4 Grade 2) with none being related to XVR011. The most common TEAE was headache (n = 8, 26.7%) in various treatment groups. In the Phase 1b study, 27 hospitalized patients were enrolled, and followed up to 30 days. Seven patients (25.9%) reported a total of 15 TEAEs, the majority (80%) being mild to moderate (Grade 1-2). There were no treatment-related serious TEAEs. All TEAEs resolved by the end of the study. Peak exposure (maximal concentration, Cmax) and systemic exposure (area under the curve, AUC0-t, and AUC0-inf) for XVR011 increased dose-proportionally. Geomean half-life ranged from 15.4 to 17.0 days in Phase 1a, while individual half-life ranged from 11.4 to 15.6 days in Phase 1b. SARS-CoV-2 viral load, as detected in nasopharyngeal samples by reverse transcription and quantitative polymerase chain reaction (RT-qPCR), decreased similarly in all cohorts compared to baseline. No treatment-induced anti-drug antibodies (ADA) were detected in Phase 1a. In Phase 1b, higher XVR011 concentrations increased the likelihood of ADA formation, without impacting pharmacokinetics and pharmacodynamics. No obvious dose-response in COVID-19 clinical status or respiratory parameters was observed. Technological limitations included study size, absence of placebo for the Phase 1b, absence of repeated dosing, evolving SARS-CoV-2 variants and standard-of-care. CONCLUSIONS: XVR011 displayed a favourable safety, tolerability, pharmacokinetics, and immunogenicity profile, both in healthy volunteers and in patients hospitalized for mild to moderate COVID-19. These data pave the way for the design and clinical development of VHH-Fc constructs.
31 May 2026
Read appraisal →JMIR formative research
Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study
BACKGROUND: Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients. OBJECTIVE: This formative study aimed to develop and validate an ML-based model for predicting 30-day ED revisits using electronic health records from a tertiary hospital serving a medically underserved area in South Korea and to evaluate its clinical utility through interpretability analysis and risk stratification. METHODS: This retrospective cohort study analyzed 36,230 adult patients visiting the Gangneung Asan Hospital ED in 2023. We developed and compared 3 ML models (extreme gradient boosting [XGBoost], random forest, and ElasticNet) using electronic health records. Model interpretability was ensured through Shapley additive explanations (SHAP) analysis, and clinical utility was evaluated through 5-tier risk stratification. RESULTS: Among 36,230 patients, 798 (2.2%) revisited within 30 days. XGBoost achieved superior performance with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.88-0.92), a sensitivity of 0.94, and a specificity of 0.69. The SHAP analysis identified ED length of stay, oxygen saturation, systolic blood pressure, computed tomography performance, antibiotic use, and liver disease as key predictors. Risk stratification demonstrated a 25-fold difference in the actual revisit rates between the lowest (152/8450, 1.8%) and the highest (686/1500, 45.7%) risk groups. CONCLUSIONS: The XGBoost model demonstrated excellent predictive performance with high interpretability for 30-day ED revisit predictions. The implementation of this model could enable risk-stratified interventions and more efficient resource allocation in medically underserved settings, potentially reducing unnecessary revisits and improving patient outcomes. This formative study establishes feasibility and provides a foundation for future multicenter validation studies in similar medically underserved settings.
30 May 2026
Read appraisal →BMJ global health
Systematic review and meta-analysis of interventions for mental health in extreme weather events
INTRODUCTION: Climate change leading to extreme weather events (EWEs) poses critical threat to human health, including mental well-being. As the effects of climate change are becoming more pronounced, there is a need to identify effective interventions to mitigate the mental health impacts. METHODS: The aim of this review was to synthesise the existing literature on interventions to prevent and manage mental health symptoms in people affected by EWEs. We conducted a comprehensive literature search in MEDLINE, EBSCO CINAHL and Cochrane till 7 February 2024 and in Web of Science and EMBASE till 20 February 2024. Randomised controlled trials (RCTs), quasi-experimental studies and before-after study designs were included. Our search yielded 11 083 potentially relevant papers, which were screened by titles and full texts and 33 studies were finally included in the review. We recorded the study, participants, intervention, disaster and other relevant data on Excel in duplicate. We conducted a meta-analysis separately for intervention-control and before-after studies using Review Manager 5.4. The Cochrane Risk of Bias tool II was used to assess risk of bias in RCTs and the risk of bias in non-randomised studies of interventions tool for quasi-experimental and before-after designs. Main outcomes were symptoms of post-traumatic stress disorder (PTSD), depression, anxiety, stress, general functioning and well-being. RESULTS: There was significant improvement in mental health symptoms following implementation of various interventions, leading to a decrease in PTSD symptoms (standardised mean difference (SMD) -2.92, 95% CI (-4.53 to -1.31), six intervention-control studies and SMD -0.71, 95% CI (-0.95 to -0.48), five before-after studies), depression symptoms (SMD -0.28, 95% CI (-0.53 to -0.03), three intervention-control studies and SMD -1.00, 95% CI (-1.41 to -0.59), 11 before-after studies), anxiety symptoms (SMD -0.38, 95% CI (-0.69 to -0.06), two studies and SMD -0.35, 95% CI (-0.47 to -0.23), eight before-after studies), stress symptoms (SMD -0.82, 95% CI (-1.10 to -0.53), two before-after studies), general functioning impairment (SMD -1.22, 95% CI (-1.73 to -0.70), three before-after studies) and an improvement in well-being (SMD 0.29, 95% CI (0.15 to 0.42), three intervention-control studies and SMD 0.67, 95% CI (0.25 to 1.09), two before-after studies). CONCLUSION: There are existing interventions to improve mental health after an EWE, and these should be integrated into all relevant disaster management plans.
30 May 2026
Read appraisal →PloS one
Risk assessment of civil aviation cabin safety incidents based on the CNN-LSTM-Attention model
Against the backdrop of the thriving global civil aviation industry, cabin safety, which is a crucial link in civil aviation safety systems, has prompted an increasingly urgent need for intelligent risk management. This study constructs an integrated framework of "deep text mining-intelligent risk assessment" and proposes a hybrid CNN-LSTM-Attention model for cabin safety incident classification. Using 8,280 global cabin abnormal event reports from 2004-2024, the model achieves 95.01% accuracy and an F1 score of 94.17% on the test set, substantially outperforming benchmark approaches. Compared with XGBoost (89.45% accuracy, 88.98% F1), the proposed model improves accuracy by 5.56% and the F1 score by 5.19%, demonstrating superior capability in extracting deep semantic features and identifying key risk patterns. The framework also establishes a "text feature-risk mechanism-hazard level" mapping system, enabling more interpretable risk quantification. The findings provide an effective technical path for intelligent early warning of cabin safety risks and offer methodological support for data-driven decision-making in civil aviation safety management.
30 May 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacotherapy for post traumatic stress disorder (PTSD)
BACKGROUND: Posttraumatic stress disorder (PTSD) is a prevalent and disabling disorder. Evidence that PTSD is characterised by specific psychobiological dysfunctions has contributed to a growing interest in the use of medication in its treatment. OBJECTIVES: To assess the effects of medication for reducing PTSD symptoms in adults with PTSD. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; Issue 11, November 2020); MEDLINE (1946-), Embase (1974-), PsycINFO (1967-) and PTSDPubs (all available years) either directly or via the Cochrane Common Mental Disorders Controlled Trials Register (CCMDCTR). We also searched international trial registers. The date of the latest search was 13 November 2020. SELECTION CRITERIA: All randomised controlled trials (RCTs) of pharmacotherapy for adults with PTSD. DATA COLLECTION AND ANALYSIS: Three review authors (TW, JI, and NP) independently assessed RCTs for inclusion in the review, collated trial data, and assessed trial quality. We contacted investigators to obtain missing data. We stratified summary statistics by medication class, and by medication agent for all medications. We calculated dichotomous and continuous measures using a random-effects model, and assessed heterogeneity. MAIN RESULTS: We include 66 RCTs in the review (range: 13 days to 28 weeks; 7442 participants; age range 18 to 85 years) and 54 in the meta-analysis. For the primary outcome of treatment response, we found evidence of beneficial effect for selective serotonin reuptake inhibitors (SSRIs) compared with placebo (risk ratio (RR) 0.66, 95% confidence interval (CI) 0.59 to 0.74; 8 studies, 1078 participants), which improved PTSD symptoms in 58% of SSRI participants compared with 35% of placebo participants, based on moderate-certainty evidence. For this outcome we also found evidence of beneficial effect for the noradrenergic and specific serotonergic antidepressant (NaSSA) mirtazapine: (RR 0.45, 95% CI 0.22 to 0.94; 1 study, 26 participants) in 65% of people on mirtazapine compared with 22% of placebo participants, and for the tricyclic antidepressant (TCA) amitriptyline (RR 0.60, 95% CI 0.38 to 0.96; 1 study, 40 participants) in 50% of amitriptyline participants compared with 17% of placebo participants, which improved PTSD symptoms. These outcomes are based on low-certainty evidence. There was however no evidence of beneficial effect for the number of participants who improved with the antipsychotics (RR 0.51, 95% CI 0.16 to 1.67; 2 studies, 43 participants) compared to placebo, based on very low-certainty evidence. For the outcome of treatment withdrawal, we found evidence of a harm for the individual SSRI agents compared with placebo (RR 1.41, 95% CI 1.07 to 1.87; 14 studies, 2399 participants). Withdrawals were also higher for the separate SSRI paroxetine group compared to the placebo group (RR 1.55, 95% CI 1.05 to 2.29; 5 studies, 1101 participants). Nonetheless, the absolute proportion of individuals dropping out from treatment due to adverse events in the SSRI groups was low (9%), based on moderate-certainty evidence. For the rest of the medications compared to placebo, we did not find evidence of harm for individuals dropping out from treatment due to adverse events. AUTHORS' CONCLUSIONS: The findings of this review support the conclusion that SSRIs improve PTSD symptoms; they are first-line agents for the pharmacotherapy of PTSD, based on moderate-certainty evidence. The NaSSA mirtazapine and the TCA amitriptyline may also improve PTSD symptoms, but this is based on low-certainty evidence. In addition, we found no evidence of benefit for the number of participants who improved following treatment with the antipsychotic group compared to placebo, based on very low-certainty evidence. There remain important gaps in the evidence base, and a continued need for more effective agents in the management of PTSD.
30 May 2026
Read appraisal →Journal of medical Internet research
Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis
BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F1-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.
29 May 2026
Read appraisal →European journal of pediatrics
Pentoxifylline in children with acute encephalitis syndrome: a randomized control trial
Because of the excessive endogenous inflammatory mediators, acute encephalitis syndrome (AES) results in death, or sequelae in survivors. Pentoxifylline, a modulator of inflammation, can have beneficial role in AES. The primary objective was to assess the efficacy of pentoxifylline in terms of time taken for improvement in Glasgow coma scale (GCS) from baseline, in children with AES. The secondary objectives were adverse effects of pentoxifylline and frequency of sequelae among the survivors. This open-label randomized controlled trial (RCT) was conducted in a tertiary care teaching institution for 2 years (January 2022 to December 2023). Children aged 1 month to 14 years with a clinical diagnosis of AES were randomized to receive either standard treatment along with pentoxifylline or standard treatment alone. The data were recorded in case record forms. Forty-four children were randomized (median age, 77 months; male 72.7%). There was no significant difference in the time (median, IQR) taken for improvement of Glasgow coma scale (GCS) by 3 points or normalization between the pentoxifylline group [26 (19-50) hours] and control group [28 (16-52) hours] (p = 0.92). Other outcomes like requirement of mechanical ventilation, mortality, and sequalae were not significantly different between the two groups. The adverse event rate was also not different. Conclusion: Adjunctive pentoxifylline did not provide significant clinical benefit in children with AES, although it was safe and well tolerated. Larger, adequately powered multicenter trials with etiological stratification and biomarker integration are needed to identify potential subgroup benefits. What is Known: • Acute encephalitis syndrome (AES) is associated with high mortality and significant neurological sequelae, largely driven by host-mediated inflammatory responses. • No immunomodulatory adjunct therapy has yet shown proven benefit in pediatric AES. What is New: • Adjunctive pentoxifylline did not significantly improve neurological recovery or clinical outcomes in children with AES. • Pentoxifylline was safe and well tolerated, supporting further evaluation in larger, etiology-stratified trials.
29 May 2026
Read appraisal →Psychological medicine
Effectiveness of 13-week dialectical behavior therapy skills training for non-suicidal self-injury: a multi-center randomized clinical trial in China
BACKGROUND: Dialectical behavior therapy skills training (DBT-ST) has received empirical evidence for treating non-suicidal self-injury (NSSI). However, its effectiveness in Chinese contexts remains understudied. This study aims to explore the effectiveness of DBT-ST for NSSI in a clinical sample of individuals with NSSI. METHODS: We conducted a multicenter, randomized clinical trial in China to evaluate the effectiveness of a 13-week DBT-ST compared with social support group therapy (SSGT). A total of 196 individuals with mental disorders and NSSI were recruited. The primary outcome was the frequency of NSSI thoughts and behaviors, assessed using the Ottawa Self-Injury Inventory. RESULTS: Participants were randomized to the DBT-ST group (n = 97) or the SSGT group (n = 99). DBT-ST was significantly more effective than SSGT in reducing the frequency of NSSI behaviors. It also showed superior effects on borderline symptoms and multiple domains of psychological functioning, including emotion dysregulation, impulsivity, alexithymia, mindfulness, psychological resilience, and emotional intelligence. Both groups exhibited significant reductions in the frequency of NSSI thoughts, suicidal ideation and attempts, and depression, with no significant between-group differences in these outcomes. CONCLUSIONS: These findings confirm that the 13-week DBT-ST intervention is an effective treatment for Chinese patients with NSSI in clinical settings.
29 May 2026
Read appraisal →JMIR research protocols
An AI-Enabled Predictive Harm Response Management Algorithmic Tool to Reduce Adverse Events in Health Care Settings (PreHaRM): Protocol for a Three-Phase Model Development and Pilot-Testing Study
BACKGROUND: Current data analysis and coordination methods do not effectively support nurses and midwives in risk reduction, as retrospective reporting does not allow real-time insights and precludes proactive, preventive care. Analysis of administrative data within Australia's health care sector to predict risk may help address this shortcoming. Predictive analytics can transform these data into meaningful insights, identifying harm risk profiles that benefit the performance of Australian and international clinical programs. Importantly, these tools may support nurses and midwives in preventing adverse events and predicting high-risk situations. Researchers, in collaboration with local health network staff, will develop a proof-of-concept predictive risk algorithm. The "predictive harm response management algorithmic tool to reduce adverse events in healthcare settings" program (project DHCRC-0156) will provide real-time insights via an interactive dashboard, enabling nurses, midwives, and health administration to assess risks and optimize resources in health care settings. This protocol details the algorithm development activities for subproject 1a, predictive risk model development, which aims to develop and pilot-test a predictive harm algorithm across 2 South Australian local health networks. OBJECTIVE: This study aims to identify the clinical harm outcome of interest and relevant data sources per site and build a suitable data solution to model predictors of harm risk and identify actionable clinical, workforce, and environmental factors that affect the harm outcome of interest. METHODS: This study design includes three phases: (1) model generation, (2) model evaluation, and (3) prototype development. Data linkage by SA-NT DataLink can only proceed following approval from each of the following: the South Australia Department for Health and Wellbeing Human Research Ethics Committee, the University of South Australia Human Research Ethics Committee, and hospital governance committees. The clinical dataset will be split into a training set, a validation set, and a test set. Exploratory data analysis will be undertaken to ascertain features and classify outcomes from the raw dataset. Derived features will be computed, feature correlations will be estimated, and initial feature selection will be performed. Iterative model development will occur over 3 stages, and a dashboard to display these results will be developed. RESULTS: The study commenced on July 19, 2021, and will conclude on December 31, 2025. Finalized results are expected in December 2025. CONCLUSIONS: This research will conclude with the development of the algorithm for transferability to health care environments. Research activities will be detailed in publicly available reports and manuscripts prepared for peer-reviewed journals that will be drafted in accordance with existing and appropriate checklists. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75474.
29 May 2026
Read appraisal →BMJ health & care informatics
Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury
OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progression. This study developed a machine learning (ML) model to predict membership in a high-risk longitudinal estimated glomerular filtration rate (eGFR) trajectory associated with subsequent kidney replacement therapy (KRT) initiation. METHODS: We conducted a cohort study of 88 632 adults with ≥2 post-discharge eGFR measurements between 2010 and 2017. Joint latent class mixed models were used to identify dynamic eGFR trajectories from 665 499 measurements. Patient-level data splitting (80/20) was strictly enforced. Extreme gradient boosting (XGBoost) and random forest models were trained using clinical features within 3 months after discharge to predict membership in the highest-risk eGFR trajectory class. RESULTS: Three eGFR trajectories were identified: Steady Low (high-risk phenotype), Slow Decline and Recovery. The Steady Low group showed the strongest association with KRT. The 10-variable XGBoost model demonstrated excellent discrimination (AUROC 0.974, sensitivity 0.913, specificity 0.919). The eGFR slope from discharge to 3 months was the most influential predictor of high-risk progression. DISCUSSION: Although the study was limited to a single healthcare system, this ML framework identified patients at risk of rapid kidney function decline using routinely available clinical data. The model supports early risk stratification after AKI and may enable more timely kidney-protective interventions. External validation, calibration assessment and benchmarking against simpler eGFR-based rules remain important next steps. CONCLUSION: A 10-variable XGBoost model accurately identifies patients with deteriorative eGFR trajectories (Steady Low) early after discharge, providing a practical framework for personalised post-AKI kidney care.
29 May 2026
Read appraisal →BMJ open
Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality prediction, overtreatment and delayed palliative care. Existing prediction models are limited and lack applicability to Chinese elderly patients with advanced COPD. Given the heavy disease burden and limited palliative care in China, we designed this multicentre cohort study to develop a 6-month mortality prediction model for elderly patients with advanced COPD to aid risk stratification, timely palliative care and efficient healthcare resource allocation. METHODS AND ANALYSIS: Patient recruitment has been ongoing since May 2024 and will be completed by December 2026, with a 12-month follow-up to be completed by December 2027. Eligible patients are being enrolled, and multidimensional baseline data including demographic characteristics, clinical indicators, laboratory results, comprehensive geriatric assessment and COPD-specific prognostic factors are being systematically collected. All participants will receive 12 months of standardised follow-up (monthly for the first 6 months and quarterly thereafter) to monitor 6-month all-cause mortality (primary outcome), as well as survival duration, end-of-life healthcare utilisation and do-not-resuscitate status (secondary outcomes). After completion of data collection, we will employ multiple machine learning algorithms to develop and internally validate a 6-month mortality prediction model with pre-specified centres reserved for external validation. Model performance will be evaluated by discrimination and calibration and head-to-head comparisons with the Body Mass Index, Airflow Obstruction, Dyspnoea and Exercise Capacity (BODE) and Age, Dyspnoea and Airflow Obstruction (ADO) indices will be conducted to verify its clinical value. The findings will provide a China-specific prediction tool for elderly patients with advanced COPD to guide clinical intervention, palliative care referral and healthcare resource allocation. ETHICS AND DISSEMINATION: This study was approved by the Biomedical Ethics Review Committee of West China Hospital, Sichuan University (No. 2024-2662) and registered at ChiCTR2500100351. Informed consent is being obtained from all participants. Results will be published in peer-reviewed journals and presented at academic conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500100351.
29 May 2026
Read appraisal →JAMA
Fast Antimicrobial Susceptibility Testing for Gram-Negative Bacteremia: The FAST Randomized Clinical Trial
IMPORTANCE: Novel blood culture diagnostics provide rapid antimicrobial susceptibility testing (AST) results for bacteria causing bloodstream infections (BSIs) but have unclear clinical impact. OBJECTIVE: To evaluate clinical outcomes of blood culture testing using a rapid AST method compared with standard AST in patients with BSIs caused by gram-negative bacilli in regions with high prevalence of antimicrobial resistance. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial that enrolled participants from December 2023 to May 2025 (final follow-up, June 18, 2025) at 7 medical centers in Greece (n = 2), India (n = 1), Israel (n = 3), and Spain (n = 1). Hospitalized patients (adults and children) with BSIs caused by gram-negative bacilli were eligible. Statistical analysis was conducted from August 2025 to January 2026. INTERVENTION: Patients were randomized to undergo blood culture evaluation using rapid, phenotypic AST directly from positive blood cultures plus standard susceptibility testing (n = 413) vs standard susceptibility testing alone (n = 437). Local antimicrobial stewardship teams reviewed all patients and provided treatment recommendations. MAIN OUTCOMES AND MEASURES: The primary outcome was a desirability of outcome ranking (DOOR) at day 30, with 3 categories (alive without deleterious events, alive with deleterious events, and death). The difference between rapid and standard testing was summarized as the probability that the DOOR outcomes were more desirable in the rapid testing group. Superiority was concluded if the lower limit of the 95% CI exceeded 50%. Secondary outcomes included 30-day mortality, length of hospitalization, intensive care unit admission, acquisition of hospital-acquired infections, time to effective antibiotic therapy within 3 days, and time to antibiotic escalation or deescalation within 3 days. RESULTS: Of the 899 patients randomized, 850 were included in the primary outcome analysis (median [IQR] age, 72 [21] years; 43% female). The probability that DOOR outcomes were more favorable in the rapid testing group was 48.8% (95% CI, 45.3%-52.4%). Median time to effective antibiotic therapy was not different between the groups in the as-randomized population. Median time to antibiotic escalation or deescalation was faster in the rapid testing group by 14 hours (95% CI, 6-22) in the as-randomized population. There were no differences between the groups in other secondary outcomes. In the prespecified subgroup with carbapenem-resistant infections, median time to effective therapy was 9.5 hours in the rapid testing group vs 28 hours in the standard testing group (difference, -18 hours [95% CI, -42 to 6]). CONCLUSIONS AND RELEVANCE: Among patients with gram-negative bacilli BSIs, rapid blood culture AST was not superior to standard testing by DOOR. When considered with other efficacy and safety outcomes, these findings may help inform the use of rapid susceptibility testing in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06174649.
29 May 2026
Read appraisal →The New England journal of medicine
Ultrasound-Facilitated, Catheter-Directed Fibrinolysis for Acute Pulmonary Embolism
BACKGROUND: Whether anticoagulation alone is an adequate treatment for acute, intermediate-risk pulmonary embolism is uncertain. METHODS: We conducted a multinational, adaptive-design trial with blinded outcome adjudication. Patients with intermediate-risk pulmonary embolism (with a ratio of right ventricular end-diastolic diameter to left ventricular end-diastolic diameter of ≥1.0 and an elevated troponin level) were eligible if they had at least two indicators of cardiorespiratory distress (systolic blood pressure of ≤110 mm Hg, a heart rate of ≥100 beats per minute, or a respiratory rate of >20 breaths per minute). Patients were randomly assigned to undergo ultrasound-facilitated, catheter-directed fibrinolysis with alteplase plus anticoagulation (the intervention group) or anticoagulation alone (the control group) according to prespecified treatment protocols. The primary outcome was a composite of pulmonary embolism-related death, cardiorespiratory decompensation or collapse, or symptomatic recurrence of pulmonary embolism within 7 days. RESULTS: The intention-to-treat population comprised 544 patients: 273 in the intervention group and 271 in the control group. The mean (±SD) age was 58.2±13.5 years, and 42.6% of the patients were women. A primary-outcome event occurred in 11 patients (4.0%; 95% confidence interval [CI], 2.3 to 7.1) in the intervention group and 28 (10.3%; 95% CI, 7.2 to 14.5) in the control group (relative risk, 0.39; 95% CI, 0.20 to 0.77; P = 0.005). The effect was driven primarily by a lower risk of cardiorespiratory decompensation or collapse in the intervention group. Major bleeding occurred within 7 days after randomization in 11 patients (4.1%) in the intervention group and 6 (2.2%) in the control group (P = 0.32); major bleeding occurred within 30 days in 11 patients (4.1%) and 8 patients (3.0%), respectively (P = 0.64). No substantial between-group differences in the incidence of other serious adverse events were observed up to 30 days after randomization; no intracranial hemorrhage occurred. CONCLUSIONS: In patients with acute, intermediate-risk pulmonary embolism, ultrasound-facilitated, catheter-directed fibrinolysis plus anticoagulation led to a lower risk of the composite of pulmonary embolism-related death, cardiopulmonary decompensation or collapse, or symptomatic recurrence of pulmonary embolism within 7 days than anticoagulation alone. (Funded by Boston Scientific; HI-PEITHO ClinicalTrials.gov number, NCT04790370.).
29 May 2026
Read appraisal →BMJ open
Does a protocolised oxygenation target impact clinical outcomes in mechanically ventilated patients in the intensive care unit across the 10 academic and community hospitals in the UCHealth system? Protocol and statistical analysis plan for an EHR-embedded pragmatic stepped-wedge randomised controlled trial.
INTRODUCTION: We designed the Optimising Care in Critically Ill at the UCHealth by Liberalising the Target O₂ in Mechanically ventilated intensive care unit (ICU) Patients to determine the effectiveness of a multimodal educational and electronic health record order panel intervention in limiting occult hypoxaemia and hyperoxaemia in patients receiving invasive mechanical ventilation by targeting a prespecified oxygen saturation (SpO₂ 90%-96%) range. METHODS AND ANALYSIS: This trial is a pragmatic electronic health record-embedded, multisite, cluster-randomised, stepped-wedge implementation of a multimodal educational and electronic health record order panel intervention aimed at achieving a standardised intermediate target range of SpO₂ (90%-96%) or partial pressure of oxygen (PaO₂ 60-100 mm Hg) in mechanically ventilated adult patients admitted to the ICU across a 10 hospital health system that includes a large academic centre and smaller community hospitals.The primary endpoint is ventilator-free days to day 30, defined as the number of days alive and not receiving support through invasive mechanical ventilation following the first initiation of invasive mechanical ventilation in a participating ICU during the hospitalisation. Secondary outcomes include a variety of clinical endpoints: hospital-free days to day 30, all-cause mortality to day 90, need for supplemental oxygen at discharge and incidence of occult hypoxaemia and hypoxaemia. We will analyse primary and secondary endpoints using a mixed effects modelling framework, with specific distributions chosen depending on the type of outcome (eg, binary, count, ordinal, time-to-event). ETHICS AND DISSEMINATION: Research is performed under a waiver of informed consent for minimal-risk research, as approved by Colorado Multiple Institutional Review Board (24-0065). Results will be disseminated in peer-reviewed publications and at national and international conferences. TRIAL REGISTRATION NUMBER: NCT06501118.
28 May 2026
Read appraisal →JMIR research protocols
Reducing Mis-triage in Emergency Departments (RemEDy): Protocol for Improving Triage Accuracy Through Real-time Evaluation and Artificial Intelligence
BACKGROUND: Mis-triage represents a global concern, with reported rates ranging from 15% to 33%. Understanding its causes and contributing factors is essential for ensuring patient safety. Currently, available studies have mainly focused on evaluating triage systems rather than investigating the human factors affecting triage performance. A major limitation in triage evaluation studies is the lack of standardized criteria to assess patient acuity and the absence of a clear consensus on how to measure triage accuracy. Most studies rely on retrospective data, which often fail to capture real-life clinical complexity. Therefore, the underlying causes and consequences of mis-triage remain partially understood. OBJECTIVE: This study aims to improve triage by defining the optimal triage evaluation process and identifying clinician-, patient-, and system-level factors that compromise its accuracy and safety. METHODS: Reducing Mis-Triage in Emergency Departments (RemEDy) will be a 4-phase, mixed methods project conducted across 7 Swiss emergency departments. The first phase will focus on developing a standardized triage evaluation instrument, combining evidence from a scoping review of triage evaluation processes, workshops with triage clinicians using design thinking methodology, and a modified Research and Development-University of California Delphi involving international experts and patient representatives. The second phase will prospectively implement this instrument in real time within a multicenter observational cohort study to evaluate triage performance; quantify mis-triage; and identify predictors at the patient level (eg, demographics), clinician level (eg, training), and system level (eg, crowding and length of stay). The third phase will focus on designing and validating an artificial intelligence-based decision support tool, applying multimodal models that integrate real-time triage data to enhance acuity prediction and minimize human error. The fourth phase will develop and evaluate a targeted training program, guided by the Capability, Opportunity, Motivation, and Behavior model, to strengthen triage accuracy and mitigate cognitive biases. RESULTS: The project was funded by the Swiss National Science Foundation in March 2025 (grant 10004535). At submission, the scoping review is ongoing and expected to be completed in early 2026. Development and piloting of the triage evaluation instrument will take place in 2026. A multicenter cohort study is planned between October 2026 and June 2027. The intervention study is scheduled between October 2027 and December 2028. Final results are expected in 2029. CONCLUSIONS: The RemEDy project addresses key limitations of current triage research, including the lack of standardized evaluation methods. By combining expert and clinician consensus; real-time assessment; and multilevel analysis of patient-, clinician-, and emergency department-level factors, RemEDy is expected to provide a more comprehensive understanding of mis-triage and its causes. RemEDy will establish a novel framework for real-time triage evaluation and inform the development of targeted training programs with the potential to improve triage accuracy, safety, and equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92264.
28 May 2026
Read appraisal →Molecular biology reports
The role of non-coding RNAs in the coronary no-reflow phenomenon: a systematic review
Coronary no-reflow poses a challenge in interventional cardiology. Increasing attention has been directed toward the potential role of non-coding RNAs (ncRNAs) in predicting coronary no-reflow (CNR), with no prior systematic review available to date. We aimed to systematically review the available evidence on the potential clinical utility of ncRNAs in CNR and/or microvascular obstruction (MVO). Embase, MEDLINE Ultimate, PubMed, Scopus, and Web of Science were searched, last on February 9, 2026. Studies enrolling adult patients with CNR and/or MVO that evaluated the expression of ncRNAs or investigated genetic variants within ncRNA-related pathways were included. Risk of bias was not assessed. Upregulation of miR-1, miR-7a/b, miR-98-5p, miR-133a, miR-133b, miR-208a, miR-224-3p, miR-660-5p, long non-coding RNA (lncRNA) TUG1, lncRNA MALAT1, and downregulation of miR-30e, miR-126, miR-155, and miR-542-3p were reported in patients with STEMI with CNR and/or MVO compared with those without CNR/MVO. Almost all ncRNAs were reported by a single study, with the exception of miR-30e, which was consistently reported in three independent studies. Several ncRNAs may demonstrate good performance in identifying patients at risk of CNR and/or MVO occurrence. Nevertheless, the available evidence is scarce and limited by the low robustness of the reported findings. Circulating ncRNAs are promising biomarkers for CNR. Nevertheless, several limitations persist, highlighting the need for further studies with standardized methodology to validate the clinical utility of ncRNAs.
28 May 2026
Read appraisal →European radiology experimental
Who labels best? Radiologists, rules, or large language models for CT reports on pulmonary embolism.
OBJECTIVE: To compare open-weight and proprietary large language models (LLMs), a rule-based extractor (RBE) and radiologists for labelling pulmonary embolism CT reports, and to test whether a hybrid RBE-LLM workflow improves labelling performance. MATERIALS AND METHODS: This single-centre retrospective study included structured CT reports from October 2021 to March 2025. Three labelling pipelines were evaluated: an RBE; a model-agnostic LLM extractor (18 open-weight, four GPT-4 variants); and a hybrid pipeline routing only RBE failures to an LLM. Ground truth was defined at the report-text level by deterministic schema matching for initially RBE-valid fields and blinded adjudication of RBE-invalid fields by two attending radiologists. Eight radiologists provided a human baseline. Outcomes included F1 scores, accuracy, LLM-based salvage of RBE failures, and labelling time. RESULTS: In total, 2,923 reports from 2,923 patients (mean age 66 ± 17 years; 1,465 women) were included. Falcon3-10b and GPT-4.1-mini achieved similar item-level performance (F1 0.98 [95% CI, 0.97-0.98] for both; p = 0.70) and both exceeded the RBE (F1 0.81 [95% CI, 0.80-0.82]; p < 0.001). Salvage of RBE failures was comparable between open-weight and proprietary models (88.1% vs 91.9%; p = 0.12). The hybrid RBE-LLM workflow achieved 99.8% accuracy and F1 0.99 (0.98-0.99), exceeding both the RBE and pooled radiologists (F1 0.92 [95% CI, 0.90-0.93]; all p < 0.001). CONCLUSION: Schema-constrained open-weight and proprietary LLMs exceeded rule-based extraction and, at the upper end of performance, matched a pooled radiologist label-transfer baseline. A rules-first, targeted LLM workflow enabled near-perfect extraction from finalised structured pulmonary embolism CT reports. RELEVANCE STATEMENT: A rules-first LLM workflow can automate high-fidelity extraction of structured CT findings from finalised radiology reports, enabling scalable, auditable, and more consistent cohort curation for clinical research, registries, and quality improvement. KEY POINTS: A hybrid rules-first workflow combining a rule-based extractor (RBE) with targeted large language model (LLM) salvage achieved the highest overall performance for labelling of pulmonary embolism CT reports (F1, 0.99; accuracy, 99.8%). The top standalone open-weight and proprietary LLMs (Falcon3-10b and GPT-4.1-mini) both exceeded the RBE and, at the upper end of performance, matched a pooled radiologist label-transfer baseline. The hybrid workflow reduced cohort-curation time from 32.2 h for radiologists to 1.0 h while reducing LLM calls by 85.6%, because the LLM was only triggered for rule-failed fields.
28 May 2026
Read appraisal →Journal of robotic surgery
Robot-assisted percutaneous pelvic channel screw placement for unstable pelvic ring injuries: a systematic review and meta-analysis with narrative technical comparison of multi-platform differences
Percutaneous pelvic channel screw fixation is a key technique for the minimally invasive treatment of pelvic ring injuries. Robotic-assisted systems aim to improve accuracy and reduce radiation, but direct comparative studies across platforms are lacking. This systematic review and meta-analysis primarily compares robot-assisted versus conventional fluoroscopy-guided pelvic channel screw placement, with a secondary narrative comparison of technical differences across TiRobot, Mazor X, and ROSA platforms. We systematically searched PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang, and CBM databases up to April 2026. Comparative studies of robot-assisted versus conventional fluoroscopy-guided pelvic channel screw placement in adult pelvic ring injury patients were included. Primary outcomes were screw accuracy (Gras classification) and radiation exposure. Secondary outcomes included operative time, functional recovery (Majeed score), complications, and cost. No direct head-to-head studies comparing different robotic platforms for pelvic screw placement were identified; therefore, platform comparisons are primarily narrative and indirect, with spine surgery data presented as hypothesis-generating only. Risk of bias was assessed using ROBINS-I and RoB-2, and evidence was graded using the GRADE system. Fifteen studies (1,247 patients, 2,893 screws) were included. Primary meta-analysis showed that robot-assisted placement significantly improved screw accuracy (excellent/good rate: 98.2% vs. 86.4%; OR = 5.32, 95%CI: 3.18-8.89, p < 0.001; GRADE: Low certainty due to risk of bias) and reduced radiation exposure (SMD=-1.84, 95%CI: -2.43 to -1.25; GRADE: Low certainty due to serious inconsistency). Operative time showed no significant difference in studies by experienced surgeons (MD=-5.2 min, 95%CI: -11.0 to 0.6). Functional outcomes (Majeed score) were comparable between groups (MD = 0.8, 95%CI: -0.7 to 2.3). Overall complications were significantly reduced with robotic assistance (OR = 0.46, 95%CI: 0.30-0.71; GRADE: Low certainty due to risk of bias). For platform comparisons, TiRobot has the strongest pelvic trauma-specific evidence from 11 studies (847 patients). Evidence for Mazor X and ROSA in pelvic trauma is limited to technical descriptions and indirect spine surgery data; no direct pelvic trauma validation is available. Robot-assisted pelvic channel screw placement is superior to conventional methods in accuracy and radiation reduction, with comparable functional outcomes and fewer complications. TiRobot demonstrates the strongest pelvic trauma-specific evidence based on available comparative studies. Mazor X and ROSA require direct pelvic trauma validation before their performance can be reliably compared. TiRobot may have lower acquisition costs in Chinese healthcare settings, but formal cost-effectiveness analyses are lacking for all platforms. Significant evidence gaps remain, including lack of high-quality RCTs, absence of direct platform comparisons in pelvic trauma, and insufficient cost-effectiveness analyses.
28 May 2026
Read appraisal →Neurology
Efficacy of Levetiracetam in Patients With Pediatric Epilepsy: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Levetiracetam (LEV) is widely used in pediatric epilepsies because of its favorable pharmacokinetics, ease of administration, and perceived tolerability. However, its comparative efficacy relative to established antiseizure medications (ASMs) in children remains uncertain. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate LEV efficacy in pediatric epilepsies and compare outcomes vs placebo and active comparators. METHODS: We systematically searched PubMed/MEDLINE and Embase (2000-6 August 2025) for RCTs enrolling patients 16 years or younger with epilepsy and reporting seizure freedom and/or ≥50% responder rate. Trials including both pediatric and adult patients were eligible if pediatric participants were represented. Comparisons included LEV vs placebo or active ASMs as monotherapy or adjunctive therapy. Primary outcomes were seizure freedom and responder rate at the trial's primary endpoint or, if not specified, longest reported follow-up. We assessed risk of bias using Cochrane Risk of Bias 2. We pooled risk differences (RDs) with 95% CIs using random-effects models, stratified by comparator and epilepsy subtype. RESULTS: We included 25 RCTs (4,070 participants): 23 contributed to pooled meta-analyses. Across 25 trials, the mean age ranged from 0.4 to 39.3 years, reflecting pediatric-only and mixed-age RCTs; 43.8% were female. In placebo/no-therapy-controlled trials (mainly add-on studies), LEV was associated with higher seizure freedom (RD 11.0%; 95% CI 5.3%-16.7%) and responder rates (RD 24.3%; 95% CI 19.1%-29.4%). In active-comparator-controlled trials (mainly monotherapy head-to-head studies), LEV showed no overall advantage vs active comparators for seizure freedom (RD -2.4%; 95% CI -5.6% to 0.7%) or responder rate (RD -7.4%; 95% CI -23.0% to 8.1%). Fourteen trials were at high risk of bias. Sensitivity analyses confirmed benefit vs placebo but showed significant disadvantage vs active comparators in low risk-of-bias trials. Findings in pediatric-only trials (16 RCTs; 1,380 participants) were consistent with the overall results. DISCUSSION: LEV confers benefit vs placebo, mostly as adjunctive therapy, but does not consistently outperform established ASMs in pediatric epilepsies and may be inferior in some subgroups when higher-quality evidence is considered. Limitations include substantial heterogeneity, frequent high risk of bias, variable follow-up durations, publication bias, and limited pediatric-only comparative data.
28 May 2026
Read appraisal →Journal of the American College of Cardiology
Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department
BACKGROUND: Routine noncardiac computed tomography (CT) imaging may contain information about cardiovascular risk. Head computed tomography (CTH) is among the most common imaging studies, conducted annually in millions of patients. Its utility for cardiovascular risk assessment has not been studied. OBJECTIVES: The purpose of this study was to develop and validate deep learning models for predicting incident cardiovascular disease (CVD) and estimating coronary artery calcium (CAC) scores from CTH, and assess performance against clinical risk factors. METHODS: This retrospective cohort study used data from the Stanford Health Care Emergency Department from August 2020 to August 2024. The CVD cohort comprised 27,990 adult patients without known CVD who underwent CTH. The CAC cohort included 2,313 patients who underwent both CTH and coronary CT angiography. Imaging features were extracted from CTH using pretrained deep learning models. Other risk factors were extracted from electronic health records. Outcomes were incident CVD complications (myocardial infarction, stroke, heart failure) and CAC scores (0, 1-10, 11-100, 101-400, >400 AU). Performance was evaluated using the concordance index (C-index) and area under the receiver operating characteristic curve, compared against the baseline model using the variables of the American Heart Association PREVENT (Predicting Risk of cardiovascular disease EVENTs) risk model. RESULTS: Four percent (1,110 of 27,990) of patients (median age 63.0 years, 51.7% female) experienced cardiovascular events. The CTH model achieved a C-index of 0.82 (95% CI: 0.78-0.85) compared with PREVENT (0.75; 95% CI: 0.70-0.79) with difference of 0.07 (95% CI: 0.04-0.10). For CAC estimation (n = 2,313, median age 65.0 years, 53.5% female), the CTH+PREVENT model achieved a C-index of 0.76 (95% CI: 0.72-0.80) and area under the receiver operating characteristic curve of 0.80 (95% CI: 0.73-0.85) for CAC >100. Of the patients, 15.7% were reclassified; higher-risk patients were younger but with higher prevalence of vascular calcifications (30.2% vs 24.8%, P = 0.001) and brain infarcts (20.1% vs 5.8%, P < 0.001). CONCLUSIONS: Routine CTH scans complement traditional risk factors for cardiovascular risk stratification, identifying subclinical disease in younger patients with favorable risk profiles. Clinical integration could improve CVD detection and prevention without additional costs or radiation.
28 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Comparative analysis of the improvement in shoulder rotation in complex shoulder fractures treated by reverse shoulder arthroplasty compared to open reduction and internal fixation: a systematic review and meta-analysis
PURPOSE: To compare the clinical and functional outcomes of open reduction and internal fixation (ORIF) and reverse shoulder arthroplasty (RSA) in patients aged ≥ 65 years with complex proximal humeral fractures (PHFs). METHODS: A systematic review and meta-analysis was conducted according to the PRISMA guidelines. PubMed, Web of Science, ScienceDirect, EBSCO, and the Cochrane Library were searched for randomized controlled trials (RCTs) and cohort studies published in English without date restrictions. Eligible studies compared RSA and ORIF in elderly patients with PHFs and reported functional, radiographic, or complication outcomes. Pooled data were analyzed using a random-effects model. RESULTS: Twenty-two studies involving > 34,000 patients were included. RSA was associated with greater forward flexion and a trend toward improved abduction, whereas internal rotation favored ORIF without reaching significance. No significant differences were observed in external rotation. The functional scores (Constant-Murley, Oxford Shoulder Score) were similar, and the complication and reoperation rates did not differ significantly between the groups. CONCLUSION: RSA offers modest advantages in forward flexion compared with ORIF but does not consistently improve overall functional scores or reduce complications. RSA may be preferred in elderly patients with poor bone stock or rotator cuff pathology, whereas ORIF remains appropriate for patients in whom joint preservation is feasible. High-quality RCTs with standardized outcome reporting are needed to clarify the optimal surgical management.
28 May 2026
Read appraisal →European journal of psychotraumatology
Biological and neurocognitive correlates of comorbid post-traumatic stress disorder and alcohol use disorder: a systematic review
Background: Post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.Objective: We conducted a systematic review to synthesize evidence on molecular, genetic, neural, and cognitive mechanisms contributing to comorbid PTSD & AUD.Method: Following PRISMA guidelines, we performed a comprehensive search of five databases using PTSD-related, AUD-related, biological, and neurocognitive terms. Participants had to meet diagnostic criteria for both PTSD and AUD, and studies were required to have a comparator including controls, PTSD only, or AUD only. A critical appraisal was completed for all studies.Results: From 3904 identified papers, 14 met the inclusion criteria. Four studies examined the same molecular marker, with three papers derived from the same cohort, investigating baseline and stress-induced cortisol and adrenocorticotropic hormone, and found no differences unique to PTSD & AUD. One study linked low brain-derived neurotrophic factor and hazardous drinking to PTSD onset over 2 years following hospital admission. Genetic studies showed considerable overlap (72%) between PTSD and AUD in female twins, whereby the DRD2 A1 allele and the absence of the APOE ϵ2 allele were strongly associated with PTSD and drinking. Studies also reported lower neurometabolites, white matter integrity, and hippocampal volume in PTSD & AUD. Critical appraisal of these studies highlighted prominent selection bias (predominantly male and veterans) and limited justification of sample size.Conclusions: These findings suggest that PTSD & AUD may be characterized by distinct neurobiological alterations and genetic vulnerabilities relative to comparator groups. However, as there are currently insufficient data to support or refute these findings, this highlights the need for further research. PTSD and AUD frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.The current study aimed to review the existing molecular, genetic, neural, and cognitive evidence, and suggests similar impacted stress regulation across PTSD-only, AUD-only, and PTSD & AUD, yet distinct neural and genetic substrates among individuals with PTSD & AUD.Future research should include more female participants and a broader range of trauma types (currently dominated by veteran samples), and use large-scale neuroimaging and genetic datasets to help to identify mechanisms underpinning PTSD & AUD.
27 May 2026
Read appraisal →NEJM evidence
Outcomes of Pneumonia in ICUs in Low- and Middle-Income Countries - A Systematic Review
BACKGROUND: Community-acquired pneumonia (CAP) is a leading cause of intensive care unit (ICU) admission and death in low- and middle-income countries (LMICs). We conducted a systematic review and meta-analysis to estimate mortality among patients with CAP admitted to ICUs in LMICs. METHODS: We searched multiple databases using terms related to CAP admitted to the ICU and LMIC. We included both observational studies and clinical trials. The primary outcome was all-cause ICU, 28-day or 30-day mortality. Demographics, comorbidities, clinical characteristics, mechanical ventilation, and length of ICU stay were described. We registered the study in PROSPERO (CRD42022363048). RESULTS: A total of 52 studies from 18 countries met inclusion criteria, encompassing 48,707 patients. Most studies originated in upper-middle-income countries and no studies from low-income countries were identified. The most frequent comorbidities were hypertension, chronic obstructive pulmonary disease, and diabetes. Mechanical ventilation was reported in 36 studies, with median use in 59% (interquartile range, 41.4-77.4) of patients. Pooled mortality was 37% (95% confidence interval [CI], 31-42), rising to 61 (44-75) among patients requiring mechanical ventilation. In a meta-regression, age and mechanical ventilation were the strongest moderators of mortality, explaining 55.2% of heterogeneity in short-term mortality. CONCLUSIONS: Mortality among patients with CAP admitted to ICUs in middle-income countries remains high, especially in older and mechanically ventilated patients. The lack of data from low-income countries is notable.
27 May 2026
Read appraisal →Journal of neurology
Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.
BACKGROUND: Headache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and clinical applicability of artificial intelligence (AI) and machine learning (ML) models for classifying adult headache disorders against clinician diagnoses using the International Classification of Headache Disorders (ICHD) criteria. METHODS: In this systematic review, we searched PubMed, Embase, and the Cochrane Library (January 2015-December 2025) for AI/ML diagnostic headache studies. Two reviewers extracted data and assessed risk of bias using the QUADAS-2 tool with the QUADAS-AI extension. Main outcomes were sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and risk of bias. RESULTS: We included 74 studies encompassing 154,856 participants. Models utilized traditional ML (n = 47), deep learning (n = 18), and hybrid or rule-based approaches (n = 9). Data inputs included neuroimaging (n = 27), multimodal datasets (n = 20), neurophysiological signals (n = 17), and clinical questionnaires (n = 10). Only 4 studies performed independent external validation. Overall sensitivity ranged from 47.5% to 100.0%, specificity from 50.4% to 100.0%, and AUC-ROC from 0.658 to 1.000. Models using structured questionnaires reported realistic accuracies (74-86%), whereas neuroimaging models frequently produced near-perfect, likely overfit estimates. Most studies (65 of 74) exhibited a high risk of bias driven by artificial case-control designs, data leakage, and absent external validation. CONCLUSIONS: AI-based headache diagnostic systems demonstrate promising but highly variable accuracy. Pervasive methodological flaws-specifically severe spectrum bias, data leakage, and a lack of independent external validation-currently preclude clinical implementation. Future studies require prospective, real-world validation to safely integrate these tools into practice.
27 May 2026
Read appraisal →BMC urology
Comparison of suction and standard ureteral access sheath versus sheathless flexible ureteroscopy in management of upper ureteral and renal calculi
PURPOSE: Comparative effectiveness of suction versus traditional ureteral access sheath (UAS) remains an area of ongoing research. We aim to compare the outcomes of retrograde intrarenal surgery (RIRS) with suction and standard UAS versus without UAS in the management of renal and upper ureteric stones, with a specific focus on operative efficiency, stone-free rates, and postoperative complications. METHODS: This study was conducted between October 2023 and January 2025, including 90 patients with renal stones ≤ 3 cm and upper ureteric stones ≤ 1.5 cm. Patients were divided into three groups by random Allocation Software (version 2): Group A1 (standard UAS, n = 30), Group A2 (suction UAS, n = 30), and Group B (no UAS, n = 30). Operative time, vision quality, stone clearance, complications, and hospital stay were assessed. RESULTS: Group A2 had the shortest mean operative time (70.1 ± 10.3 min) with significantly better vision compared to Group A1 (114.3 ± 27.0 min) and Group B (125.0 ± 28.01 min) (p < 0.001). Residual stone rates were lowest in Group A2 (3.3%) compared to Group A1 (10.0%) and Group B (26.7%) (p = 0.035). The need for a second procedure was significantly higher in Group B (26.7%) compared to Group A1 (10.0%) and absent in Group A2 (p = 0.002). Hospital stay was significantly shorter in Group A2 compared to Group A1 and Group B (p = 0.005). Fever incidence (Clavien Grade II) was significantly higher in Group B (23.3%) compared to Group A1 (16.7%) and absent in Group A2 (p = 0.005). CONCLUSION: Suction ureteral access sheaths in flexible ureteroscopic lithotripsy for renal stones ≤ 3 cm and upper ureteric stones ≤ 1.5 cm was associated with improved stone clearance, reduced operative time and hospital stay, provide better endoscopic vision, minimized infectious complications and need for second procedure, compared to standard UAS or no sheath. However, outcomes may be influenced by surgeon experience and familiarity with suction-assisted systems. Prospective multicenter studies with larger sample sizes and varying surgeon experience levels are recommended.
27 May 2026
Read appraisal →Global health action
Integrating GIS and local knowledge for optimised medical supply prepositioning in flood-prone Nigeria: a strategic framework
BACKGROUND: Nigeria faces recurring flood disasters that severely disrupt its healthcare systems and delays medical relief efforts. Current medical supply prepositioning by the National Emergency Management Agency (NEMA) remains inadequate due to infrastructure limitations, funding constraints, and poor coordination. OBJECTIVE: To develop and validate a Strategic Medical Hub Locator Framework that integrates Geographic Information Systems (GIS) analysis with qualitative local knowledge to optimise medical supply prepositioning for flood emergencies in Nigeria. METHODS: We employed a mixed-methods approach, combining qualitative analysis of 22 GIS-based flood risk studies in Nigeria and structured expert survey responses from 34 professionals with flood response experience, including five with direct NEMA warehouse experience. The framework was grounded in Disaster Risk Reduction theory and supported by Protection Motivation Theory and Theory of Planned Behaviour. RESULTS: The framework identified eight strategic medical hub locations: six existing NEMA warehouses (Abuja, Lagos, Kano, Gombe, Maiduguri, Kaduna) with the strongest performance, and two new strategic sites (Akure and Uyo) to address service gaps in South-South and South-East regions. Abuja emerged as the highest-performing hub with a composite score reflecting good centrality, governance capacity, and operational readiness. The framework successfully bridged data silos between spatial flood risk patterns and operational constraints. CONCLUSIONS: Integration of GIS thematic analysis with qualitative local data provides a viable, replicable method for enhancing humanitarian medical logistics in flood-prone regions. The Strategic Medical Hub Locator Framework offers a structured decision-support tool that can inform evidence-based investment in Nigeria's disaster health infrastructure, while being adaptable to other Global South contexts. Key findings: Where primary geospatial data silos are present, GIS analysis can be enhanced by combining it with local qualitative insights, thereby bridging the gap between technical modelling and practitioner realities.Added knowledge: The paper offers a methodological contribution through the design of a Strategic Medical Hub Locator Framework that is replicable in contexts where primary data and complex modelling are not feasible.Global health impact for policy and action: This study provides policymakers and humanitarian actors with a proof-of-concept decision-support tool. The exploratory framework offers a scalable model that can be adapted to other flood-prone settings where primary data and practical knowledge are scattered, thereby strengthening disaster preparedness and health system resilience strategies.
27 May 2026
Read appraisal →JMIR formative research
Testing a Smartphone-Based Intervention Targeting Anxiety Sensitivity Among Women Presenting for Emergency Care After Sexual Assault: Pilot Randomized Controlled Trial
BACKGROUND: Anxiety sensitivity (AS), defined as the fear of anxious arousal, is a promising therapeutic target for reducing posttraumatic stress disorder (PTSD) symptom development after trauma exposure. Initial research suggests that smartphone-based AS interventions may be acceptable to sexual assault survivors at risk for PTSD symptoms and effective for symptom reduction, but only small one-arm proof-of-concept studies have been conducted. OBJECTIVE: The goal of this study was to extend prior proof-of-concept work by conducting a pilot randomized controlled trial. The aims were to evaluate intervention efficacy, AS and PTSD symptom change, the acceptability and credibility of a control intervention, and the feasibility of a larger randomized controlled trial. METHODS: A total of 60 women with high AS presenting for emergency care after sexual assault were recruited and randomized to either the AS intervention or a control condition, and they were followed up with for over 6 months via remote self-report questionnaires. RESULTS: The findings indicated that the study population is at risk and in need of intervention: 88.8% (40/45) and 80.6% (29/36) of women sexual assault survivors in the sample met the criteria for probable PTSD at 7 weeks and 6 months post assault, respectively. Most (16/27, 59.3%) individuals receiving the AS intervention who completed it rated it as acceptable (eg, 18/21, 85.7% reported that the treatment was helpful). Early within-group reductions were not statistically significant, but by month 6, statistically significant reductions in AS and PTSD symptoms were observed in both conditions. Recruitment and retention data supported the feasibility of the study design, although some suggestions were noted for future research (eg, improving intervention and ecological momentary assessment compliance). CONCLUSIONS: This pilot study replicated the proof of concept and acceptability of a novel smartphone-based intervention targeting AS delivered to women sexual assault survivors presenting to emergency care. Intervention completion was in line with or better than traditional therapy but remained a challenge. Our selected control condition demonstrated a larger effect than expected, and we were unable to track its initiation or completion, causing difficulty in drawing conclusions. Overall, the results highlight the need for additional research.
27 May 2026
Read appraisal →Analytical chemistry
Human-to-Rat Validation of PMI Biomarkers: A Bidirectional Cross-Species Metabolomics Study Reveals Asymmetric Translation
Accurately estimating the post-mortem interval (PMI, also known as time since death) remains a major challenge in forensic science due to substantial biological and environmental variability in human cases. While animal models provide controlled conditions for identifying PMI-related metabolic changes, their translational value for human forensics is unclear. This study bridges controlled rat metabolomics with real-world human autopsy samples using LC-MS-based metabolomics and machine learning. We analyzed femoral blood from 435 medico-legal autopsies (PMI 1-10 days) and employed two complementary modeling strategies. An untargeted Lasso model using all 3450 detected features achieved R2 = 0.51 and identified 51 consistently selected PMI-informative molecular features. To assess cross-species robustness, we reanalysed rat samples using the same analytical platform. Eighteen of the 51 human-derived features were detected in rats, showing clear PMI-dependent trends and strong correlations with PMI. Two metabolites achieved level 2 annotation and matched known PMI biomarkers from previous animal studies, demonstrating meaningful human-to-rat biochemical overlap. In contrast, a targeted Lasso model based on biomarkers identified in rats performed poorly in humans (R2 = 0.10), underscoring limited direct rat-to-human translation. Overall, human-derived biomarkers validated robustly in the controlled rat model proved far more reliable. These findings highlight both the potential and limitations of cross-species PMI biomarker discovery and emphasize the need for improved metadata, especially temperature history, to advance PMI modeling in forensic practice.
27 May 2026
Read appraisal →Nature communications
Measuring multi-site pulse transit time with an AI-enabled mmWave radar
Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present an AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways - heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery - achieving correlation coefficients of 0.75-0.86 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90-0.91 for estimating DBP, and achieved a mean error of -0.62-0.06 mmHg and standard deviation of 4.54-5.20 mmHg, meeting the FDA's AAMI guidelines for non-invasive blood pressure monitors. These results suggest that our proposed system has the potential to provide a non-invasive measure of cardiovascular health across multiple regions of the body.
27 May 2026
Read appraisal →Journal of perinatal medicine
Oral adjuvants supplemented to phototherapy as a therapeutic strategy for neonatal hyperbilirubinemia: a systematic review
INTRODUCTION: Neonatal jaundice is a common condition characterized by elevated serum bilirubin levels. Although usually benign, inadequate management may result in severe complications such as bilirubin encephalopathy and kernicterus. Phototherapy is the standard treatment; however, concerns regarding potential short- and long-term adverse effects have motivated the investigation of oral adjuvants as complementary therapies to accelerate bilirubin clearance and reduce phototherapy exposure. CONTENT: This systematic review followed PRISMA guidelines and was registered in PROSPERO (CRD420251071886). Randomized controlled trials published between 2010 and 2025 were included, evaluating full-term neonates (35-42 weeks, 2-4 kg) without Rh incompatibility. Searches were conducted in PubMed, Cochrane Central, Embase, and Web of Science. Of 193 identified records, 18 RCTs met eligibility criteria. The oral adjuvants assessed included probiotics, phenobarbital, ursodeoxycholic acid (UDCA), oral calcium, zinc sulfate, fenofibrate, and clofibrate. Lactobacillus reuteri demonstrated greater 24-hour reductions in total serum bilirubin (TSB) compared to controls, whereas Bifidobacterium strains primarily reduced phototherapy duration. Phenobarbital did not outperform phototherapy alone and was associated with longer hospitalization. UDCA showed substantial bilirubin reductions, although findings were heterogeneous. Zinc supplementation did not demonstrate consistent significant benefit. Fenofibrate produced statistically significant decreases in TSB across trials and showed the most consistent efficacy. Clofibrate also reduced bilirubin levels but with methodological variability and limited comparative data. SUMMARY: Oral adjuvants combined with phototherapy may enhance bilirubin reduction in term neonates, particularly fenofibrate. OUTLOOK: However, heterogeneity and potential bias limit definitive conclusions, emphasizing the need for large, multicenter randomized trials.
27 May 2026
Read appraisal →Scientific reports
Impact of a structured case report on self-reported responses to simulated emergency scenarios: a randomized survey-based study
Case reports are a long-standing component of medical research, but their association with self-reported responses to simulated clinical scenarios remains underexplored. This study investigated whether exposure to a structured case report influences simulated emergency medical decisions. This randomized survey-based study was conducted among licensed physicians between March and May 2025. Participants were randomly assigned to an intervention group, which received a structured case report on blunt neck trauma, or a control group, which did not. Both groups subsequently provided self-reported Likert-scale responses to identical simulated emergency scenarios, covering five domains: local trauma consequences, accompanying injuries, diagnostic methods, airway management strategies, and hospital selection. Responses were collected on Likert scales. Group differences were assessed using Mann-Whitney U tests, t-tests, and chi-squared tests; prespecified exploratory subgroup analyses examined the moderating role of clinical experience. Sixty-three participants completed the study. Baseline demographics and qualifications did not differ significantly between groups. Exposure to the case report was associated with statistically significant differences in self-reported responses to simulated scenarios, with the most pronounced associations in airway management strategies (all five items statistically significant, median effect size r = 0.44). Intervention participants assigned higher ratings to maintaining spontaneous breathing and supraglottic airway use, while assigning lower appropriateness to endotracheal intubation and cricothyrotomy. Moderate associations were observed in diagnostic choices (e.g., pathological breath sounds, ultrasound use) and hospital selection (e.g., ECMO availability). No consistent effects were found for local or accompanying injuries. Exploratory stratified analyses revealed that junior physicians (≤ 5 years of practice) appeared more responsive, with significant differences across multiple domains and larger effect sizes (median r > 0.6). In contrast, no statistically significant between-group differences were detected in the senior (> 15 years) subgroup, although this exploratory analysis was limited by small subgroup size. Structured case reports may influence self-reported responses to simulated emergency scenarios, particularly among less experienced clinicians. These findings suggest that the educational impact of case-based learning formats may vary depending on the level of clinical experience, but do not allow conclusions about real-world clinical behavior or patient outcomes. Future work should further investigate adaptive case report formats and their potential integration into emergency medicine training.Trial registration: German Clinical Trials Register (DRKS00038961), registered von 13 January 2026.
27 May 2026
Read appraisal →Scientific reports
Depressive symptoms are a key determinant of health-related quality of life in ICU survivors with psychological distress
Survivors of critical illness frequently experience persistent impairments in health-related quality of life (HRQoL), with psychological symptoms contributing substantially to this burden. The relative contribution of co-occurring depression, anxiety, and post traumatic stress symptoms remains insufficiently understood. To address this gap, we conducted a cross-sectional analysis of pre-randomization data from the PICTURE randomized controlled trial, a multicenter study of a brief primary care-based psychological intervention for post-traumatic stress disorder symptoms following critical illness, including 319 intensive care unit survivors. Clinical, demographic, and mental health assessments were obtained after ICU discharge. Latent profile analysis, random forest modeling, and quantile regression were applied to identify determinants of HRQoL measured by the EuroQol Five-Dimension Five-Level (EQ-5D-5L) index and visual analog scale (VAS). The mean EQ-5D-5L index was 0.71 (SD 0.27; median 0.81) and the mean EQ VAS score was 60.7 (SD 19.4; median 60.0), indicating considerable overall impairment. Depression, anxiety, and post-traumatic stress symptoms showed substantial overlap and formed four distinct symptom profiles associated with specific functional impairments. Screening positive for depression on the 2-item Patient Health Questionnaire (PHQ-2) with ≥ 3 points was associated with a median reduction of -0.13 (95% CI -0.19 to -0.07) on the EQ-5D-5L index and -12.45 points (95% CI -17.93 to -6.96) on the EQ VAS, exceeding clinical and demographic predictors. These findings indicate that depressive symptoms are a major determinant of impaired health related quality of life among intensive care survivors with psychological distress and support routine brief depression screening in post-intensive care follow up.Trial registration: ClinTrials.gov: NCT03315390 (Registration date: 2017-10-20); German Clinical Trials Register: DRKS-ID: DRKS00012589 (Registration date: 2017-10-17).
26 May 2026
Read appraisal →Renal failure
Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning
Acute kidney injury (AKI) is a severe and frequent complication following bee stings, with a reported incidence of 30-50%. Early identification is clinically challenging due to the delayed rise in serum creatinine. This retrospective study aimed to develop and validate an interpretable machine learning model for the early prediction of AKI in bee-sting patients. We included 305 patients admitted between January 2018 and September 2025, of whom 99 (32.5%) developed AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection from 33 baseline variables, identifying eight key predictors, including lactate, creatinine, and dark urine. Six machine learning algorithms - logistic regression, random forest, support vector machine, neural network, extreme gradient boosting (XGBoost), and light gradient boosting machine - were developed and evaluated. The XGBoost model demonstrated superior performance, achieving an area under the receiver operating characteristic curve of 0.964 and an accuracy of 92.6% on the test set. SHapley Additive exPlanations analysis was applied to interpret the optimal model, highlighting creatinine, lactate dehydrogenase, lactate, and dark urine as the most influential predictors. A clinically applicable nomogram was constructed and showed good calibration and utility. In conclusion, this study successfully developed a high-performance and interpretable XGBoost model for early prediction of bee-sting-induced AKI. The validated nomogram provides an accessible bedside decision-support tool to facilitate timely risk stratification and intervention, potentially improving patient outcomes.
26 May 2026
Read appraisal →Renal failure
Complication rates of 16- and 18-gauge needles for native kidney biopsies: a systematic review and proportional meta-analysis
This systematic review and meta-analysis evaluated complication rates and diagnostic yield reported in studies of adult native kidney biopsy using 16-gauge (16 G) or 18-gauge (18 G) needles. We included randomized trials and cohort studies of real-time ultrasound-guided biopsies, including case series. MEDLINE, Embase, and CENTRAL were searched through October 2024. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using Joanna Briggs Institute tools. Random-effects meta-analyses estimated pooled proportions with 95% confidence intervals (CI), and univariable random-effects meta-regression explored study-level associations with major complications, transfusion, or gross hematuria. We screened 4,499 titles and abstracts and reviewed 319 full-text articles; 62 studies comprising 68 biopsy series were included. The pooled major complication rate in studies using 16 G needles was 1.83% (95% CI: 1.20-2.79) and 1.29% (95% CI: 0.78-2.13) in studies using 18 G needles, with no statistically significant difference. Mean glomerular yield was 18.8 with 16 G and 17.5 with 18 G needles. In study-level meta-regression, studies with higher prevalence of acute kidney injury, lower mean estimated glomerular filtration rate, or lower mean hemoglobin reported higher pooled complication rates. Most studies were single-arm cohorts; between-needle differences therefore reflect indirect study-level contrasts. Interpretation is limited by retrospective design and heterogeneity across studies. Overall, studies using both needle sizes reported low complication rates and similar diagnostic yield, although definitions and reporting varied. Direct comparative studies are needed to determine whether meaningful differences exist. What was knownKidney biopsy is essential for diagnosing many medical kidney diseases and guiding treatment, requiring adequate tissue samples for subsequent analyses.Modern biopsy techniques use spring-loaded, ultrasound-guided devices with smaller gauge needles, but optimal needle size remains uncertain.While 14G needles are associated with higher complication risk, evidence on whether 16G or 18G needles differ in safety or diagnostic yield is conflicting.This study addsIn this systematic review and meta-analysis of 62 studies involving 22,208 adult native kidney biopsies, we found no statistically significant difference in major complication rates between studies using 16G and 18G needles.In study-level meta-regression analyses, studies with a higher prevalence of acute kidney injury, lower mean estimated glomerular filtration rate, and lower mean hemoglobin reported higher pooled major complication rates.Potential impactStudies using both 16G and 18G needles reported similarly low major complication rates, although comparisons are based on indirect evidence with substantial heterogeneity.Findings support the ethical feasibility and clinical relevance of a large, randomized controlled trial to definitively guide needle size selection in native kidney biopsies.
26 May 2026
Read appraisal →Renal failure
Prognostic factors in malaria patients with acute kidney injury: a systematic review and meta analysis
This systematic review and meta-analysis aims to evaluate the predictors of mortality in malaria patients with AKI. Studies were searched in PubMed, Scopus, Ebsco, and ScienceDirect. We included observational studies that showed the association between clinical or laboratory abnormalities and mortality, reported in odds ratio. The quality of each study was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using R. Sixteen studies with 1,104 patients were analyzed. Factors significantly associated with mortality were respiratory distress syndrome (OR: 18.50, p < 0.001), neurological involvement (OR: 12.83, p < 0.001), disseminated intravascular coagulation (OR: 12.00, p < 0.001), hypotension (OR: 18.23, p < 0.001), oliguria (OR: 3.41, p = 0.002), ventilator requirement (OR: 30.35, p < 0.001), leukocytosis (OR: 4.87, p = 0.003), hyponatremia (OR: 3.67, p = 0.048), acidosis (OR: 4.88, p < 0.001), hyperkalemia (OR: 3.99, p < 0.001), and jaundice (OR: 5.03, p = 0.001). ARDS, neurological involvement, DIC, hypotension, oliguria, ventilator requirement, leukocytosis, hyponatremia, acidosis, hyperkalemia, and jaundice were associated with mortality in malaria patients with AKI. The findings underscored the importance of heightened awareness and vigilance in managing patients with these conditions.
26 May 2026
Read appraisal →Critical care explorations
External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data
BACKGROUND: Acute kidney injury (AKI) is common in critically ill children and is associated with high morbidity and mortality. Risk prediction models designed for clinical decision support implementation can facilitate early identification and proactive mitigation of AKI risk. Existing models have primarily been validated using single-center data, partly because of the lack of appropriately detailed multicenter datasets. OBJECTIVE: To determine the performance of a single-center model to predict new AKI at 72 hours of ICU admission in children across two multicenter datasets and refine this model to improve prediction performance while maintaining acceptable alert burden. DERIVATION AND VALIDATION COHORTS: We analyzed two datasets: the Pediatric Learning Health System Network-Virtual Pediatric Systems (PEDSNET-VPS) dataset, created through the linkage of PEDSnet electronic health record (EHR) extraction with VPS (LLC, http://www.myvps.org), and the PICU Data Collaborative dataset, created through EHR extraction and harmonization from eight participating institutions. We divided each dataset into a derivation and test split. PREDICTION MODEL: We first recalibrated an existing single-center model and measured discrimination (area under the receiver operating characteristic curve [AUROC] and area under the precision-recall curve [AUPRC]) and performance at multiple cutpoints. We next added features available at 12 hours of ICU admission, optimizing by precision and recall. We measured discrimination and performance at multiple cutpoints and identified the features contributing most to the risk score. RESULTS: In total we analyzed 186,540 ICU admissions. We found early AKI by serum creatinine criteria within 72 hours of admission in 2.2-2.7%. Initial recalibration of an existing single-center model demonstrated poor discrimination (AUROC 0.65-0.78; AUPRC 0.10-0.12). Following the addition of new features, the model had higher AUROC (0.80-0.88) and AUPRC (0.13-0.22). CONCLUSIONS: In this first use of two new multicenter datasets, we found improved performance in a model designed using features available at 12 hours of ICU admission, balancing sensitivity and precision to predict patients at risk for AKI development.
26 May 2026
Read appraisal →Circulation journal : official journal of the Japanese Circulation Society
High Human Epididymis Protein 4 Levels Are Associated With Lower Systolic Function and Poor Prognosis in Heart Failure With Reduced Ejection Fraction
BACKGROUND: Human epididymis protein 4 (HE4) is a novel fibrosis marker expressed in activated myofibroblasts. Although we previously demonstrated its prognostic value in dilated cardiomyopathy, its significance in general heart failure with reduced ejection fraction (HFrEF) remains unclear. METHODS AND RESULTS: Serum HE4 concentrations were measured in 140 patients with HFrEF (left ventricular ejection fraction <40%) and followed for up to 3 years to assess cardiovascular (CV) death and heart failure hospitalization. Kaplan-Meier analysis revealed significantly higher CV event rates in the high HE4 group (above the median) than in the low HE4 group (log-rank P<0.001). Multivariate Cox regression analysis identified HE4 (ln[HE4]) as an independent predictor of adverse outcomes (hazard ratio 3.99; 95% confidence interval [CI] 2.05-7.75; P<0.001). In multivariate linear regression, HE4 was significantly associated with age, serum creatinine, plasma B-type natriuretic peptide, E/e', and global longitudinal strain (GLS; β=-0.083; 95% CI -0.111, -0.054; P<0.001). Combining HE4 and GLS demonstrated that patients with high HE4 and low GLS had the highest CV event rate (log-rank P<0.001). CONCLUSIONS: HE4 is an independent prognostic biomarker in HFrEF, and its association with GLS may reflect underlying myocardial dysfunction and fibrotic remodeling leading to adverse outcomes.
26 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Extracorporeal shockwave therapy in bone fractures: a systematic review
INTRODUCTION: The increasing incidence of fractures and cases of delayed union or nonunion has encouraged the search for non-invasive therapies. Extracorporeal shockwave therapy (ESWT) has been proposed as a strategy to stimulate bone regeneration and improve fracture healing. MATERIALS AND METHODS: A systematic review was conducted following PRISMA guidelines, with searches performed in PubMed, Scopus, Web of Science, and Embase (2005-2025). Clinical studies evaluating ESWT for fracture treatment with radiographic assessment of bone healing and the presence of a control group were included. RESULTS: A total of 834 studies were initially identified, of which 7 were included in the final analysis. Most studies demonstrated positive effects of ESWT, including improved bone healing, pain reduction, and functional recovery. However, there was considerable heterogeneity in treatment protocols and relatively small sample sizes. CONCLUSION: ESWT shows potential as a non-invasive therapy to stimulate fracture healing, particularly in cases of delayed union or nonunion. However, further controlled clinical trials and standardized protocols are needed to confirm its clinical effectiveness.
25 May 2026
Read appraisal →Circulation journal : official journal of the Japanese Circulation Society
A Phase 2, Randomized, Clinical Trial of Inhaled Nitric Oxide for Acute Severe Right Heart Failure With Pulmonary Hypertension (PHiNO Study)
BACKGROUND: Acute right heart failure (RHF) is life threatening in patients with pulmonary hypertension (PH). This study investigated the efficacy and safety of inhaled nitric oxide (iNO; INOflo®for inhalation 800 ppm), a rapid pulmonary vasodilator, in patients with acute severe RHF due to PH. METHODS AND RESULTS: In this Phase 2 randomized controlled trial, 30 patients with acute severe RHF due to PH (pulmonary arterial hypertension or chronic thromboembolic PH) were randomized 1 : 1 to either an iNO or control group (which did not receive iNO). The primary endpoint was the change in pulmonary vascular resistance (PVR) from baseline to 30 min after either iNO initiation or patient registration and assignment (control group), which differed significantly between the iNO and control groups (mean [±SD] -2.41±2.47 vs. 0.8±1.03 Wood units, respectively; between group difference -3.21 [95% confidence interval -4.633, -1.785] Wood units). Serum B-type natriuretic peptide (BNP) levels and inferior vena cava diameter (secondary endpoints) decreased significantly in the iNO group over the 7-day study period. No serious adverse events, including methemoglobinemia, were observed. CONCLUSIONS: iNO significantly reduced PVR in patients with acute severe RHF due to PH, without serious adverse events. Serum BNP levels and inferior vena cava diameter improved in the iNO group. These findings suggest that iNO is a promising acute treatment option for RHF due to PH.
25 May 2026
Read appraisal →Rheumatology international
Diffuse splenic calcifications in systemic lupus erythematosus: a case-based review
Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disease with a wide spectrum of splenic manifestations. In recent literature, splenic calcifications have increasingly been recognized as a rare manifestation of SLE. We describe a patient presenting with fever in whom abdominal imaging revealed splenomegaly with diffuse splenic microcalcifications, prompting further diagnostic evaluation that ultimately led to the diagnosis of SLE. To contextualize this finding, we performed a systematic case-based literature review and identified 29 previously reported cases, resulting in a total of 30 cases including our own. Across reported cases, no distinct serological or clinical phenotype could be identified. However, a characteristic imaging pattern emerged, predominantly consisting of diffuse, small, round-to-rod-shaped splenic calcifications. When longitudinal data were available, calcifications were usually stable, although progression was reported in association with disease flares, sometimes evolving to functional autosplenectomy. This case illustrates that splenic calcifications may represent an early or presenting manifestation of SLE and that an incidental detection should prompt consideration of autoimmune disease in the broad differential diagnosis. By synthesizing previously scattered reports, this review provides an updated overview to support clinical interpretation and awareness.
25 May 2026
Read appraisal →European journal of pediatrics
Pediatric Trauma Score (PTS) combined with blood lactate measurement provides clinical treatment guidance and prognosis prediction for children with compound injuries: a prospective study
UNLABELLED: Compound injuries in children are the leading cause of death and disability, and early and accurate assessment is crucial to optimize treatment. This study aimed to explore the clinical value of Pediatric Trauma Score (PTS) combined with blood lactate measurement in the triage, treatment guidance and prognosis prediction of pediatric compound injuries. From January 2023 to December 2024, a randomized, single-blind controlled trial was conducted in the emergency department of Hebei Provincial Children's Hospital (the only tertiary pediatric medical center in Hebei Province), China. A total of 546 children with compound injuries who presented to the hospital were included and randomly divided into the intervention group (n = 273, managed by PTS combined with dynamic monitoring of blood lactate) and the control group (n = 273, managed by PTS only). The main outcomes were the timeliness of rescue (time to critical intervention) and the accuracy of prognosis prediction (predictive effectiveness of mortality and major complications). Secondary outcomes included emergency department length of stay, unplanned Intensive Care Unit (ICU) admission, and 30-day complication rate. The median time from admission to initiation of key interventions was significantly shorter in the intervention group compared with the control group (45 [interquartile range (IQR): 30-65] min vs. 68 [IQR: 45-95] min, P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) of the combined indicator (PTS + initial lactate) to predict 30-day mortality was 0.936 (95% CI: 0.902-0.970), which was significantly higher than PTS alone (AUC: 0.812, P < 0.001) or initial lactate (AUC: 0.855, P = 0.002). The emergency department length of stay, unplanned ICU admission rate and 30-day complication rate of the intervention group were significantly lower than those of the control group (all P < 0.05). Dynamic monitoring showed that 6-h lactate clearance < 30% was a strong independent predictor of the occurrence of multiple organ dysfunction syndrome (MODS) (odds ratio (OR) = 5.42, 95% CI: 2.88-10.21,P < 0.001. CONCLUSION: The combined application of PTS and blood lactate integrates physiological status and metabolic information, which can significantly improve the accuracy of early triage of pediatric compound injuries, guide the implementation of rescue measures, and effectively predict prognosis through dynamic monitoring, providing a reliable basis for precise personalized management. WHAT IS KNOWN: • Compound injuries in children are the leading cause of death and disability, and early and accurate assessment is crucial to optimize treatment. WHAT IS NEW: • The combined application of the Pediatric Trauma Score (PTS) and blood lactate signifi cantly improves the accuracy of early triage and 30-day mortality prediction, shortens the time to critical intervention, and reduces unplanned ICU admission and 30-day complication rates in pediatric compound injuries. • Dynamic monitoring shows that 6-hour lactate clearance 30% is a strong independent predictor of multiple organ dysfunction syndrome (MODS).
25 May 2026
Read appraisal →World journal of urology
Retrograde intrarenal surgery versus miniaturized percutaneous nephrolithotomy for renal calculi: a grade-assessed meta-analysis of randomized controlled trials with trial sequential analysis
BACKGROUND: The optimal management of medium-sized (1-3 cm) renal stones remains a clinical dilemma, with retrograde intrarenal surgery (RIRS) and miniaturized percutaneous nephrolithotomy (mPCNL) serving as competing minimally invasive options. To overcome the selection bias inherent in previous meta-analyses that included observational data, we aimed to compare the efficacy and safety of both techniques by exclusively analyzing randomized controlled trials (RCTs). METHODS: A systematic literature search of five databases was conducted up to February 2026. Only RCTs comparing RIRS and mPCNL were included. The primary outcomes were the single session stone-free rate (SFR) and overall complication rate. Secondary outcomes included stone clearance-related outcomes, operative performance outcomes, and recovery-related outcomes. Data were pooled using a random-effects model, and trial sequential analysis (TSA) was applied. RESULTS: Thirty RCTs encompassing 4173 patients were included. mPCNL demonstrated a significantly higher single-session SFR compared to RIRS (R.R: 0.92, 95% C.I: 0.88-0.96, p < 0.001). RIRS showed a trend toward a lower overall complication rate although the difference did not reach statistical significance (RR = 0.79, 95% CI: 0.63-1.01; p = 0.057), reduced blood transfusion requirements, smaller hemoglobin drops, and shorter hospitalization times. However, RIRS required significantly more postoperative auxiliary procedures. Operative times and high-grade Clavien-Dindo III-V complication rates were comparable between both techniques. TSA confirmed that the cumulative evidence for single-session SFR was sufficient and conclusive, whereas TSA findings for overall complication rate should be interpreted cautiously. CONCLUSIONS: mPCNL achieves significantly higher SFR than RIRS in the management of 1-3 cm renal calculi. However, RIRS was associated with lower bleeding-related morbidity and shorter hospitalization, although no statistically significant difference was observed in the overall complication rate. Therefore, the choice between RIRS and mPCNL should be individualized, balancing maximal SFR against procedural invasiveness according to patient characteristics and stone complexity.
25 May 2026
Read appraisal →World journal of urology
From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology.
INTRODUCTION: Accurate assessment of stone burden is fundamental in urolithiasis, as it directly influences treatment selection and prognostic evaluation. Although maximum stone diameter on non-contrast computed tomography remains the most widely used parameter, it does not adequately reflect the three-dimensional complexity of urinary calculi. This review aimed to summarize the evolution of stone burden assessment from conventional imaging-based methods to software-assisted volumetry and artificial intelligence (AI)-driven tools, with emphasis on their accuracy, reproducibility, and clinical utility. METHODS: A narrative review of the literature was performed using PubMed/MEDLINE, Scopus, and Google Scholar for English-language studies published up to March 2026. Original studies, validation studies, technical reports, reviews, and guideline-related papers addressing conventional CT-based measurement, software-assisted volumetry, AI-based stone segmentation, and the clinical significance of stone volume were included. Due to heterogeneity in study design and reported outcomes, the evidence was synthesized narratively. RESULTS: Maximum stone diameter remains simple and widely available, but it incompletely represents true stone burden, particularly in larger or irregular stones. Formula-based ellipsoid calculations are practical yet show limited accuracy in complex geometries. Semi-automated CT-based segmentation software provides more reliable volumetric assessment, with excellent agreement with reference standards and reduced interobserver variability. AI-based approaches have further improved efficiency by enabling rapid and highly accurate automated stone detection and volume calculation. Across the reviewed literature, stone volume was consistently shown to be more clinically informative than linear dimensions for predicting spontaneous passage, stone-free rates after shockwave lithotripsy and ureteroscopy, and future symptomatic events during surveillance. CONCLUSIONS: Stone volume offers a more accurate and clinically meaningful estimate of stone burden than maximum stone diameter alone. The transition from formula-based methods to software-assisted and AI-driven volumetry represents an important advance in urolithiasis imaging. Wider adoption will depend on standardized imaging protocols, improved software accessibility, and validation of volume-based thresholds for routine clinical practice.
25 May 2026
Read appraisal →Journal of medical systems
A Standardized Framework for Evaluating State Opioid Surveillance Dashboards: A Health Informatics Assessment
BACKGROUND: State public health agencies across the U.S. depend on opioid surveillance dashboards to monitor overdose trends and guide interventions. Despite their central role in decision-making, no standardized framework exists to evaluate how well these dashboards actually function. OBJECTIVE: We developed a health informatics framework to systematically assess state opioid surveillance dashboards and applied it across all 50 U.S. states. METHODS: Our framework evaluates dashboards across nine domains (including temporal analysis, geographic granularity, data disaggregation, visualization features, narrative support, simplification techniques, and interactive functionality) using 28 specific sub-variables. Sub-variables were selected based on public health informatics literature on effective dashboard design, including features identified in COVID-19 dashboard evaluations and usability frameworks. We scored each feature as either present or absent, creating composite scores with a theoretical maximum of 28 points. All 50 U.S. state-level dashboards were reviewed twice by the study team, with an external consistency check on a subset of 10 dashboards. We calculated the correlation coefficient between dashboard scores and state population estimates. RESULTS: Dashboard scores varied widely, ranging from 12 to 22 points (mean:19.18, standard deviation:2.20). Three states (Delaware, Indiana, and New York) achieved the highest score of 22 by offering comprehensive geographic detail, extensive data breakdowns, and full interactive capabilities. Interactive filtering was present in 92% of dashboards, but only 80% enabled switching between visualization modes. While nearly all dashboards showed time trends, only 6% provided zip code-level detail, and over a third lacked race and ethnicity data. Single external reviewer demonstrated strong correlation (r = 0.84, p = 0.002). Dashboard scores were not correlated with state population size (r = 0.187, p = 0.193). CONCLUSIONS: State opioid surveillance dashboards varied substantially in observable capabilities. This framework provides a standardized method to compare dashboard features and identify gaps that may inform future refinement.
25 May 2026
Read appraisal →JMIR formative research
Prediction of 30-Day All-Cause Hospital Readmissions Using Limited Structured Electronic Health Record Data: Retrospective Comparative Study
BACKGROUND: Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, with 3.8 million 30-day all-cause readmissions in 2018 at an average cost of US $15,200 each, totaling US $58 billion in costs. Many published prediction models rely on comprehensive information (eg, full billing abstractions, discharge summaries, laboratory tests, and vitals) that becomes available only late in the encounter, limiting usefulness for real-time, in-hospital intervention. This creates a timeliness-accuracy trade-off: models that are most accurate retrospectively may arrive too late to act upon. OBJECTIVE: This study tests whether a clinically meaningful predictive signal for 30-day all-cause readmission is present within a limited set of structured clinical codes recorded during the patient's hospital stay. This approach evaluates whether predictive signals are retained when using a restricted set of structured clinical codes. METHODS: We conducted a retrospective comparative modeling study using a large, deidentified electronic health record dataset of 50,000 inpatient encounters from the 2019 New York State Emergency Department Database. Two feature sets were constructed: (1) a limited set consisting of the first 5 ICD-10 (International Classification of Diseases, 10th Revision) diagnosis codes, the first 5 Current Procedural Terminology (CPT) codes, and Charlson Comorbidity Index (CCI; 11 input features); and (2) a rich set using all available ICD-10 and CPT codes plus CCI (up to 135 input features). We trained 4 models: random forest, CatBoost, multilayer perceptron, and DistilBERT (a distilled Bidirectional Encoder Representations from Transformers [BERT] model; structured codes mapped to text and tokenized with DistilBERT-base-uncased). Evaluation used an untouched hold-out set of 10,000 encounters, preserving the natural 21.1% readmission prevalence. Primary metrics were area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. To address class imbalance, the training split only was balanced via undersampling of the majority class and bootstrap oversampling of the minority class; validation/test distributions were left unchanged. RESULTS: Models trained on the limited feature set achieved AUROC values ranging from 0.5369 to 0.5596 and F1-scores from 0.2555 to 0.3434. Across 3 of 4 architectures, models trained on the limited feature set matched or exceeded the discrimination of their rich counterparts. The best model (random forest, limited) achieved an area under the curve AUROC 0.5596 (95% CI 0.5440-0.5739) compared to the best performing rich model (DistilBERT) at 0.5703 (95% CI 0.5565-0.5842), an absolute difference of 0.0107. The highest F1-score (0.3434) was achieved by DistilBERT on the limited feature set. Differences across architectures were small in absolute terms, with threshold-dependent metrics (eg, F1-score) being comparable. CONCLUSIONS: The findings suggest that models using a limited set of structured clinical codes can achieve performance comparable to those using more comprehensive coding information.
25 May 2026
Read appraisal →Medicine
Effects of a psychological capital intervention on emergency nurses' work engagement and turnover intention: A randomized controlled trial
BACKGROUND: Emergency nurses are exposed to high occupational stress, which leads to decreased psychological capital, reduced work engagement, and high turnover intention. Evidence on targeted psychological capital intervention (PCI) for this population remains limited. METHODS: A parallel randomized controlled trial was conducted. A total of 122 emergency nurses from a tertiary hospital in Zhejiang Province were randomly assigned to the intervention group (6-week PCI, n = 61) or the control group (routine training, n = 61). Outcomes were measured at baseline (T0), post-intervention (T1), 1 month (T2), and 3 months (T3) using the Psychological Capital Questionnaire, Utrecht Work Engagement Scale, and Turnover Intention Scale. RESULTS: Baseline characteristics and outcome scores were comparable between groups (all P > .05). At T1 to T3, the intervention group had significantly higher Psychological Capital Questionnaire and Utrecht Work Engagement Scale scores and lower Turnover Intention Scale scores than the control group (all P < .001). Psychological capital was positively correlated with work engagement (R = 0.604, P < .001) and negatively correlated with turnover intention (r = -0.624, P < .001). CONCLUSION: This 6-week PCI program significantly improves psychological capital and work engagement, and reduces turnover intention among emergency nurses, with effects sustained for at least 3 months. These findings support the utility of PCI as a psychological management strategy for emergency nursing staff.
25 May 2026
Read appraisal →Current urology reports
Association Between Ambient Temperature and Urolithiasis: A Systematic Review and Meta-analysis
PURPOSE OF REVIEW: The global incidence of urolithiasis has risen in recent decades. Emerging evidence suggests that higher ambient temperatures are linked to an increased risk of kidney-stone-related events, including acute renal colic, likely through physiological mechanisms such as increased urinary calcium excretion and greater supersaturation of calcium oxalate and calcium phosphate. This study aims to systematically review existing evidence on the relationship between ambient temperature and urolithiasis risk. RECENT FINDINGS: Of 892 records identified, 46 studies met the inclusion criteria for the systematic review, and 16 were included in the meta-analysis. Most studies reported a positive association between higher ambient temperatures and increased kidney stone-related events, with seasonal peaks occurring during summer months in the Northern Hemisphere and from December to March in Australia. Considerable heterogeneity was observed in exposure definitions, meteorological data sources, outcome ascertainment, and lag structures. Reported effect estimates ranged from RR 1.10 to 2.54. Meta-analysis showed that higher ambient temperature was associated with an increased risk of urolithiasis presentation (pooled RR 1.31, 95% CI 1.21-1.43; I²=86.7%). Subgroup analyses showed pooled RRs of 1.27 (95% CI 1.18-1.37) for stone colic, 1.33 (95% CI 1.06-1.67) for emergency visits, and 1.36 (95% CI 0.98-1.90) for hospitalizations. Associations were stronger at medium and long lag periods than at short lags. Most studies were rated as high risk of bias, primarily because of confounding and outcome measurement concerns. Sensitivity analyses supported the robustness of the findings. This systematic review highlights an association between higher ambient temperatures and increased presentations for kidney stone disease. Addressing environmental factors may be a valuable strategy for reducing nephrolithiasis incidence.
25 May 2026
Read appraisal →Medicine
Predicting poor wrist function recovery after internal fixation of distal radius fractures: A retrospective study based on multivariate analysis and machine learning models
This study aimed to identify independent risk factors associated with poor wrist function recovery 6 months after internal fixation of distal radius fractures (DRF) and develop machine learning models for early risk prediction. This retrospective study included 328 patients who underwent internal fixation for DRF at our hospital between January 2022 and June 2025. Wrist function was evaluated using the Cooney Wrist Score at 6 months postoperatively. Poor recovery was defined as a Cooney score <75. The overall incidence of poor wrist function recovery was 21.56% (71/328). Clinical variables including demographic characteristics, comorbidities, laboratory indicators, fracture type, postoperative pain scores, and rehabilitation compliance were collected. Rehabilitation compliance referred to patients' adherence to prescribed postoperative functional training and follow-up plans. Variables with P <.05 in univariate analysis were entered into multivariate logistic regression to identify independent risk factors. These variables were subsequently used to construct random forest (RF), XGBoost, and backpropagation (BP) neural network models. Model performance was evaluated using ROC curves and classification metrics. Multivariate analysis identified low rehabilitation compliance, age ≥65 years, AO type C fractures, postoperative visual analogue scale scores >3 at 1 week, and decreased serum albumin levels as independent predictors of poor wrist function recovery. Among the machine learning models, the RF model demonstrated the best performance (AUC = 0.939, accuracy = 0.946), followed by the BP neural network (AUC = 0.924). The XGBoost model showed comparatively lower predictive performance (AUC = 0.876). Poor wrist function recovery after internal fixation of DRF is influenced by multiple clinical factors. Machine learning models based on identified independent risk factors, particularly the RF model, show strong predictive ability and may assist clinicians in early risk stratification and individualized rehabilitation planning.
25 May 2026
Read appraisal →Open heart
Predictors of high-grade atrioventricular block recovery in Takotsubo cardiomyopathy: a systematic review of case reports
BACKGROUND: Atrioventricular block (AVB) in Takotsubo cardiomyopathy (TCM) is a rarely reported complication. Due to its low incidence and lack of randomised trials, there remain significant gaps in the evidence regarding its underlying mechanisms, the probability of conduction recovery and the optimal timing for permanent pacemaker implantation. METHODS: A systematic review was conducted on published case reports on TCM associated with high-grade AVB, diagnosed simultaneously or within the same hospital admission. Cases with low-grade AVB, sole sinus node disease or TCM induced by pacemaker implantation were excluded. RESULTS: A total of 50 cases were included in the final analysis; stratified into two groups based on AVB recovery. Fifteen patients (30%) developed complete AVB recovery. Compared with persistent AVB, those who experienced recovery were younger (median (IQR): 66 (61-73) vs 77 (65-82), p=0.02), more frequently reported identifiable emotional or physical stressors (93% vs 49%, p=0.004) and demonstrated earlier recovery of left ventricular ejection fraction (LVEF) within 1 week (60% vs 24%, p=0.02). No significant differences were observed in terms of comorbidities (hypertension in 27% vs 47%, p=0.22), presenting symptoms (chest pain in 40% vs 14%, syncope or presyncope in 27% vs 46%, p=0.22), ST elevation (27% vs 14%, p=0.28) or regional wall motion abnormalities (apical akinesia in 47% vs 46%, p=0.93). On multivariate analysis, the presence of an identifiable stressor was an independent predictor of AVB recovery (HR 8.27; 95% CI 1.07 to 63.73, p=0.04). CONCLUSION: In patients with TCM and high-grade AVB, the absence of a clear stressor highlights the possibility of baseline conduction disorder, with a higher risk of persistent AVB. Earlier LVEF recovery and younger age may help to predict AVB recovery and guide patient selection for PPM implantation. PROSPERO REGISTRATION NUMBER: CRD420251173831.
24 May 2026
Read appraisal →The AAPS journal
A Mechanistic Framework Integrating Renal QSP-PK-PD and Machine Learning for Baseline-Informed Stratification of Diuretic Resistance
Diuretic resistance represents a major source of heterogeneity in loop diuretic response and remains a key barrier to effective decongestion in heart failure. A key clinical challenge is the early identification of patients at high risk of an inadequate response to standard-dose furosemide in order to inform timely treatment intensification or alternative decongestive strategies. However, current approaches remain reactive and rely on post-treatment response, limiting prospective risk stratification and treatment decision-making. To address this problem, we integrated a mechanistic renal quantitative systems pharmacology (QSP) model with furosemide pharmacokinetics and pharmacodynamics to enable baseline-informed risk stratification of diuretic resistance. The model was calibrated using published clinical pharmacokinetic and renal response data and applied to generate a physiologically constrained virtual patient population capturing heterogeneity in renal function, tubular sodium handling, and neurohormonal activation. Machine learning methods were incorporated as complementary analytical tools to identify and validate baseline physiological determinants that define risk categories of diuretic response within the mechanistic simulation framework. Model-based analyses indicated that diuretic resistance arises from the combined effects of reduced filtration capacity, enhanced tubular sodium avidity, diminished pharmacodynamic sensitivity and sustained neurohormonal activation. Across analytical approaches, baseline fractional excretion of sodium and glomerular filtration rate emerged as integrative biomarkers that stratify patients into distinct risk categories of diuretic response, reflecting a mechanism-informed reduction of underlying physiological variability. These findings provide a mechanistically grounded framework for baseline-informed risk stratification and may inform earlier identification of patients at risk of inadequate response, supporting model-informed evaluation of treatment intensification or alternative decongestive strategies.
24 May 2026
Read appraisal →Medicine
Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI
Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification in patients with non-ST-segment elevation myocardial infarction (NSTEMI) remains challenging. We aimed to develop and validate prediction models for ICU delirium in NSTEMI patients using Boruta-based feature selection and machine learning (ML) approaches, and to compare ML performance with a nomogram model. We retrospectively identified adult ICU patients (≥ 18 years) diagnosed with NSTEMI in the Medical Information Mart for Intensive Care IV database, and included only those with no delirium within the first 24 hours of admission and no prior history of delirium. A total of 392 patients were included (mean age 72.2 ± 12.6 years; 64% male) and categorized into a delirium group (n = 201) and a non-delirium group (n = 191). Clinical characteristics and laboratory indices were extracted. Participants were randomly split into a training cohort (70%) and a validation cohort (30%). The Boruta algorithm was applied in the training cohort for feature selection and identification of relevant predictors. A nomogram was constructed using Boruta-selected variables. In parallel, the same features were incorporated into ML models; model discrimination and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. SHapley Additive exPlanations were used to quantify and visualize feature contributions for the best-performing model. Boruta identified 9 key variables: systolic blood pressure, serum calcium, pulse oximetry, serum sodium, creatinine, serum chloride, anion gap, blood urea nitrogen, and body temperature. In the validation cohort, CatBoost achieved the best discrimination among ML models, with an area under the receiver operating characteristic curve of 0.743 (95% CI = 0.653-0.833). The nomogram demonstrated lower discrimination (validation area under the curve = 0.61). SHapley Additive exPlanations analyses indicated that the Boruta-selected variables contributed differentially to delirium prediction, supporting model interpretability. In ICU patients with NSTEMI, a Boruta-informed CatBoost model showed moderate predictive performance and outperformed a nomogram constructed from the same predictors. This interpretable ML approach may facilitate early identification of patients at high risk of delirium and support timely preventive strategies.
24 May 2026
Read appraisal →Medicine
Global, regional, and national burden of clavicle, scapula, or humerus fracture in 204 countries and territories, 1990 to 2021: A systematic analysis from the Global Burden of Disease Study 2021
Clavicle, scapula, or humerus fracture represent a major public health challenge, yet their epidemiology and health burden remain poorly characterized. Using the Global Burden of Disease 2021 framework, we quantified incidence, prevalence, and years lived with disability (YLDs) of clavicle, scapula, or humerus fractures across 204 countries from 1990 to 2021. Estimates were stratified by age, sex, region, and socio-demographic index (SDI), and reported with 95% uncertainty intervals (UIs) from Bayesian models. Globally, the age-standardized incidence rate (ASIR) declined from 276.68 (95% UI: 219.06-346.71) per 100,000 person-years in 1990 to 220.94 (95% UI: 172.60-280.99) per 100,000 person-years in 2021, with an estimated annual percentage change of -0.78% (95% UI: -0.82% to -0.73%). High SDI regions showed the greatest ASIR (300.30 per 100,000 person-years), age-standardized prevalence rate (112.03 per 100,000 person-years), and age-standardized YLDs rate (3.72 per 100,000 person-years). In 2021, Eastern Europe had the highest ASIR (465.06 per 100,000 person-years), while Australasia led in age-standardized prevalence rate (151.42 per 100,000 person-years) and age-standardized YLDs rate (5.04 per 100,000 person-years). China ranked first in absolute prevalence (1.21 million, 95% UI: 1.04-1.39 million) and YLDs (40,235, 95% UI: 24,471-61,196). Despite declines in age-standardized rates, absolute cases increased substantially, highlighting demographic and regional disparities. Priority populations for targeted prevention include women aged ≥60 years and residents of high-SDI regions, as these strata accounted for the highest burden across incidence/prevalence/YLD rates.
24 May 2026
Read appraisal →Medicine
Efficacy of traditional Chinese medicine in treating severe pneumonia: A meta-analysis and systematic review
BACKGROUND: Severe pneumonia (SP) is a critical respiratory disease characterized by its severity and high mortality rate. Traditional Chinese medicine (TCM) has gradually been used as an adjunctive therapy in clinical treatment of SP, yet its efficacy and safety lack systematic evidence. This study aims to analyze, evaluate, and compare current research evidence on TCM adjunctive therapy for SP, providing a basis for clinical treatment of this condition. METHODS: A systematic search was conducted on both English and Chinese online databases (PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP) to identify relevant papers published from inception to November 2023. High-quality randomized controlled tests (RCTs) were selected for analysis. Outcome measures related to SP were extracted from the literature. Statistical analysis was performed using RevMan 5.3 (Cochrane) software to assess heterogeneity among included studies, calculate mean differences, odds ratios, and their 95% confidence intervals, and detect publication bias. RESULTS: A total of 34 RCTs met the inclusion criteria. Comparative analysis revealed that integrating TCM with conventional treatment significantly improves the overall clinical effective rate(P < .05). Specifically, it markedly alleviates TCM symptoms (P < .05). Additionally, TCM adjunctive therapy reduces levels of inflammatory markers including white blood cells, C-reactive protein (P < .05), and decreases Clinical Pulmonary Infection Score and Acute Physiology and Chronic Health Evaluation II scores compared to conventional treatment alone (P < .05). Compared to conventional treatment alone, TCM adjunctive therapy also enhances arterial blood gas indices (PaO2) (P < .05) and pulmonary function (forced expiratory volume in 1 second) (P < .05). Complication rates and mortality are lower in the TCM adjunctive therapy group(P < .05). Moreover, TCM treatment significantly boosts immune function (cluster of differentiation 4+ T lymphocyte) (P < .05). CONCLUSION: Integrating TCM with conventional treatment shows advantages in the efficacy of SP treatment, clinical symptoms management, inflammatory markers, and severity grading. Compared to conventional treatment alone, TCM adjunctive therapy improves immune function, pulmonary function, complication rates. However, future research should focus on large-scale, high-quality RCTs to further validate the effectiveness and safety of TCM adjunctive therapy for SP.
24 May 2026
Read appraisal →Lancet (London, England)
Endovascular thrombectomy for patients with large-core ischaemic stroke presenting up to 24 h after onset (ATLAS): a systematic review and individual patient data meta-analysis with central imaging adjudication
BACKGROUND: Patients with extensive ischaemic change are often excluded from endovascular thrombectomy. We aimed to synthesise the evidence from recent trials in these patients by performing a systematic review and individual patient data meta-analysis to estimate treatment benefit, including within clinical and imaging subgroups. METHODS: In this systematic review and meta-analysis, we searched PubMed and Embase for randomised trials published between March 1, 2018, and March 1, 2025, that evaluated efficacy and safety of endovascular thrombectomy compared with medical management in patients with large-core ischaemic stroke (based on an Alberta Stroke Program Early CT Score [ASPECTS] of ≤5 or estimated ischaemic core ≥50 mL) presenting within 24 h of onset. Individual patient-level data from all eligible trials were obtained. A central imaging core laboratory readjudicated ASPECTS and reanalysed ischaemic core volume. A two-stage meta-analysis with random-effects model was used to evaluate the distribution of 90-day modified Rankin Scale (mRS) scores (the primary outcome) using adjusted pooled generalised odds ratios (aGenORs). Missing data were handled by multiple imputation. Safety outcomes were all-cause mortality within 90-day follow-up and neurological worsening within 24-48 h of randomisation, reported as adjusted pooled relative risk (aRR); and symptomatic intracerebral haemorrhage within 36 h of randomisation (reported as risk difference). Subgroup analyses based on clinical and imaging characteristics were done, including subgroups defined by ischaemic core volume, ASPECTS, and time window from onset to randomisation. The meta-analysis was registered with PROSPERO (CRD420251058584). FINDINGS: We included 1886 patients (944 assigned to endovascular thrombectomy and 942 assigned to medical management) from six trials. Baseline characteristics were similar between treatment groups. At day 90, the distribution of mRS scores was improved in patients in the endovascular thrombectomy group (median score 4 [IQR 3-6]; n=940) versus those in the medical management group (5 [4-6]; n=931; aGenOR 1·63 [95% CI 1·42-1·88], p<0·0001). The endovascular thrombectomy group also had reduced mortality (292 [31·1%]) compared with the medical management group (347 [37·3%]; aRR 0·82 [95% CI 0·70-0·97], p=0·022). No significant differences were observed in symptomatic intracranial haemorrhage (ten [1·1%] of 944 vs nine [1·0%] of 942 patients; pooled unadjusted risk difference -0·17 percentage points [95% CI -1·01 to 0·67], p=0·69) or neurological worsening (197 [22·0%] of 896 patients vs 161 [17·9%] of 899; aRR 1·19 [0·87-1·62], p=0·27). Improved functional outcomes with endovascular thrombectomy were consistent across clinical and imaging subgroups, except for those with an estimated ischaemic core volume of 150 mL or greater, in whom point estimates favoured endovascular thrombectomy, particularly in the early time window (0-6 h), but wide 95% CIs limited interpretation. INTERPRETATION: Endovascular thrombectomy was associated with improved functional outcomes and reduced mortality versus medical management in patients with large-core ischaemic stroke presenting within 24 h of onset. With the exception of very extensive ischaemic changes (core volume ≥150 mL) presenting beyond 6 h, where evidence remains limited, benefit was sustained across ASPECTS and ischaemic core strata for patients presenting up to 24 h after onset. FUNDING: None.
24 May 2026
Read appraisal →Studies in health technology and informatics
AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework
The clinical use of electroencephalography (EEG) for neuro-prognostication in neurocritical care remains limited, despite its ability to provide non-invasive insight into brain activity. This study presents a conceptual framework and early-stage prototype of an AI-assisted EEG decision-support system. Using a literature-informed design and system architecture approach, a web-based prototype was developed to support automated EEG analysis and interpretable brain response reporting. Feasibility testing with four users demonstrated reliable system operation and generation of within-patient decision-support indicators. We highlight the system design and feasibility of operationalizing EEG-based analytics into a clinically interpretable decision-support framework for neurocritical care.
24 May 2026
Read appraisal →Studies in health technology and informatics
Assessing AI-Based Decision Support in Early Sepsis and AKI Recognition
Sepsis and acute kidney injury (AKI) remain among the most critical conditions in acute care, associated with high morbidity and mortality. Early risk recognition is essential but often hampered by nonspecific symptoms. Recent studies have demonstrated that AI- and ML-based Clinical Decision Support Systems (CDSS) can enhance the early detection of sepsis and AKI and support clinical decision-making [1-3]. In May 2025, the University Hospital Augsburg (UKA) integrated an externally developed AI-based CDSS to provide real-time risk predictions for sepsis and AKI. This study evaluates the effectiveness of the system in routine clinical practice using retrospective data from periods before and after its implementation. Primary endpoints include diagnostic accuracy. The results will indicate whether the CDSS contributes to improved early risk recognition and enhanced patient safety in a real-world hospital environment.
24 May 2026
Read appraisal →Studies in health technology and informatics
Exploring a Large Language Model-Based Chatbot Use in Data Analysis: A Case Study of the Problems Related to the Do Not Attempt Resuscitation Order
The interpretative framework was used to explore how healthcare professionals (HCPs), artificial intelligence (AI), and researchers construct and negotiate meaning, trust, and authority in critical, highly ethical medical contexts. This case study aimed to explore and test the integration of the Microsoft Copilot® large language model (LLM) chatbot into ethically sensitive clinical decision-making (DM) to identify the problems HCPs face in do-not-attempt-resuscitation (DNAR) order-making and key areas for development. A sample of 100 HCPs' views was analyzed using qualitative content analysis. AI identified 15 thematic categories emerging from responses that described issues related to DNAR protocols and DM. The frequency of opinions in categories ranged from 4 to 16 (Copilot®) and from 2 to 16 (researchers). Based on a three-level frequency scale, AI's interpretations were especially evident in categories describing the roles of patients and families as well as practical issues related to DNAR orders. In contrast, the researchers focused on the importance of communication and documentation.
24 May 2026
Read appraisal →Studies in health technology and informatics
Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review
This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proactive and personalized care through primary (preventive care), secondary (acute care), and tertiary (chronic care) prevention levels. Following PRISMA-ScR guidelines, studies were identified from PubMed and Scopus, yielding 105 eligible studies. Most studies focused on tertiary prevention, such as predicting in-hospital mortality, complications, and functional outcomes, while fewer addressed early risk prediction or acute-phase detection. Common predictors included age, NIHSS score, glucose, stroke volume, BMI, hypertension, and diabetes, reflecting reliance on routine clinical data. Traditional ML models (e.g., logistic regression, random forest, SVM) remain dominant, although Ensemble/Hybrid and Deep Learning models have increased steadily since 2021. ML demonstrated promising predictive accuracy and clinical potential, though challenges remain in data heterogeneity, model transparency, and external validation.
24 May 2026
Read appraisal →PloS one
A qualitative study to inform the design and implementation of AI-driven diagnosis: Challenges, barriers, and clinical insights of physicians
INTRODUCTION: Acute febrile illnesses are significant clinical challenge in tropical regions due to overlapping symptomatology among infections such as dengue, scrub typhus, and leptospirosis. This diagnostic dilemma leads to delays in appropriate treatment and negatively impacted patient outcome due to limited diagnostic tool availability. Recent advancement in artificial intelligence (AI) application in healthcare help in developing diagnostic aiding tool in such clinical dilemma. AIMS: Study aimed to explore physicians' perspectives on diagnostic challenges of tropical Acute-febrile-illnesses and assess perceived utility of AI as a supportive tool in clinical decision-making. METHODS: A qualitative interview was conducted using validated semi-structured interview guide using literature and expert opinion and validated by IPR method. To understand the physicians perspective on management infectious diseases in critical care, interview guide administered among ten physicians with 10-30 years of experience across different centres of India. Interviews were audio-recorded, transcribed verbatim, and thematically analyzed using ATLAS.ti software. RESULTS: Four major themes emerged: clinical experience and practice, diagnostic-challenges, diagnostic-parameters and decision-making, and AI integration in clinical-practice. However, no statistical test can be performed since study mainly focused to understand perception of physicians with respect to managing illness and hence prevalence cannot be determined. Diseases such as dengue, scrub typhus, and leptospirosis were reported as most prevalent, particularly during monsoon seasons. Key diagnostic challenges included overlapping symptoms, diagnostic delays, limited sensitivity/specificity of available tests, and cost constraints. Physicians primarily relied on clinical judgment and laboratory findings, despite the limitations. Most of the participants expressed positive attitudes toward the potential of AI as supportive tool, citing its ability to enhance diagnostic accuracy and streamline clinical workflow with proper validation. CONCLUSIONS: Growing in clinical dilemma AI has significant role in diagnosis of tropical fever with integration of the clinical data & physician perspective can successfully able to incorporate in clinical application.
23 May 2026
Read appraisal →Lancet (London, England)
Safety and efficacy of astegolimab for COPD with frequent exacerbations regardless of baseline blood eosinophil counts (ALIENTO and ARNASA): randomised, double-blind, placebo-controlled, phase 2b and 3 trials
BACKGROUND: Interleukin-33 and its receptor, ST2, are implicated in neutrophilic and eosinophilic inflammation during chronic obstructive pulmonary disease (COPD) exacerbations. We aimed to assess the efficacy and safety of astegolimab, an anti-ST2 human IgG2 monoclonal antibody, which were evaluated in two COPD pivotal trials. METHODS: In two randomised, double-blind, placebo-controlled trials (phase 2b ALIENTO and phase 3 ARNASA), current or former smokers with COPD and a history of frequent exacerbations, irrespective of baseline blood eosinophils, were randomly assigned (1:1:1; stratification by smoking status and region) to receive subcutaneous astegolimab 476 mg every 2 weeks, every 4 weeks, or placebo, alongside optimised inhaled maintenance therapy over 52 weeks. The primary endpoint (analysed in participants receiving one or more doses) was annualised rate of moderate or severe exacerbations. Missing data were considered similar to data from other participants in the same treatment group with the same baseline characteristics. The trials were registered with ClinicalTrials.gov (NCT05037929 and NCT05595642). FINDINGS: In ALIENTO, 1301 participants (astegolimab every 2 weeks, n=433; astegolimab every 4 weeks, n=437; and placebo, n=431) initiated treatment between Oct 5, 2021, and Feb 19, 2024. In ARNASA, 1375 participants (astegolimab every 2 weeks, n=459; astegolimab every 4 weeks, n=459; and placebo, n=457) initiated treatment between Jan 9, 2023, and June 25, 2024. Adjusted rate ratios versus placebo for the primary endpoint were 0·85 (95% CI 0·72-1·00; p=0·049) for astegolimab every 2 weeks and 0·93 (0·79-1·10; p=0·38) for astegolimab every 4 weeks in ALIENTO, and 0·85 (0·72-1·01; p=0·068) for astegolimab every 2 weeks and 0·82 (0·70-0·98; p=0·024) for astegolimab every 4 weeks in ARNASA. Adverse events were balanced between treatments, with most participants experiencing one or more adverse events (1093 [84·0%] of 1301 participants in ALIENTO and 1176 [85·5%] of 1375 in ARNASA). The most common non-COPD adverse event was nasopharyngitis in ALIENTO and upper respiratory chest infection in ARNASA. Deaths occurred in 40 (3·1%) of 1301 participants in ALIENTO and in 44 (3·2%) of 1375 participants in ARNASA, and were balanced across treatment groups. Across both trials, a total of three deaths (0·1%) were considered to be related to treatment by investigators. INTERPRETATION: In ALIENTO, astegolimab every 2 weeks was associated with a lower annual rate of exacerbations versus placebo in patients with COPD and a history of frequent exacerbations. In ARNASA, these findings did not meet statistical significance. Together, these findings suggest a role for targeting the ST2/IL-33 pathway to reduce the frequency of COPD exacerbations in patients with limited treatment options. FUNDING: Genentech, a member of the Roche Group, and F Hoffmann-La Roche.
23 May 2026
Read appraisal →The journal of trauma and acute care surgery
Chilling the nerve, easing the pain?: A randomized clinical trial evaluating surgeon-administered bedside percutaneous cryoneurolysis for rib fracture pain.
BACKGROUND: A cornerstone of rib fracture management is multimodal pain control, which includes scheduled nonopioid analgesics, as-needed opioids, regional or neuraxial blockade, and surgical stabilization of rib fractures. However, adverse effect profiles and individual factors limit their use in all patients. Surgeon-administered, ultrasound-guided percutaneous cryoneurolysis performed at the patient bedside is a promising analgesic adjunct. METHODS: We performed a prospective, randomized clinical trial assessing benefit and safety of surgeon-administered percutaneous cryoneurolysis to our existing multimodal rib fracture pain control bundle (standard of care [SoC]) for injured adults aged 18 to 64 years. Patients with fractured ribs 3 to 9 were randomized within 72 hours of admission to receive either surgeon-administered, ultrasound-guided percutaneous cryoneurolysis at the bedside and our multimodal pain control bundle, or SoC alone. Patients undergoing surgical stabilization of rib fractures were excluded. The primary outcome was pain score at discharge. Secondary outcomes included hospital length of stay, intervention-associated adverse events, morphine milligram equivalent (MME) use, Short Form (SF)-12, and McGill Pain Score (Patient Reported Outcomes), which were assessed at discharge and 1-, 3-, and 12-month intervals after discharge. RESULTS: Forty-three patients were randomized: 24 in the intervention arm and 19 in the SoC arm, with 3 screen failures. The median age was 53 years (interquartile range [IQR], 45-60 years), 9 patients (23%) were female, the median Injury Severity Score was 13 (IQR, 10-17), and the median number of rib fractures was 6 (IQR, 4-8). Patients were well matched with no differences between groups. No intervention-associated adverse events were identified. Pain scores, MME use, and Patient Reported Outcomes were not different between groups at discharge, 1 month, 3 months, or 12 months. CONCLUSION: Application of surgeon-administered, ultrasound-guided percutaneous cryoneurolysis at the bedside is safe but was not associated with reduced pain or MME use when compared with SoC. ( J Trauma Acute Care Surg . 2026;100: 863-870. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level II.
23 May 2026
Read appraisal →The journal of trauma and acute care surgery
Cognitive dysfunction after polytrauma in the absence of traumatic brain injury: A systematic review of incidence.
BACKGROUND: Polytrauma patients frequently experience long-term health impacts, including cognitive impairments. While traumatic brain injury (TBI) is a recognized nonmodifiable cause, polytrauma patients are likely to face cognitive challenges potentially linked to systemic inflammation and multiple surgical interventions even in the absence of TBI. This review aims to describe the incidence and identify factors associated with cognitive dysfunction in adult multiple injury patients without Frank TBI. METHODS: A systematic search was conducted across MEDLINE, CINAHL, EMBASE, and Scopus databases on August 17, 2023, to identify studies reporting on cognitive dysfunction in adults with polytrauma, excluding brain injuries. The Critical Appraisal Skills Programme checklists guided study appraisal, and findings were narratively synthesized. RESULTS: From 2719 articles identified (including one through citation searching), 47 were fully screened, yielding 10 cohort studies for inclusion. The reported incidence of cognitive dysfunction among multiple injury patients without TBI varied widely, from 0% to 60%, with a majority (eight out of ten studies) noting incidences of 30% or higher. No consensus was found for a relationship of other studied factors with cognitive dysfunction. Injury Severity Score was found to not be associated with cognitive dysfunction in selected studies which analyzed this factor. CONCLUSION: This review suggests a high prevalence of cognitive dysfunction in multiple injury patients without TBI. The evidence base is limited by heterogeneity of the inclusion criteria, and the cognitive outcome measures. IMPLICATIONS OF KEY FINDINGS: Multiple injury is associated with long term cognitive dysfunction even without primary brain injury. This aspect of the disease of multiple injury needs further characterization to identify predictors and potential preventive and therapeutic interventions. Standardized reporting is also required to be able to monitor incidence and prevalence. ( J Trauma Acute Care Surg . 2026;100: 973-981. Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.). LEVEL OF EVIDENCE: Systematic Review; Level II.
23 May 2026
Read appraisal →European journal of pediatrics
Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study
UNLABELLED: Several severity scores have been developed to assess disease severity in infants with bronchiolitis, but they often lack objectivity and may not reliably reflect clinical outcomes. We performed a single-centre retrospective cohort study including infants younger than 12 months admitted with a clinical diagnosis of bronchiolitis over three epidemic seasons (2021-2024). Cluster analysis was applied to identify groups of infants with similar clinical patterns, which were subsequently mapped to severity levels. A machine learning (ML) model was then trained to predict these severity levels using anamnestic and clinical variables available at admission. For comparison, the bronchiolitis risk of admission score (BRAS) and the respiratory severity score-heart rate (RSS-HR) were also calculated at admission. A total of 112 infants were included, and three severity levels were identified. Mild bronchiolitis corresponded to infants not requiring NICU admission or supplementary ventilation. Moderate cases involved infants needing supplementary ventilation without NICU admission. Severe bronchiolitis was associated with both NICU admission and supplementary ventilation. BRAS showed only a weak association with the newly defined clinical severity score (τ = 0.354, p < 0.001), while RSS-HR showed no significant correlation. In contrast, the ML model demonstrated strong predictive performance (τ = 0.731, p < 0.001 in the test set). A simplified decision tree based on three admission variables-age, retractions, and oxygen saturation-also showed good predictive ability (τ = 0.654, p < 0.001). CONCLUSIONS: A new dynamic approach for assessing bronchiolitis severity at hospital admission, capable of robust and adaptable prediction across clinical settings, has been elaborated. WHAT IS KNOWN: • Several bronchiolitis severity scores exist, but many lack validation, are subjective, and have limited applicability across settings, making early and accurate severity prediction difficult. WHAT IS NEW: • Our study introduces a three-level, outcome-based severity score and a dynamic ML tool, plus a simplified ARS decision tree, all outperforming conventional scores in predicting severity at admission.
23 May 2026
Read appraisal →ACS sensors
In Situ Formation of Mechanical Interlocking Skin and Electronics Interfaces through Dual-Phase Transition of Conductive Materials
The increasing demand for daily health monitoring has accelerated the development of wearable devices. However, conventional wearables, typically based on rigid materials and external fixation methods, suffer from interfacial instability during dynamic motion and heavy sweating, leading to signal distortion and device delamination. Skin-like wearable electronics and in situ fabrication strategies have emerged to improve conformability and signal stability. However, challenges remain in achieving a stable skin-device interface and reliable performance under dynamic conditions using simplified fabrication methods. Here, we present a flexible multiparameter monitoring device that integrates with the skin through a skin-electronics interface undergoing a dual-phase transition from hydrogel to liquid and subsequently to a solid state via in situ heating and photonic crosslinking. This transition forms mechanically interlocked structures that ensure robust adhesion and stable skin contact even under humid and high-acceleration conditions. Rapid curing enabled by an integrated heating layer and optical waveguide reduces the fabrication time to 1 min, allowing efficient on-demand deployment. The device achieves stable monitoring of ECG, PPG, acceleration, and temperature signals and supports Bluetooth data transmission under accelerations up to 11 g, as well as during swimming and showering. Furthermore, machine-learning-based motion recognition and individual identification models achieve 100% accuracy. This work advances in situ-fabricated wearable electronics by improving fabrication efficiency, interfacial robustness, and environmental adaptability, providing a promising platform for portable physiological monitoring.
23 May 2026
Read appraisal →PloS one
Enhancing aviation safety: An 80-year data-driven model for classification of aviation incident and accident
The aviation system is safety-critical by nature, and any occurrence of an incident or accident can lead to the loss of human life and significant operational disruptions. The International Civil Aviation Organization (ICAO) emphasizes that every flight must take off and land safely-a goal achieved over 126,000 times daily. Despite major advancements,mishaps and accidents continue to occur, underscoring the need for robust safety management systems. The accurate classification of aviation occurrences (Incident or Accident) reports is essential for safety management, yet manual review is time-consuming and prone to inconsistency. While incident/accident labels are assigned during reporting, automated classification enables rapid triage, detection of potential mislabeling, and support for severity assessment in high-volume aviation safety operations. To address this,we developed and compared three machine learning classifiers-Multinomial Naive Bayes, Random Forest, and Support Vector Machine-using TF-IDF vectorization on an 80-year dataset of 53,770 aviation occurrence summaries obtained from the Transportation Safety Board of Canada. A two-stage evaluation strategy was employed, consisting of an initial 80/20 train-test split to create an independent test set, followed by 5-fold cross-validation applied exclusively to the training data to ensure robustness and prevent optimistic bias. The Support Vector Machine (SVM) classifier achieved the highest classification performance, attaining an accuracy of 98.06% during 5-fold cross-validation, with consistent results across folds, demonstrating its effectiveness in managing high-dimensional textual data and dataset complexity. The proposed framework provides a robust foundation for automated aviation safety report processing, offering practical value for (1) early triage of safety reports, (2) identification of potentially mislabeled cases requiring expert review, and (3) integration into downstream severity assessment pipelines. This work advances beyond prior classification studies by establishing a benchmark on the largest historical aviation safety dataset while delivering a deployable and operationally relevant framework for real-world safety management applications. The findings offer valuable insights for regulatory authorities and airline operators, contributing to enhanced safety oversight, improved response strategies, and safer aviation operations.
23 May 2026
Read appraisal →Prehospital and disaster medicine
Evaluating Nursing Students' Performance and Retention of BLS/AED Knowledge and Skills One Year After Training: A Randomized Controlled Trial
INTRODUCTION: Nurses often serve as the initial responders in cases of cardiac arrest, making cardiopulmonary resuscitation (CPR) competence essential to effective resuscitation. It is therefore important for nursing students to possess sufficient CPR knowledge and skills for future clinical practice. OBJECTIVE: The aim of this study was to examine whether CPR post-qualification training is needed in maintaining knowledge and skills and if face-to-face or online training is more effective as an educational method in preserving CPR knowledge and skills in nursing students. METHODS: A three-group, parallel randomized-controlled, single-blind design was used. The participants were 133 nursing students, randomly assigned to the face-to-face group (n = 43), the e-learning group (n = 43), and the control group (n = 47). Before randomization, all participants (n = 133) successfully completed the European Resuscitation Council (ERC) standard five-hour Basic Life Support/Automated External Defibrillation (BLS/AED) course (t0) and a questionnaire based on the 2021 ERC Guidelines to assess their knowledge. Six months later, Group A received face-to-face training, Group B received e-learning training, and Group C had no intervention. Six months after interventions, all participants (n = 133) were re-assessed on the same questionnaire, and they were given the same scenario of cardiopulmonary arrest that was used during the initial BLS course to assess the skills that they retained (t1) with the use of the ERC CPR/AED 11-item checklist. The collected data were analyzed with inferential "among group" analysis involving Kruskal-Wallis's and Pearson's Chi-Squared test and inferential "within group" analysis including Wilcoxon's Signed-Rank and McNemar's test. Multiple linear regression was used for adjusting demographic characteristics. RESULTS: Based on pairwise differences between independent groups, both intervention groups presented higher scores in knowledge at t1 (P < 0.001) and in skills at t1 (P < 0.001) compared to the control group. Moreover, both training methods demonstrated comparable effectiveness. Based on comparisons between paired groups, there was also a statistically significant decrease in the Skill Score in the control group after one year (P < 0.001). Most skills were notably higher, primarily in the e-learning group, and to a lesser extent in the face-to-face group. In specific, intervention groups demonstrated statistically significant improvement in nine of the eleven assessed skills (all P < 0.05). CONCLUSION: The current study showed that post-qualification training is needed to retain CPR knowledge and skills of nursing students, and that face-to-face and e-learning training had similar outcomes.
23 May 2026
Read appraisal →Systematic reviews
Impact of the COVID-19 pandemic on diagnosis, and healthcare utilization, among patients with cancer (lung, breast, and pancreas) and cardiovascular diseases (HF, AF, hypertensive, and chronic ischemic heart disease) in Germany: two systematic reviews.
BACKGROUND: The COVID-19 pandemic and related non-pharmaceutical interventions (NPIs) (e.g., lockdowns and contact restrictions) disrupted routine healthcare delivery. In Germany, these measures affected diagnostic and treatment services for people with cancer and cardiovascular diseases, potentially delaying diagnosis and adversely influencing outcomes. We assessed whether and to what extent diagnosis, health utilization and health outcome among patients with selected cancer and cardiovascular conditions changed in Germany during the pandemic. METHODS: We conducted two systematic reviews of studies from Germany on selected cancers (breast, lung and pancreatic) and cardiovascular conditions (atrial fibrillation/flutter, heart failure, hypertensive and chronic ischemic heart disease). Protocols were registered in PROSPERO and the reviews were reported in accordance with PRISMA. We searched PubMed, Web of Science, Cochrane Library, Scopus, and Embase and screened grey literature. Outcomes included changes in new diagnoses, healthcare utilization, treatment, and disease-specific mortality during the pandemic (2020-2023) compared with the pre-pandemic period (2018-2019). Two reviewers independently screened records, extracted data, and assessed risk of bias using an adapted ROBINS-E tool. Owing to heterogeneity, we synthesized findings narratively. RESULTS: We screened 1991 records for cancer and 4,981 records for cardiovascular diseases, and included 9 cancer studies and 10 cardiovascular studies. For cancer, several studies reported a relative reduction in new breast and lung cancer diagnoses of up to 25% during lockdown periods; hospital admissions decreased by up to 9%. For cardiovascular conditions, hospital admissions for atrial fibrillation/flutter and heart failure decreased by up to 20%, particularly during pandemic peaks. Evidence on treatment delays, changes in treatment, and mortality was limited, and outcomes for other included diagnoses were often not reported. DISCUSSION: The available evidence indicates substantial reductions in hospital admissions and new diagnoses among patients with cancer and cardiovascular disease in Germany during the pandemic, suggesting major disruptions to care delivery. However, heterogeneity and gaps in the evidence base limit a comprehensive assessment of downstream outcomes. More comprehensive, linked data and further research are needed to quantify the full pandemic's impact and to strengthen health-system resilience for future crises.
23 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis
INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intelligence (AI) is currently developing rapidly in the medical field and has a promising outlook in disease diagnosis, treatment, and prognosis. METHODS: We screened five studies that fully met the requirements from the PubMed, Web of Science, and EMBASE databases using relevant search terms such as PSE and AI, and analyzed the role of AI in predicting and diagnosing PSE in these studies. RESULTS: The results showed that the sensitivity of AI in predicting and diagnosing PSE was 88% (95% CI 0.78-0.94), and the specificity was 83% (95% CI 0.79-0.86). The area under the summary receiver operating characteristic (SROC) curve was 0.90 (95% CI 0.87-0.92). CONCLUSION: These results indicate that using AI to predict and assist in diagnosing PSE demonstrates high specificity and sensitivity, and has certain prospects in the future auxiliary diagnosis of PSE.
23 May 2026
Read appraisal →Journal of medical economics
A systematic review of acute and long-term outcomes in patients with non-transvenous implantable cardioverter-defibrillators
BACKGROUND: Non-transvenous implantable cardioverter-defibrillator (ICD) systems have been developed to address limitations of transvenous ICDs. This systematic literature review aimed to summarize periprocedural and long-term outcomes of non-transvenous ICD systems in adult patients with guideline-based indications for ICD therapy. METHODS: A systematic review was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420261327618). Eligible studies enrolled adults (≥18 years) receiving a non-transvenous ICD for primary or secondary prevention. Outcomes included periprocedural and long-term endpoints and were summarized descriptively. The Newcastle-Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) checklist for case series were used to assess risk of bias. RESULTS: Twenty-five publications representing 18 unique studies and 4,633 patients were included. Most studies were single-arm (n = 13), while 2 were randomized controlled trials and 3 were nonrandomized comparative studies. Procedural success ranged from 81%-100%, with rates ≥94% of patients in all but one early-phase study; defibrillation threshold testing success ranged from 89%-100% (≥96% in studies with n ≥ 100). Periprocedural complications were reported in 0%-5% of patients in most studies. Long-term complication rates ranged from 0%-8% at 0.5-1.5 years of follow-up and 1%-14% beyond 1.5 years. All-cause mortality ranged from 1%-5% within 6 months and increased to 16%-26% at 4-8 years. Clinical defibrillation success was consistently high (95%-100%). Incidence of inappropriate shock generally ranged from 7%-13% at 1 year and 2%-18% at later timepoints. Appropriate shock incidence ranged from 2% to 9% within 1 year and increased with longer follow-up. Risk of bias was predominantly moderate, with limited variation using NOS and greater discrimination observed with JBI. CONCLUSIONS: Non-transvenous ICD systems demonstrate favorable safety and effectiveness profiles, with high procedural and defibrillation success and acceptable complication and inappropriate shock rates across studies. Overall, the available evidence supports their use in appropriately selected adult patients with standard ICD indications. Non-transvenous implantable cardioverter-defibrillators (ICDs) are devices placed outside the blood vessels and heart, designed to treat life-threatening heart rhythm problems while avoiding complications related to transvenous ICDs.This review summarized results from 25 published studies including 4,600 patients to understand how safe and effective these devices are.Across studies, non-transvenous ICDs showed high implant procedure success, effective therapy delivery, and had generally low rates of short- and long-term complications.
23 May 2026
Read appraisal →BMJ open
Impact of COVID-19-related data drift on machine-learning prognostic models predicting 30-day opioid-related emergency department visits, hospitalisation or mortality: a population-level administrative data study in Alberta, Canada
OBJECTIVE: To develop machine-learning (ML) models during the COVID-19 pandemic and adjacent time periods to evaluate the impact of data drift on model performance. DESIGN: This prognostic study used population-level administrative health data to develop ML prediction models. SETTING: Alberta, Canada during 2019-2023. PARTICIPANTS: All patients over 18 who received at least one opioid dispensation from a community pharmacy within the province of Alberta between 2019-2023. EXPOSURE: Each opioid dispensation served as the unit-of-analysis. MAIN OUTCOMES/MEASURES: Opioid-related outcomes were identified from linked health administrative datasets. Light Gradient Boosting-machine models were developed on pre-pandemic, pandemic and endemic data and temporally validated on 2023 data (pre-pandemic model was also validated on 2020-2021 data) to predict the risk of emergency department visit, hospitalisation or mortality within 30-days of an opioid dispensation. We described key feature distributions across the study time period and changes in model prediction performance on the validation sets using relevant metrics. RESULTS: Among 1.2 million study participants representing over 13 million opioid dispensations, there were 59 809 (2.1%), 134 402 (2.4%) and 62 143 (2.3%) events reported in the pre-pandemic (2019), pandemic (2020 and 2021) and endemic (2022) time periods, respectively (estimated 2023 validation set pre-test probability of 2.8%). Notable differences in key features were observed in the 2020-2021 model relative to other years. In the 2023 validation set, discrimination performance was highest for the pre-pandemic and endemic models compared with the pandemic model (0.81, 0.83, 0.74, respectively). A similar trend regarding changes from pre-test to post-test probabilities in higher categories of predicted risk (23%, 40%, 16%) was observed. 2020-2021 had the lowest discrimination performance (0.71) and uninformative post-test probabilities (<10%). CONCLUSION: COVID-19 pandemic health data contributed to significant ML drift. Although ML approaches allow for quick re-training to mitigate drift, health regulators should approach ML prediction with caution when using pandemic-times data.
23 May 2026
Read appraisal →BMJ paediatrics open
Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.
BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromic surveillance tools, including the WHO Severe Acute Respiratory Infection definition, demonstrate limited diagnostic accuracy in children whose symptom profiles vary widely. This study aimed to develop a machine learning (ML) model to predict microbiologically confirmed vRTIs in hospitalised children and to evaluate performance across age groups and viral pathogens. METHODS: We conducted a retrospective cross-sectional study of 2050 paediatric patients (<18 years) admitted with acute respiratory infections to two tertiary paediatric hospitals in Canada. Predictors included age, sex, hospital transfer status, chronic comorbidity status and 22 presenting symptoms. The primary outcome was microbiologically confirmed vRTI, determined by multiplex PCR or rapid antigen testing. Six ML algorithms were trained and the best-performing model, identified by area under the receiver operating characteristic curve (auROC), was tested on age subgroups, viral pathogens and sites. RESULTS: Among 2050 patients (median (IQR) age 2.4 (0.8-5.2) years), 1831 (89.3%) tested positive, most commonly for respiratory syncytial virus (RSV) (38.7%) and enterovirus/rhinovirus (32.8%). Logistic regression with L2 regularisation demonstrated the best performance (auROC, 0.754; 95% CI 0.697 to 0.808; sensitivity, 69.2%; specificity, 69.9%), with greatest performance among children <1 year (auROC, 0.763) and RSV cases (auROC, 0.727). CONCLUSIONS: An ML-based logistic regression model using admission data accurately predicted paediatric vRTIs, outperforming traditional syndromic surveillance definitions, especially among infants <1 year. By integrating ML models into hospital electronic medical records, healthcare systems can achieve enhanced respiratory virus surveillance, faster outbreak detection, greater diagnostic efficiency and improved pandemic preparedness.
23 May 2026
Read appraisal →ACS sensors
Recent Advances in Electrospun Nanofiber-Based Wearable Sweat Sensors
Electrospun nanofibers, owing to their high porosity, flexibility, straightforward fabrication, and tunable physicochemical properties, have been widely employed in wearable sensors for gas detection, temperature monitoring, pulse measurement, gesture recognition, and other applications. In recent years, research on employing electrospun nanofibers in wearable sweat sensors has grown substantially. This paper provides the first comprehensive review of the research progress in electrospun nanofiber-based wearable sweat sensors, focusing on structural design, device fabrication, and potential applications. The review begins with a concise introduction to the fundamental aspects of electrospun nanofibers, including their formation principles, processing routes, material selection, and morphological characteristics. It then delves into the key components involved in constructing wearable sweat sensors based on electrospun nanofibers, such as reagent immobilization matrices, signal-enhancing materials, flexible substrates, and sweat collection components. A detailed analysis of the functional mechanisms of nanofibers in these contexts is provided, accompanied by an assessment of their advantages and limitations. Furthermore, recent applications of electrospun nanofiber-based wearable sweat sensors are systematically reviewed. Finally, the review discusses the challenges and potential future applications of electrospun nanofibers in wearable sweat sensors. This comprehensive overview aims to keep researchers informed of the latest advancements in the field and to stimulate further innovative developments.
23 May 2026
Read appraisal →Surgical and radiologic anatomy : SRA
Proximal femoral morphometry and its association with hip fracture patterns: a systematic review with meta-analysis
PURPOSES: Proximal femoral fractures (PFFs) represent a major cause of morbidity and mortality in the elderly population. Beyond bone mineral density, proximal femoral (PF) and hip joint (HJ) morphometry have been implicated in fracture susceptibility and fracture pattern. However, the relative contribution of individual geometric parameters remains incompletely defined. The purpose of this study was to systematically evaluate the association between PF and HJ morphometry and hip fracture (HF) patterns, and to assess the predictive value of radiographic morphometric parameters derived from standard anteroposterior radiographs. MATERIALS AND METHODS: A systematic review with meta-analysis was conducted in accordance with PRISMA guidelines. PubMed and MEDLINE databases were searched up to September 2025 for observational studies reporting radiographic morphometric parameters in patients with unilateral PFFs. Statistical meta-analysis was performed with random-effect models to compare intracapsular-ICF/subcapital-SCF fractures with intertrochanteric (ITF) and extracapsular (ECF) fracture patterns. RESULTS: Twenty-two studies comprising 4184 patients (77.8% female; pooled mean age 77.4 years) were included. Meta-analysis using a random-effects model revealed that ITF were significantly associated with a smaller femoral head diameter (p < 0.001) and increased medial neck cortex thickness (p = 0.021) compared with ICF. ECF demonstrated significantly longer femoral neck axis length (p = 0.012), increased horizontal offset (p = 0.048), smaller absolute offset (p = 0.026) and shorter hip axis length (p = 0.026) than ICFs. Angular parameters provided the most robust stratification. ITFs and ECFs exhibited significantly lower (more varus) neck-shaft angles (p < 0.001) and significantly higher Wiberg angles (p < 0.001) compared with the ICF group. CONCLUSIONS: PF and HJ morphometry are associated with distinct HF patterns, independently of bone mineral density. Simple linear and angular measurements obtained from standard anteroposterior radiographs may assist fracture pattern stratification, risk assessment, and preoperative planning in elderly patients.
22 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Factors associated with unplanned healthcare encounters in pediatric cancer patients.
INTRODUCTION: Pediatric cancer therapies can cause complications that drive unplanned healthcare encounters (e.g., emergency visits and unplanned admissions). Characterizing their frequency and risk factors is important for resource planning, family counseling, and targeted mitigation strategies. In a secondary analysis of data from a cluster-randomized controlled trial of symptom monitoring in pediatric cancer patients, the primary objective was to describe the characteristics of unplanned healthcare encounters. The secondary objective was to identify risk factors associated with unplanned healthcare encounters. METHODS: This was a sub-analysis of a cluster-randomized trial of 10 sites randomized to symptom screening and 10 sites randomized to usual care. We included English- or Spanish-speaking pediatric patients newly diagnosed with cancer who were 8-18 years of age receiving any cancer treatment. At symptom screening sites, enrolled participants were reminded to complete symptom screening with Symptom Screening in Pediatrics Tool (SSPedi), a validated symptom screening and assessment, three times weekly for 8 weeks. The healthcare team was notified for severely bothersome symptoms. Emergency department visits, and unplanned clinic visits and admissions were determined from families and charts. Encounter documentation was reviewed to determine if the unplanned encounter was related to SSPedi symptoms. RESULTS: Among the 444 participants from 20 participating sites, there were 652 unplanned encounters and the most common were emergency room visits (n = 269) followed by unplanned clinic visits (n = 143) and unplanned admissions (n = 240). Pain was the primary reason for emergency room visits (59, 13.3%), unplanned clinic visits (52, 11.7%), and unplanned admissions (19, 4.3%). Nausea/vomiting was the next most common symptom for emergency room visits (40, 9.0%), unplanned clinic visits (4, 0.9%), and unplanned admissions (14, 3.2%). In multivariable analysis, leukemia diagnosis (P = 0.0003), site randomization (P = 0.018), and increasing physician full-time equivalents per 100 new cancer diagnoses (P = 0.032) were associated with more unplanned encounters. CONCLUSIONS: Unplanned healthcare utilization is common among newly diagnosed pediatric cancer patients. The association between unplanned healthcare utilization and standardized symptom screening and increasing physical full-time equivalents warrant further investigation. Key aspects to the implementation of routine symptom screening among pediatric patients with cancer will be resource allocation to prevent and manage common symptoms including those that may lead to unplanned healthcare visits.
22 May 2026
Read appraisal →PloS one
A collaborative approach to applying Natural Language Processing (NLP) to Domestic Homicide Reviews (DHRs): A study protocol
Since 2011, there has been a statutory requirement in England and Wales to conduct a Domestic Homicide Review (DHR) into any domestic abuse-related death: a multi-agency review into the death of a person aged 16 or over that appears resulting from violence, abuse or neglect from an intimate partner, family member or household member. However, analyses of large numbers of DHRs are rare. One of the core challenges is the time and effort required to analyse narrative text within reports. Doing so manually is both time-consuming and resource-intensive and is a primary reason why researchers typically focus on only a portion of the available data. Natural Language Processing (NLP)-a sub-branch of artificial intelligence that enables computers to interpret and process natural language-provides a viable and scalable alternative by offsetting much of the heavy data processing to a computer. This protocol outlines a study to assess the feasibility of applying NLP to DHRs. The study will take a collaborative approach which balances the speed and scale of automation with the embedded knowledge and expertise of practitioners. This approach helps to ensure that outputs of NLP are sensitive and transparent about the biases common within datasets on violence and abuse. Based on initial consultations, we identify the priority research questions for investigation. In addition, we outline details of an ongoing collaboration with one partner, Imkaan. The protocol describes the data access, and retrieval and analysis stages before summarising how feasibility will be evaluated. We anticipate that several challenges will emerge within this study and identify strategies for mitigation. We suggest that working with practitioners who hold deep contextual knowledge about the social realities of violence and abuse, including language, risks, and experiences, mean that tools can be developed that are accountable to communities and appropriately applied to real-world problems.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Evaluating Personal and Environmental Decolonization Strategies for Children With Skin and Soft Tissue Infection and Their Households - A Randomized Clinical Trial
BACKGROUND: Staphylococcus aureus colonization predisposes to skin and soft tissue infection (SSTI). Given the waning effectiveness of decolonization, we evaluated periodic-personal decolonization and household environmental decontamination to reduce SSTIs among households affected by S. aureus. METHODS: Households of children with community-associated S. aureus SSTI participated in an open-label randomized trial. After baseline decolonization, households were randomized 1:1:1 to a 3-month decolonization intervention: (1) periodic-personal (twice weekly chlorhexidine bathing and monthly intranasal mupirocin application); (2) environmental-hygiene (weekly household surface disinfection and enhanced laundry measures); or (3) integrated-approach of both periodic-personal and environmental-hygiene. Over 9 months, participants and household surfaces were sampled at 5 study visits to detect S. aureus; SSTI incidence was recorded. The primary outcome was household-level cumulative SSTI at 3 months, comparing the integrated-approach to the combined personal-periodic and environmental-hygiene groups. RESULTS: Between 2015 and 2021, 196 index patients and their household contacts (n = 623) were enrolled. Cumulative SSTI incidence did not differ between groups during the 3-month intervention. In index patients and household contacts with SSTI in the year prior to enrollment, SSTI incidence was lower among the integrated-approach at 6- and 9-months compared to the combined periodic-personal and environmental-hygiene groups. In multivariable analyses, prior year SSTI was associated with subsequent SSTI; intervention group was not significant. All interventions reduced methicillin-resistant S. aureus, but not methicillin-susceptible S. aureus, personal colonization over 9 months. CONCLUSIONS: While an integrated-approach of prolonged personal and environmental decolonization reduced SSTIs among participants with prior year SSTI, prior SSTI strongly predicted SSTI incidence longitudinally. Novel prevention strategies are needed.
22 May 2026
Read appraisal →Psychological medicine
Global prevalence of prolonged grief disorder during the COVID-19 pandemic under standardized diagnostic frameworks: A systematic review and meta-analysis
Prolonged grief disorder (PGD), recently classified in ICD-11 and DSM-5-TR, is characterized by persistent and functionally impairing grief lasting beyond 6-12 months. The COVID-19 pandemic was accompanied by widespread mortality, social isolation, disrupted mourning rituals, and social disconnection, raising concerns about a potentially high burden of PGD during the pandemic period. We conducted a systematic review and meta-analysis, following PRISMA guidelines and PROSPERO registration (CRD42023463720), to estimate PGD prevalence under standardized ICD-11 and DSM-5-TR diagnostic frameworks and to examine potential moderators during the COVID-19 pandemic. PubMed, EMBASE, and the Cochrane Library were searched from inception to October 2024. Eligible studies included adults who experienced bereavement during the pandemic and were assessed using validated PGD instruments (PG-13-R, ICG, BGQ). Random-effects models were applied to pool prevalence estimates, with subgroup and meta-regression analyses. Thirteen studies comprising 5,766 participants were included. The pooled prevalence of PGD during the pandemic period was 24% (95% CI: 13%-36%), with the highest estimates observed in China (43%, 95% CI: 33%-54%). In the overall pooled analysis, studies applying DSM-5-TR criteria yielded lower prevalence estimates than those using ICD-11 criteria (18% vs.26%, p = 0.41). Digital interventions showed no statistically significant pooled effects (Hedges' g = -0.38, 95% CI: -0.90 to 0.14). The high and geographically heterogeneous prevalence of PGD observed during the COVID-19 pandemic underscores the need to strengthen mental health surveillance, standardized assessment, and service accessibility in large-scale public health emergencies, and provides important evidence to inform population-level interventions and resource allocation strategies.
22 May 2026
Read appraisal →Open heart
Prognostic value of CT-derived fractional flow reserve for major adverse cardiovascular events in patients with suspected or known coronary artery disease: a systematic review and meta-analysis
BACKGROUND: Fractional flow reserve derived from CT (FFR-CT) enables non-invasive functional assessment of coronary stenoses in patients with suspected or known coronary artery disease, but evidence regarding its prognostic value remains fragmented. We conducted a systematic review and meta-analysis to quantify the association between abnormal FFR-CT and major adverse cardiovascular events (MACE), updating the 2022 meta-analysis by Nørgaard et al METHODS: We searched PubMed, Embase and Scopus through December 2025 for studies comparing outcomes in patients with suspected or known coronary artery disease with FFR-CT ≤0.80 versus >0.80 (PROSPERO: CRD420261276897). Random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman adjustment was performed. Subgroup analyses examined FFR-CT technology, geography and follow-up duration. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool and certainty of evidence using Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. RESULTS: Twenty-two studies with 20 067 patients were included, representing a fourfold increase from the prior meta-analysis. Follow-up ranged from 12 to 120 months. Patients with FFR-CT ≤0.80 had significantly higher MACE risk (HR 3.94; 95% CI 2.92 to 5.31; p<0.0001) with substantial heterogeneity (I²=79.9%). However, restricting to hard endpoints (death/myocardial infarction; k=6) yielded HR 3.28 (95% CI 2.25 to 4.79) with zero heterogeneity (I²=0%). No significant differences emerged across FFR-CT technologies (p=0.812), including HeartFlow, machine learning and deep learning algorithms. Trim-and-fill analysis suggested possible publication bias, with adjusted HR 2.35. GRADE certainty was moderate. CONCLUSIONS: Abnormal FFR-CT is associated with nearly fourfold increased risk of MACE in patients with suspected or known coronary artery disease, with consistent prognostic value across technologies. The absence of heterogeneity for hard endpoints supports FFR-CT as a reliable prognostic marker. These findings address the evidence gap identified by current guidelines regarding FFR-CT prognostic utility. PROSPERO REGISTRATION NUMBER: CRD420261276897. CLINICAL TRIAL NUMBER: Not applicable (as this is a Systematic Review and Meta-Analysis and not a Clinical Trial).
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Mycobacterium tuberculosis Transmission in Healthcare Settings: A Systematic Review of Frequency, Risk Factors, and Exposure Conditions
BACKGROUND: Uncertainty regarding factors that govern Mycobacterium tuberculosis transmission in healthcare settings leads to broad and variable exposure definitions and low-yield contact investigations. We systematically reviewed data on M. tuberculosis transmission in healthcare to inform risk stratification and exposure definitions. METHODS: We searched MEDLINE, Embase, CINAHL, and Cochrane databases from inception through December 2024 for studies describing M. tuberculosis transmission from adult patients to healthcare personnel (HCP) or patients. We tabulated data on transmission frequency, circumstances, and risk factors. RESULTS: Of 6695 studies screened, 86 met inclusion criteria, encompassing 1083 source patients, 35 698 exposed individuals, and 2517 secondary cases (7.1% overall transmission risk, 3.8% among studies published since the US Centers for Disease Control and Prevention last updated healthcare ventilation standards in 2005). Transmission was associated with prolonged contact between HCP and source patients (median duration 24 hours, interquartile range [IQR] 24-25 hours) and between patients in shared rooms (median duration 24 hours, IQR 12-24 hours), clinical care without N95 respirator (1232 transmissions/10 149 exposures that included data on face covering use, 12.1%), frequent direct care (185 transmissions/410 exposures, 45.1%), and lack of negative pressure ventilation (1906 transmissions/5670 exposures, 33.6%). Only 8/116 (6.9%) transmissions with documented exposure durations were associated with <8 hours of cumulative exposure and all in poorly ventilated spaces. CONCLUSIONS: Mycobacterium tuberculosis transmission in healthcare is uncommon, particularly in spaces that meet current healthcare ventilation standards. Risk factors for transmission include prolonged exposure, close contact, lack of respirators, and poor ventilation. Defining exposures as >8 hours without a respirator should identify most transmissions and could improve the efficiency of contact investigations.
22 May 2026
Read appraisal →BMJ military health
Risk and protective factors for suicide-related outcomes among serving military personnel: a systematic review of cohort studies
INTRODUCTION: Understanding risk and protective factors for suicide-related outcomes (suicidal ideation, attempts, and deaths) among military personnel is key to the development and design of suicide prevention initiatives. Current literature has predominantly focused on ex-serving or a combination of ex-personnel and serving-personnel. Therefore, factors that may be pertinent for serving personnel are less understood. This review aims to identify risk and protective factors for suicide-related outcomes comprising serving military personnel. METHODS: The review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and was registered on PROSPERO. A systematic literature search of academic databases on military personnel and suicide from 2004 was conducted. Two independent reviewers conducted study selection. Inclusion criteria included serving personnel, cohort study design, and suicide-related outcomes. Exclusion criteria were National Guard or reservists, and studies not in English. Data on study and participant characteristics, military-specific variables, and suicide-related outcomes were extracted. All studies were critically appraised. RESULTS: A total of 53 studies were included. Most studies were published in the past 10 years, comprised army personnel from the USA, and focused on suicide attempts. Overarching risk factor categories included adverse military experiences, deployment, repeated help seeking and support, junior rank, occupation and time in service and vulnerability factors within the military context. Since many of these risk factors are inherent to military service, they may potentially be considered non-modifiable. Despite a limited number of studies on protective factors, several studies found that unit cohesion reduced the likelihood of suicidal ideation and suicide attempts. CONCLUSIONS: Suicide prevention strategies within the military may be enhanced by targeting risk factors that are potentially modifiable. Less modifiable risk factors could potentially be targeted through improved personnel management practices, particularly in preparation for and after deployment among first-year personnel. Longitudinal research programmes that identify and examine risk and protective factors for serving military personnel are needed. PROSPERO REGISTRATION NUMBER: CRD42024558183.
22 May 2026
Read appraisal →European journal of pediatrics
The effect of virtual reality glasses used during inhaler treatment on anxiety, fear and vital signs in children: a randomized controlled trial
UNLABELLED: This study aimed to determine the effect of virtual reality (VR) glasses used during inhaler treatment on anxiety, fear and vital signs in children. The study had an experimental, randomized controlled design. Participants were assigned to groups using a lottery method based on simple randomization. The random sequence was generated by physically mixing up 30 red (experimental) and 30 white (control) cards. All the data were collected and finalized by two trained researchers (Yasemin Özyer Güvener, a nurse researcher, and Şeyma Şimşirgil Kara, a pediatrician). Participants were recruited using a convenience sampling method from among individuals who met the inclusion criteria. In terms of baseline measurements, the experimental group had significantly higher mean pulse rate, fear scores, and anxiety scores (130.23 ± 14.64, 2.77 ± 1.25, and 4.60 ± 2.04, respectively) compared to the control group (118.83 ± 17.10, 2.07 ± 1.26, and 2.80 ± 2.07, respectively) (p = 0.007, p = 0.035, and p = 0.001, respectively). For post-intervention measurements, the experimental group had significantly lower mean fear and anxiety scores (0.443 ± 0.16 and 0.703 ± 0.22, respectively) compared to the control group (2.39 ± 0.16 and 3.197 ± 0.22, respectively) (p < 0.001 and p = 0.001, respectively). CONCLUSION: It was determined that VR glasses reduced anxiety and fear, increased oxygen saturation, and reduced respiratory and pulse rates. They can thus be used as an alternative method to reduce fear and anxiety levels and balance vital signs during inhalation therapy in children. WHAT IS KNOWN: • Virtual reality (VR) is an effective non-pharmacological method for reducing pain, anxiety, and fear in pediatric patients during various medical procedures. • VR applications create a sense of presence in a virtual world, limiting the impact of anxiety and fear in clinical settings. WHAT IS NEW: • Using VR glasses during the administration of inhaled medication significantly reduces anxiety and fear levels in children compared to traditional methods. • While evidence for VR use during inhalation therapy was previously limited, this study demonstrates its effectiveness as a promising alternative to improve treatment compliance and physiological responses in pediatric patients.
21 May 2026
Read appraisal →JMIR formative research
Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study
BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved. OBJECTIVE: This study aimed to investigate the application of an artificial intelligence (AI)-based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care. METHODS: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non-early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non-early warning group. Differences between groups were assessed using the Mann-Whitney U test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic). RESULTS: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non-early warning group, the early warning group had a longer hospital stay (z=-5.180; P<.001) and higher hospitalization costs (z=-6.500; P<.001), and the differences between groups were statistically significant (P<.001). Among the top 3 admission categories-respiratory, neurological, and hematologic diseases-children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all P<.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald χ²1=5.533; odds ratio 1.055, 95% CI 1.035-1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald χ²1=6.075; odds ratio 1.001, 95% CI 1.001-1.001). CONCLUSIONS: Compared with the non-early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (P<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all P<.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.
21 May 2026
Read appraisal →JBJS reviews
Medial Parapatellar vs. Transpatellar Approach in Tibial Intramedullary Nailing: A Systematic Review and Meta-Analysis of Pain and Function
BACKGROUND: Tibial intramedullary nailing (IMN) is the standard treatment for tibial shaft fractures, yet anterior knee pain (AKP) remains common, affecting nearly 47% of patients. Despite the reported advantages of suprapatellar techniques, infrapatellar approaches-medial parapatellar (MPP) and transpatellar (TP)-remain the most commonly used methods for tibial IMN. This systematic review and meta-analysis compares MPP and TP approaches regarding pain, AKP incidence, range of motion (ROM), knee function, and fracture healing. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, PubMed, Embase, Scopus, and Web of Science were searched up to November 2024. Randomized clinical trials (RCTs) and comparative observational studies in adults undergoing tibial IMN via MPP or TP approaches were included. Outcomes were AKP incidence, pain intensity, knee function, ROM, and union-related variables. Pooled mean differences (MDs) and risk ratios (RRs) were calculated using random-effects and common-effects models. RESULTS: Twelve cohort studies (782 patients) and 9 RCTs (462 patients) were included. Across cohort studies, MPP was associated with a significantly lower AKP risk at latest follow-up (RR = 0.76 [0.63; 0.90]) and at 6 months. Visual analog scale pain at 3-month postoperative also favored MPP (MD = -1.27 [-1.94; -0.60]) but did not reach the minimal clinically important difference. RCTs demonstrated similar trends toward lower pain and AKP with MPP, but without statistical significance. Functional outcomes and ROM generally favored MPP across both study designs, though findings were inconsistent and unsuitable for pooling. Fracture healing and union rates were comparable between approaches. CONCLUSION: The MPP approach for tibial IMN was linked to a lower incidence of AKP compared with the TP approach, while offering comparable or better functional outcomes, ROM, and pain intensity. These findings suggest MPP as the preferred infrapatellar approach in appropriate clinical settings. Nonetheless, high-quality studies with standardized outcome measures are essential to validate these findings. LEVEL OF EVIDENCE: Level III (systematic review of RCTs and retrospective comparative studies). See Instructions for Authors for a complete description of levels of evidence.
21 May 2026
Read appraisal →European heart journal. Acute cardiovascular care
Guided de-escalation of antiplatelet treatment in patients at high atherothrombotic risk presenting with acute coronary syndrome: a post hoc analysis of the TROPICAL-ACS trial
AIMS: Platelet function testing (PFT)-guided de-escalation of dual antiplatelet therapy (DAPT) after acute coronary syndrome (ACS) has been shown to reduce bleeding risk without increasing ischaemic events. Patients with high atherothrombotic risk (ATR) remain a challenging subgroup for such strategies. This study evaluated the safety and efficacy of early PFT-guided DAPT de-escalation according to ATR status. METHODS AND RESULTS: In the TROPICAL-ACS trial, 2610 ACS patients were randomized to standard 12-month prasugrel therapy or PFT-guided DAPT de-escalation. For this post hoc analysis, patients were stratified by ATR. High ATR was defined as age ≥ 65 years, polyvascular disease, or ≥2 risk factors (diabetes, smoking, or renal dysfunction). Hazard ratios (HRs) for clinical endpoints were derived using multistate Cox regression. High-ATR patients (n = 990) had a higher incidence of the primary net clinical benefit endpoint-composite of cardiovascular death, myocardial infarction, stroke, or Bleeding Academic Research Consortium 2-5 bleeding-compared with low-ATR patients (n = 1620) (11.0% vs. 6.7%; HR 1.67, 95% confidence interval 1.28-2.18; P < 0.001). Platelet function testing-guided de-escalation showed no significant interaction by ATR status for the primary endpoint [high ATR: 10.5% vs. 11.5%, HR 0.90 (0.61-1.32), P = 0.586; low ATR: 5.6% vs. 7.7%, HR 0.71 (0.48-1.04), P = 0.082; Pinteraction = 0.394] or ischaemic events [high ATR: 3.7% vs. 4.4%, HR 0.83 (0.44-1.56); low ATR: 1.8% vs. 2.6%, HR 0.68 (0.35-1.34); Pinteraction = 0.666]. CONCLUSION: Early PFT-guided de-escalation from prasugrel to clopidogrel was safe across atherothrombotic risk categories, supporting individualized DAPT optimization in ACS patients with both low and high ischaemic risk.
21 May 2026
Read appraisal →Biomedical microdevices
Automated blood group classification using a digital microfluidics chip and vision transformer-based image analysis
Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen-antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.
21 May 2026
Read appraisal →The Journal of clinical psychiatry
Systematic Review and Meta-Analysis of Mood Stabilizers in the Treatment of Adolescent Patients With Nonsuicidal Self-Injury in China
Objective: Nonsuicidal self-injury (NSSI) refers to deliberate, repeated acts of damaging one's own body tissue without suicidal intent. The aim of this systematic review and meta-analysis was to explore the effectiveness of mood stabilizers in treating depressive adolescent patients with NSSI in China. Data Sources: Literature published before June 30, 2025, was included. Chinese databases searched included CBM (Chinese Biomedical Database), CNKI (China National Knowledge Infrastructure), WANFANG (Wanfang Database), and CSSCI (Chinese Social Sciences Citation Index); English databases included MEDLINE, Embase, and the Cochrane Library. Searches used the following keywords: [Bipolar disorder OR depression OR NSSI] AND [Mood stabilizer OR Lithium OR Valproate OR Carbamazepine OR Oxcarbazepine OR Lamotrigine OR Antipsychotic OR Antidepressant]. Study Selection: Chinese and English literature related to the treatment of NSSI with mood stabilizers in adolescent patients in China was searched. Editorials, comments, letters, reviews, and case reports were excluded. Randomized controlled trials conducted in China comparing mood stabilizer augmentation of basic therapy versus basic therapy alone for treating NSSI were included, and studies evaluating mood stabilizer treatment for NSSI using pre-post designs were also included. A total of 9 studies were included, 4 comparative studies by control group and 5 comparative studies of before and after treatment. Data Extraction and Synthesis: Indices of depression, anxiety, impulsivity, self-injury severity, and treatment response were synthesized and analyzed. Mean differences or standardized mean differences and 95% confidence intervals (CIs) were used for symptom change. Odds ratios and their 95% CIs were also used for binary outcomes. Results: Severity of anxiety, depression, impulsivity, and self-injury was significantly reduced following treatment with mood stabilizers. Compared to treatment without mood stabilizers, anxiety, depression, and self-injury were also significantly reduced, and the response rate was significantly improved in adolescents with depression and NSSI. The incidence of self-injury in these patients was significantly reduced by mood stabilizers. Conclusion: These results demonstrate the important role of mood stabilizers in treating NSSI and suggest that mood stabilization may be more critical than mere improvement of affective symptoms.
21 May 2026
Read appraisal →Scientific reports
Analysis, control, and forecasting the dynamics of SIRD models with saturated treatment and nonlinear incidence
This study addresses the challenge of managing complex epidemic dynamics exhibited by a novel Susceptible-Infected-Recovered-Deceased (SIRD) model. The introduced model incorporates a biologically and behaviorally mediated nonlinear incidence rate alongside a saturated treatment function, realistically capturing both physical transmission risks and the human behavioral responses during an outbreak. Our comprehensive analysis establishes the mathematical rigor of the system through positivity, boundedness, and a detailed investigation of equilibrium points and bifurcations (transcritical, saddle-node, and Hopf), which reveal the potential for complex, oscillatory, and chaotic epidemic patterns. To mitigate the disease burden, we apply Pontryagin's Maximum Principle to derive optimal time-dependent prevention and treatment control strategies. Furthermore, we introduce a highly efficient machine learning framework based on a Logistic-Map Reservoir Computer (LMRC) for accurate, real-time forecasting of these complex dynamics. Numerical simulations validate the theoretical findings and reveal the model's rich dynamical behaviors, including stable limit cycles and chaotic regimes driven by parameter variations. The integrated analytical, control, and machine learning approach provides a robust toolkit for public health policymakers, offering data-driven insights crucial for designing adaptive, cost-effective interventions that directly support the achievement of Sustainable Development Goal 3 (Good Health and Well-being).
21 May 2026
Read appraisal →BMJ open
Evaluation of a deep learning model applied to chest X-rays of patients with suspected pneumonia presenting to the emergency department designed to predict admission risk: a retrospective training and prospective non-intervention validation.
OBJECTIVES: To develop, train and test a deep learning model Image-based PROgnostication applied to Chest X Rays (IPRO-X) tool that predicts the inpatient (IP) admission risk in patients with suspected pneumonia presenting to the emergency department (ED). DESIGN: The study consists of a retrospective (training) and prospective non-interventional shadow deployment (validation) of a deep learning model. SETTING: Three-hospital tertiary care system with two EDs. PARTICIPANTS: Consecutive adult patients (18 years old or older) who presented to the ED from December 2022 to February 2023 with a chief clinical complaint potentially related to pneumonia were reviewed for eligibility (n=5567), with a final number of 3677 included in the validation study. PRIMARY AND SECONDARY OUTCOME MEASURES: IPRO-X was developed using standard two-dimensional convolutional neural network InceptionNet architecture and processes chest radiographs (CXRs) acquired at admission generating a continuous value from 0 (IP-negative) to 1 (IP-positive). We examined IPRO-X's ability to predict IP admission using accuracy, area under the curve (AUC), receiver operating characteristic, sensitivity, specificity, positive predictive value and negative predictive value. IPRO-X scores were compared against observed outcomes (admission and discharge) to determine clinical utility in an ED setting. Four thresholds were defined from the retrospective phase: Youden optimal, highest specificity and thresholds equivalent to 30-day mortality rates of Pneumonia Severity Index (PSI) Risk class IV and Confusion, Respiratory Rate, Blood Pressure and Age 65 or older Score (CRB-65) scores 1 and 2. Performance was also analysed per group of chief complaints. RESULTS: In the validation set, 3677 patients (1777 (48%) female, median age 56 years (min 18, max 99)) were included. IPRO-X predicted IP admissions with an AUC of 0.795, and in the pneumonia-specific chief complaints, AUC increased to 0.828, whereas in non-related pneumonia chief complaints, the AUC decreased to 0.755. IPRO-X score was significantly higher in admitted patients compared with discharged patients (p<0.001). The accuracy of IPRO-X when using the PSI-anchored threshold was 0.751 and with the CRB-anchored threshold was 0.729, with a specificity of 0.803 and 0.968, respectively. CONCLUSIONS: IPRO-X applied to CXR of patients with signs and symptoms commonly related to pneumonia in the ED can accurately predict IP admission.
21 May 2026
Read appraisal →Journal of the American Heart Association
Incremental Prognostic Power of Coronary Flow Reserve and Artificial Intelligence-Enabled ECG-Derived Filling Pressure in Angina With Nonobstructive Coronary Arteries
BACKGROUND: Coronary flow reserve reflects microvascular function, whereas filling pressure indicates myocardial hemodynamic burden. In angina with nonobstructive coronary arteries, abnormalities in either could contribute to a supply-demand mismatch; however, their combined prognostic significance remains unclear. METHODS: Patients with angina with nonobstructive coronary arteries who underwent invasive coronary reactivity tests were studied retrospectively. Coronary microvascular dysfunction (CMD) was coronary flow reserve <2.5. An artificial intelligence-enabled ECG marker of elevated filling pressure (AIEFP) was applied as a scalable prognostic marker of myocardial hemodynamic stress. Associations with major adverse cardiovascular events were evaluated using Cox regression and propensity score overlap weighting. RESULTS: Among 1957 patients with angina with nonobstructive coronary arteries (median age, 51.8 years; 67.2% women), CMD+ was identified in 26.4% and AIEFP+ in 6.6%. Over 8 years, 204 patients (10.4%) experienced major adverse cardiovascular events. In nested models, both CMD (hazard ratio [HR]=1.42 [95% CI=1.13-1.78]; P=0.002) and AIEFP (HR=2.28 [95% CI=1.67-3.11]; P<0.001) remained associated with major adverse cardiovascular events after adjustment. Adding AIEFP to the base model improved model fitness (Δχ2=23.65, P<0.001; ΔAkaike Information Criterion=-21.7) and modestly improved discrimination (ΔHarrell concordance index=0.02, P=0.02). In a 4-group model, adjusted risks increased in a graded manner. Overlap-weighted analysis after covariate balance demonstrated distinct temporal risk trajectories: CMD+/AIEFP+ had the highest 1-year risk (12.7%), and AIEFP+ had the highest cumulative 8-year risk (57% in CMD- and 51.8% in CMD+). CONCLUSIONS: CMD and AIEFP provide complementary prognostic information, with AIEFP remaining significantly associated with outcomes after multivariable adjustment. A dual-axis framework integrating myocardial hemodynamic stress (AIEFP) and microvascular function (coronary flow reserve) may enhance risk stratification and enable identification of clinically distinct angina with nonobstructive coronary arteries phenotypes with divergent trajectories.
20 May 2026
Read appraisal →Journal of the American Heart Association
Association of Edoxaban Pharmacokinetics With Bleeding in Older Patients With Atrial Fibrillation Treated With Low-Dose Edoxaban: A Post Hoc Analysis of a Randomized Controlled Trial
BACKGROUND: Edoxaban is a direct oral anticoagulant used for stroke prevention in patients with atrial fibrillation (AF) and treatment of venous thromboembolism. We examined the relationship between trough edoxaban concentrations (E-trough) and prothrombin time (PT) and major bleeding in very old Japanese patients with atrial fibrillation and high bleeding risk. METHODS: In this post hoc analysis of the multicenter, randomized, double-blind, placebo-controlled ELDERCARE-AF (Study of DU-176b Aged 80 Years or Older) trial, patients were randomly assigned 1:1 to edoxaban 15 mg or placebo once daily. After 8 weeks of treatment, E-trough was determined and the incidence of major bleeding examined in each quartile. The incidence of major bleeding by PT was also examined. RESULTS: Data were obtained from 427 patients. E-trough (ng/mL) was ≤9.24 in the first quartile (107 patients), >9.24 to ≤13.6 in the second (111 patients), >13.6 to ≤21.6 in the third (103 patients), and >21.6 in the fourth (106 patients). Older age, lower body weight, lower creatinine clearance, and the presence of congestive heart failure were independent predictors of higher E-trough. Higher E-trough was associated with greater incidence of major bleeding (0.8%, 1.9%, 3.4%, and 4.7%/year, respectively, P=0.0408). There was a significant positive correlation between E-trough and PT (r=0.426, P<0.0001). Significantly more major bleeding events occurred in the longer (>13 seconds) versus shorter (≤13 seconds) PT subgroups (4.61% versus 0.98%/year, P=0.0060). CONCLUSIONS: Several factors were associated with higher E-trough and increased risk of major bleeding events. PT >13 seconds may be a useful predictor of the development of major bleeding. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02801669.
20 May 2026
Read appraisal →Disaster medicine and public health preparedness
Hospital Medical Surge Capacity Assessment and Planning Tools for Disaster Response: A Systematic Review and Meta-analysis
BACKGROUND: Hospital surge capacity is pivotal for disaster response, yet the effectiveness of assessment and planning tools remains underexplored. OBJECTIVE: This systematic review evaluates the reliability, validity, and applicability of surge capacity tools across disaster scenarios. METHODS: Following PRISMA guidelines, we analyzed 24 Q1 studies (PubMed, Scopus) using the PICO framework. Risk of bias was assessed via ROBINS-E, and meta-analysis (random-effects model) quantified pooled effect sizes (ES). Subgroup analyses compared simulation tools, surveys, and resource strategies. RESULTS: The combined ES was 0.74 (95% CI: 0.67-0.82), indicating moderate improvement in preparedness. Simulation tools (Group A) showed the highest consistency (ES = 0.81, I2 = 0%), while resource strategies (Group C) had wider variability (ES = 0.71, I2 = 74.42%). Egger's regression revealed publication bias (P = 0.004), and heterogeneity was substantial (I2 = 76.70%). CONCLUSIONS: Simulation-based tools are most effective for surge capacity, whereas resource strategies require contextual adaptation. Policymakers should prioritize standardized benchmarks and DSS integration, with future research addressing equity and real-world implementation gaps.
20 May 2026
Read appraisal →JAMA
Using Serious Games to Increase the Implementation of Trauma Triage Guidelines: A Randomized Clinical Trial
IMPORTANCE: Low-cost methods that improve physician adherence to clinical guidelines are lacking. Trauma triage exemplifies time-sensitive conditions where guideline adherence remains at 50% or less despite stakeholder efforts, particularly when treating older adults. OBJECTIVE: To determine whether a theory-based serious game (a purpose-driven video game) improves emergency physician adherence to trauma triage guidelines for older adults. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial recruited 800 emergency physicians responsible for the triage of Medicare fee-for-service injured patients (≥65 years of age) at emergency departments within nontrauma centers in the US. Physician enrollment began November 27, 2023, and concluded February 7, 2024. Data collection began January 1, 2024 (or the date of enrollment), and ended February 6, 2025. The patients were treated during the 1 year after physician enrollment. INTERVENTIONS: Game-based training on a tablet for an initial session of 2 hours and then a 20-minute session each quarter (4 doses; n = 400 physicians) vs usual education (n = 400 physicians). MAIN OUTCOMES AND MEASURES: The primary outcome was undertriage (defined as the proportion of severely injured patients not transferred to trauma centers) during the year after randomization. The secondary outcomes were overtriage (defined as the proportion of transferred patients with minor injuries) and a composite clinical outcome of 30-day mortality or hospital readmission. Medicare fee-for-service claims were analyzed using mixed-effects regression models. RESULTS: Of the 800 physicians, the length of experience was a median of 10 (IQR, 6-17) years in practice, 71% were male (563/789), and 750 (94%) had completed Advanced Trauma Life Support. The physicians worked at 1147 hospitals and treated 41 073 injured Medicare patients, of whom 1738 (4.2%) had severe injuries. Of the physicians in the game-based training (intervention) group, 99% (397/399) received 1 dose and 67% (268/399) received 4 doses. Physicians in the game-based training group had a lower proportion of severely injured older adults who were undertriaged (49%; 402/819) compared with the usual education (control) group (57%; 527/919) (model-adjusted difference, -7% [95% CI, -13% to -0.8%]; P = .02). The model-adjusted difference did not differ for the outcomes of overtriage (-3% [95% CI, -6% to 1%]; P = .14) and the composite outcome (-0.4% [95% CI, -5% to 4%]; P = .87). CONCLUSIONS AND RELEVANCE: Among physicians responsible for the triage of patients in emergency departments of nontrauma centers, use of a theory-based serious game decreased the proportion of severely injured patients who were undertriaged. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06063434.
20 May 2026
Read appraisal →Journal of the American Heart Association
Soluble Thrombomodulin Links Viremia and Mortality During COVID-19: Results From the ACTIV-4a Trial
BACKGROUND: COVID-19 and other respiratory viral infections can cause cardiovascular complications. SARS-CoV-2 virions are found in the blood, and circulating viral RNA levels are associated with death. We hypothesized that viremia can induce thrombotic endotheliopathy that contributes to death and studied this relationship in patients hospitalized for COVID-19 and enrolled in the ACTIV-4a (Accelerating COVID-19 Therapeutic Interventions and Vaccines) randomized trial of antithrombotic therapy. METHODS: We quantified SARS-CoV-2 nucleocapsid RNA and protein in plasma and measured their associations with clinical outcomes and biomarkers of thromboinflammation and endotheliopathy. We used Cox regression and Fine-Gray competing risk models to analyze survival and thrombosis. We conducted causal mediation analysis to explore whether thrombotic endotheliopathy mediates the relationship between viral RNA and death. RESULTS: In 93 patients, SARS-CoV-2 RNA and N-antigen were higher in nonsurvivors. Baseline viral RNA levels were associated with increased 90-day death (hazard ratio [HR], 1.31 [95% CI, 1.17-1.46]) and thrombosis (HR, 1.35 [95% CI, 1.24-1.47]). Viral RNA more effectively predicted survivorship than N-antigen levels. Soluble thrombomodulin, a biomarker of thrombotic endotheliopathy, positively correlated with viral RNA levels. Mediation analysis revealed that soluble thrombomodulin accounts for 12.2% (95% CI, 0.1%-32.3%; P=0.048) after adjustment for age and sex of the relationship between viral RNA and the 90-day mortality rate. CONCLUSIONS: Elevated plasma SARS-CoV-2 RNA levels are associated with death in ACTIV-4a, which is causally mediated in part by soluble thrombomodulin. We propose that lung-blood viral dissemination is a potential mechanism for cardiovascular complications of respiratory viruses. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04505774.
20 May 2026
Read appraisal →Emerging microbes & infections
Clinical outcomes of carbapenem therapy in OXA-48-producing Enterobacterales infections: a French multicentre cohort, systematic review, and meta-analysis
OXA-48-producing Enterobacterales (OXA-48-PE) represent a growing global health threat. Most of OXA-48-PE remain categorized susceptible to carbapenems, which might encourage their use despite uncertain clinical efficacy. This study evaluated clinical outcomes of infections caused by OXA-48-PE treated with carbapenem compared with alternative active therapies. The analyses were performed at three levels: a descriptive analysis of the French cohort, a comparative descriptive analysis of all published studies, and a meta-analysis restricted to studies providing direct comparisons between carbapenem-based and alternative active regimens. Between September 2021 and March 2023, 59 patients with monomicrobial OXA-48-PE infections were included in a French multicenter retrospective cohort. In parallel, a systematic review was conducted to identify clinical studies published through 31 December 2024, reporting outcomes of OXA-48-PE infections. In the French cohort, the overall 30-day mortality was 49.1%. Clinical failure occurred in 57.1% of patients receiving meropenem monotherapy, including patients infected with isolates exhibiting meropenem MICs within the susceptible range. Across 12 clinical studies (817 patients), carbapenem therapy was associated with high and variable crude mortality (52%), whereas newer agents active against OXA-48-PE, particularly ceftazidime-avibactam, were associated with lower and more consistent crude mortality (30.7%). In the primary meta-analysis of 6 human comparative studies, carbapenem therapy was associated with an increased risk of clinical failure compared with alternative active regimens (OR = 2.02; 95% CI = 1.05-3.88). Despite apparent in vitro susceptibility, carbapenem therapy was consistently associated with unfavourable clinical outcomes in OXA-48-PE infections, supporting the prioritization of alternative active agents whenever available.
20 May 2026
Read appraisal →Journal of the American Heart Association
Sex Differences Among Young Patients With ST-Segment-Elevation Myocardial Infarction: A Systematic Review and Meta-Analysis
BACKGROUND: Female sex is associated with lower incidence of myocardial infarction, especially at younger ages. However, sex differences in mortality and coronary artery disease patterns among young patients with ST-segment-elevation myocardial infarction are not well established. This study evaluated sex differences in in-hospital and 1-year all-cause death and in the extent of coronary artery disease in patients aged ≤45 years with ST-segment-elevation myocardial infarction. METHODS: A systematic review (December 9, 2025) was conducted using PubMed, Cochrane, Embase, major cardiology conference proceedings, and ClinicalTrials.gov. Studies comparing sexes for outcomes were included. Non-English publications, abstracts without full text, and studies published before 2010 were excluded. A random-effects meta-analysis was performed, with results presented as relative risks (RR) and 95% CIs. Heterogeneity was assessed using τ and I2, risk of bias using the Risk of Bias in Nonrandomized Studies of Interventions tool. The study was registered on the International Prospective Register of Systematic Reviews. RESULTS: Eleven studies including 738 029 patients with ST-segment-elevation myocardial infarction (22% women) were analyzed. Female sex was associated with higher unadjusted in-hospital (RR, 1.56 [95% CI, 1.40-1.74]; τ=0.04; I2=0%) and 1-year mortality rate (RR, 1.78 [95% CI, 1.50-2.12]; τ=0.00; I2=0%). Women more frequently had single-vessel disease (73% versus 60%; RR, 1.13 [95% CI, 1.03-1.23]; τ=0.07; I2=67%), while men had 2-vessel (23% versus 18%; RR, 0.76 [95% CI, 0.68-0.85]; τ=0.00; I2=0%;) and 3-vessel disease (16% versus 9%; RR, 0.49 [95% CI, 0.27-0.86]; τ=0.41; I2=33%). CONCLUSIONS: In young patients with ST-segment-elevation myocardial infarction, female sex is associated with higher unadjusted in-hospital and 1-year death despite less extensive CAD. Further studies adjusting for comorbidities and standardizing CAD definitions are needed.
20 May 2026
Read appraisal →Scientific reports
A human-centric fuzzy decision support system for medical diagnosis using fuzzy cognitive maps
Medical decision support requires models that remain interpretable under uncertainty while still adapting to evolving data and expert knowledge. Fuzzy cognitive maps (FCMs) are attractive in this setting because they encode concept-level relations in a transparent graphical form. However, static expert-defined FCMs are often too rigid, whereas purely data-driven updates may weaken semantic consistency and drift away from clinically meaningful relations. This paper proposes a human-centric adaptive framework for fuzzy cognitive map-based medical decision support. The proposed Human-Centric Fuzzy Decision Support System (HCFDSS) combines data-driven weight adjustment with an explicit expert-correction operator, allowing the causal structure of the map to be refined without discarding domain knowledge. The method is formulated through a two-stage procedure: an inner reasoning loop updates concept activations for fixed weights, while an outer learning loop updates the weight matrix using a regularized objective together with bounded expert intervention. Under standard smoothness and boundedness assumptions, we establish sufficient conditions for stability of the inner reasoning dynamics and convergence of the outer weight-update operator. A numerical illustration shows how expert feedback can revise clinically important edges and alter the final diagnostic activation in an interpretable way. In addition, the paper reports a reproducible empirical evaluation involving synthetic medical scenarios and three public medical benchmark datasets from the UCI Machine Learning Repository, together with sensitivity analysis for expert intervention, preprocessing validation, robustness checks under missingness and imbalance, and expanded comparisons with mainstream machine-learning baselines. The proposed HCFDSS is positioned not as a replacement for high-capacity black-box predictors, but as a transparent and editable decision-support framework in which clinicians can inspect, question, and refine the learned relations. The main contribution of the present work is therefore methodological and theoretical, while the empirical evaluation clarifies the predictive-interpretability trade-off of the proposed framework under reproducible benchmark settings.
20 May 2026
Read appraisal →European journal of pediatrics
Development and validation of an explainable machine learning model for predicting surgical intervention in pediatric intestinal obstruction
Pediatric intestinal obstruction, a critical acute abdomen condition, carries a risk of intestinal necrosis. Decisions for urgent surgery lack standardized criteria. This study aimed to develop an explainable machine learning (ML) model to predict surgical indication, reducing diagnostic variability. A retrospective study included 642 children from our center (split into training/internal validation sets) and 137 from two external centers. Surgical indication was defined based on the presence of one or more of the following: evidence of peritonitis on physical examination, radiographic signs of strangulation, or failure of conservative management for > 24 h; in cases of diagnostic uncertainty, consensus among three senior physicians was used as the adjudication standard. We built models using 35 easily obtainable features across eleven algorithms and evaluated performance via AUC and other metrics. SHapley Additive exPlanations (SHAP) provided model interpretability, and an optimal probability threshold was established for clinical stratification. At the optimal threshold determined by maximizing the Youden index, the model achieved a sensitivity of 89% and a specificity of 99% in the internal validation set. The random forest model demonstrated the highest discriminative performance. A streamlined, interpretable model using the eight most important features (including vital sign dynamics and clinical markers) showed no evidence of overfitting based on the close agreement between training and validation performance metrics. It achieved AUCs of 0.988 (internal validation) and 0.981 (external validation). The model has been translated into a convenient clinical tool.Conclusion: Our explainable ML model demonstrates strong discriminative performance in predicting the need for surgery in pediatric intestinal obstruction and is strongly associated with intestinal necrosis. By mitigating the "black-box" concern through SHAP, it offers transparent, interpretable decision support to clinicians.
20 May 2026
Read appraisal →Oral and maxillofacial surgery
Early versus delayed management of maxillofacial fractures: a systematic review and meta-analysis with context-based subgroup analysis
PURPOSE: Maxillofacial fractures are common injuries caused by traffic accidents, interpersonal violence, and falls. In resource-limited or remote settings, definitive management is often delayed due to limited diagnostic facilities, lack of surgical expertise, and referral constraints, raising concerns about increased complication rates. The current literature reports conflicting results. Some studies suggest that delayed management does not significantly affect outcomes, whereas others show higher complication rates and functional impairment. Therefore, this review aims to identify the associations between delayed management and complications. METHODS: This review followed PRISMA guidelines and was registered in PROSPERO. Observational studies published between 2005 and 2025 were systematically retrieved from PubMed, Europe PMC, ScienceDirect, and OpenAlex. Thirteen studies met the inclusion criteria, with nine included in the meta-analysis. RESULTS: Pooled analysis demonstrated no statistically significant difference in overall complication rates between delayed and early management (RR 1.07; 95% CI 0.72-1.60; p = 0.72), with no evidence of substantial publication bias. CONCLUSION: Although no significant difference in the occurrence of complications was found between delayed and early management of maxillofacial fractures, there was a tendency for complications such as infection, malunion, or nonunion to occur more frequently in the delayed management group.
19 May 2026
Read appraisal →Physiological measurement
EdgeECG: a lightweight edge-oriented network with dual criterion pruning for real-time ECG arrhythmia classification
Objective.Miniature electrocardiogram (ECG) devices can rapidly and accurately acquire real-time cardiac signals, enabling timely warnings for patients with heart disease. To achieve accurate arrhythmia classification on resource-constrained ECG edge devices, we propose EdgeECG, an ultra-lightweight neural network designed for deployment on low-power microcontrollers.Approach.EdgeECG is first designed with a compact convolutional architecture to ensure compatibility with resource-limited embedded platforms. A dual criterion pruning (DCP) strategy is then introduced to evaluate weight importance using both magnitude and median deviation for more precise model compression. In addition, quantization is applied to reduce storage cost and improve deployment efficiency on an STM32F103 microcontroller.Main results.Experimental results on the benchmark MIT-BIH arrhythmia dataset show that EdgeECG achieves an overall classification accuracy of 98.07%, outperforming several representative methods. In patient-independent record-level evaluation, the accuracy reaches 80.73%. The model contains only 2680 parameters and is successfully deployed on an STM32F103 microcontroller with 64 KB SRAM, achieving an inference latency of 0.025 s and an energy consumption of 0.156 mJ per inference. In addition, DCP reduces the number of non-zero parameters by nearly 50% while maintaining promising classification performance.Significance.EdgeECG provides an effective solution for five-class ECG arrhythmia classification on severely resource-constrained edge devices. Its compact architecture, effective pruning strategy, and successful deployment on an STM32F103 demonstrate its potential for low-power edge-based ECG analysis and wearable cardiac monitoring applications. The source code of EdgeECG is publicly available at:https://github.com/jyanhuang/EdgeECG-code.
19 May 2026
Read appraisal →Circulation
Prehospital Heparin Administration in Patients With STEMI Undergoing Primary PCI: HEPARIN-STEMI Randomized Controlled Trial
BACKGROUND: Primary percutaneous coronary intervention (PCI) is the preferred reperfusion strategy for patients with ST-segment-elevation myocardial infarction (STEMI). We investigated the benefits and safety of pretreatment with unfractionated heparin (UFH) in patients with STEMI referred to primary PCI. METHODS: In HEPARIN-STEMI (Pretreatment With Unfractionated Heparin for ST Elevation Myocardial Infarction)-a single-center, open-label, randomized controlled trial-patients with STEMI with ≤6 hours symptom duration were assigned to receive either a 70- to 100-IE/kg bolus of UFH at first prehospital medical contact plus a supplemental dose before PCI, adjusted to activated clothing time ≥250 seconds, or to a control group receiving standard UFH at the time of PCI. The primary efficacy end point was TIMI (Thrombolysis in Myocardial Infarction) flow grade 2-3 in the infarct-related artery at initial coronary angiography. The primary safety end point was BARC (Bleeding Academic Research Consortium) type 3-5 bleeding during the index hospital stay. RESULTS: From March 2022 to February 2025, 298 patients were randomized to UFH pretreatment and 295 to the control group. The groups were comparable in age, sex, risk factors, previous cardiovascular events, and median delay from symptoms to coronary angiography (145 versus 150 minutes; P=0.814). The median time from UFH pretreatment to coronary angiography was 60 minutes (25th-75th interquartile range, 47-55 minutes). TIMI flow grade 2-3 in the infarct-related artery was documented in 43% of patients with UFH pretreatment and 27% of patients in the control group (relative risk, 1.59 [95% CI, 1.27-1.98]; P<0.001), with no significant difference in BARC type 3-5 bleeding (2.4% versus 2.0%; relative risk, 1.16 [95% CI, 0.39-3.45]; P=0.789). CONCLUSIONS: In patients with STEMI undergoing primary PCI, in a mature STEMI network, pretreatment with UFH at first prehospital medical contact was associated with an absolute 16% increase in infarct-related artery patency without an increased risk of bleeding. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05247424.
19 May 2026
Read appraisal →Physiological measurement
Machine learning-based information flow analysis of ECG signals for long QT syndrome
Objective. Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.Approach. Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.Results. The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.Conclusions.Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.
19 May 2026
Read appraisal →World journal of urology
Supine mini-PCNL using pneumatic lithotripsy for pediatric renal stones: safety and efficacy in low-income countries
BACKGROUND: Mini-percutaneous nephrolithotomy (mini-PCNL) is an effective surgical technique for pediatric renal stones. However, data comparing different lithotripsy modalities in this population are limited. PURPOSE: To compare the efficacy, safety, and acute renal parenchymal impact of pneumatic lithotripsy versus Holmium: YAG (Ho: YAG) laser dusting during supine mini-percutaneous nephrolithotomy (mini-PCNL) in pediatric patients. PATIENTS AND METHODS: A double-masked, randomized clinical trial was conducted between March 2023 and October 2024. A total of 104 pediatric patients (ages 3-10 years) with solitary renal stones (10-25 mm) were randomized into two groups: Group 1 (n = 53) underwent mini-PCNL using a pneumatic lithotripter, and Group 2 (n = 51) underwent mini-PCNL using Ho: YAG laser dusting. Primary outcomes included operative time, stone-free rate (SFR), complications (Clavien-Dindo), and biochemical markers of renal injury, including Kidney Injury Molecule-1 (KIM-1) and albumin/creatinine (A/C) ratio. RESULTS: Pneumatic lithotripsy demonstrated significantly shorter mean operative times (59.5 vs. 67.6 min, p = 0.007) and lithotripsy times (p < 0.001) compared to the laser group. The stone-free rates were comparable between the pneumatic and laser groups (92.45% vs. 94.1%, p > 0.05). Group 1 (pneumatic) had a significantly shorter hospital stay (2.2 vs. 2.6 days, p = 0.004). While both groups showed postoperative elevations in injury markers, the laser group exhibited significantly higher and more sustained levels of urinary KIM-1 and A/C ratios, as well as higher postoperative RBC counts in nephrostomy samples (p < 0.05). No major complications or perforations occurred in either group. CONCLUSIONS: Pneumatic lithotripsy is a highly effective and safe alternative to laser lithotripsy for pediatric mini-PCNL. It offers comparable operative efficiency, reduced hospital stays, and potentially lower acute tubular injury as evidenced by lower KIM-1 levels. Given its lower cost and comparable stone-free rates, pneumatic lithotripsy is a favorable option for managing pediatric renal stones in resource-conscious settings.
18 May 2026
Read appraisal →Annals of medicine
Endovascular thrombectomy versus best medical therapy for acute vertebrobasilar artery occlusion in patients with low NIHSS scores: a meta-analysis
OBJECTIVE: The efficacy of endovascular thrombectomy (EVT) for acute vertebrobasilar artery occlusion (VBAO) presenting with mild symptoms (National Institutes of Health Stroke Scale [NIHSS] score ≤10) remains uncertain. This meta-analysis aimed to compare the effectiveness and safety of EVT versus best medical therapy (BMT) in this population. METHODS: We systematically searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception to September 2025 for comparative studies. The primary outcome was 90-day excellent functional outcome (modified Rankin Scale [mRS] score 0-1). Secondary outcomes included functional independence (mRS 0-2), symptomatic intracranial hemorrhage (sICH), and all-cause mortality. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random-effects model. RESULTS: Seven observational studies involving 3,107 patients were included. In unadjusted analyses, EVT was associated with a higher rate of excellent functional outcome (OR 2.17; 95% CI 1.58-2.96) but not with functional independence (OR 1.58; 95% CI 0.90-2.77). After adjustment for confounders, EVT was associated with higher rate of excellent functional outcome (OR 2.86; 95% CI 1.89-4.31) and functional independence (OR 1.91; 95% CI 1.01-3.62). Safety outcomes including sICH and mortality did not differ significantly between groups. CONCLUSION: In patients with acute VBAO and mild symptoms, EVT may be associated with superior functional outcomes compared to BMT alone, without a significant increase in procedural risks. These findings suggest a potential role for EVT in selected patients with low NIHSS scores and underscore the need for confirmation in randomized trials.
18 May 2026
Read appraisal →Journal of neurology
Diagnostic and prognostic value of blood neurofilament light chain in ischemic stroke: an individual patient data meta-analysis
BACKGROUND: We aimed to conduct an individual patient data meta-analysis on blood neurofilament light chain (NfL) in ischemic stroke (IS) to enhance its clinical applicability. METHODS: We performed a systematic literature search of studies on blood NfL measured in adult patients within 30 days after IS onset and derived age- and BMI-adjusted Z-scores based on a previously published reference population of healthy controls. We collected clinical, radiological and biochemical parameters of IS patients and tested associations of NfL at defined timepoints after IS onset (D1: < 24 h; D2: 24-48 h; D3: 48-72 h; D4-5: 72-120 h; D6-7: 120-168 h; D8-30: > 168 h) with baseline characteristics and 3-month follow-up outcomes (modified Rankin Scale, mRS; survival). RESULTS: We included 4081 blood NfL values from 2872 participants (IS n = 1985, transient ischemic attack n = 88, healthy controls n = 799) of 18 published studies and 3 unpublished cohorts. In patients with IS, NfL Z-score progressively increased from D1 [median: 2.0 (IQR: 0.9-2.9)] to D6-7 [median: 3.5 (IQR: 3.0-3.8)], with discriminative ability being high for IS vs. controls (AUC: 0.79-0.97) and fair for IS vs. TIA (AUC: 0.64-0.80). Higher NfL Z-score at D1 was associated with greater risk of symptomatic intracranial hemorrhage (aOR = 1.33, p = 0.014) and, from D2 onwards, with larger infarct lesion volume (highest Spearman's rho: 0.795 at D6-7). NfL independently predicted a mRS > 2 (aOR = 1.31, p < 0.001) and mortality (aOR = 1.67, p < 0.001) at 3 months. CONCLUSIONS: Blood NfL level was progressively elevated after IS, could discriminate IS from healthy controls with high accuracy and had prognostic value for intra-hospital complications and 3-month clinical outcomes in IS.
18 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam compared with imipenem-cilastatin for complicated urinary tract infection or acute uncomplicated pyelonephritis (Integral-1): a double-blind, randomised phase 3 trial
BACKGROUND: Nacubactam (OP0595) is a newly developed diazabicyclooctane β-lactamase inhibitor used in combination with cefepime or aztreonam. We assessed the efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam versus imipenem-cilastatin in complicated urinary tract infection (cUTI) or acute uncomplicated pyelonephritis. METHODS: The Integral-1 global, phase 3, multicentre, randomised, double-blind study recruited adults (aged ≥18 years) with cUTI or acute uncomplicated pyelonephritis at 79 sites in Bulgaria, China, Czech Republic, Estonia, Georgia, Japan, Latvia, Lithuania, and Slovakia. Patients were randomly assigned (2:1:1) to receive intravenous cefepime (2 g) plus nacubactam (1 g), aztreonam (2 g) plus nacubactam (1 g), or imipenem (1 g) plus cilastatin (1 g) every 8 h for 5-14 days. Randomisation was stratified by diagnosis and geographical region. The primary endpoint was the proportion of patients achieving composite clinical and microbiological success at test of cure in the microbiological modified intention-to-treat population-all patients who were randomly assigned, received any amount of the study drug, and had a baseline qualifying pathogen that was susceptible to imipenem and meropenem. The prespecified non-inferiority margin was more than 15 percentage points difference; the superiority margin was more than zero percentage points difference, for the lower bound of the two-sided 95% CI for imipenem-cilastatin. Safety was assessed in all patients who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, NCT05887908. FINDINGS: Between May 22, 2023, and Nov 26, 2024, 614 patients were randomly assigned and 431 were included in the primary efficacy analysis (cefepime-nacubactam [n=214], aztreonam-nacubactam [n=112], or imipenem-cilastatin [n=105]); 228 patients (53%) were male and 203 (47%) were female. The primary endpoint was achieved by 176 (82%) of 214, 81 (72%) of 112, and 64 (61%) of 105 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. The percentage difference in the success rate versus imipenem-cilastatin was 21·3% (95% CI 10·9 to 32·0) for cefepime-nacubactam (non-inferior and superior), and 11·4% (-1·2 to 23·7) for aztreonam-nacubactam (non-inferior). Treatment-emergent adverse events were reported in 100 (33%) of 306, 45 (30%) of 152, and 65 (43%) of 150 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. No treatment-related deaths occurred. INTERPRETATION: Cefepime-nacubactam and aztreonam-nacubactam are potential treatment options for Gram-negative cUTI and acute uncomplicated pyelonephritis, including infections caused by antimicrobial-resistant strains. FUNDING: Meiji Seika Pharma and Japan Agency for Medical Research and Development.
18 May 2026
Read appraisal →The Journal of infectious diseases
Safety, Tolerability, and Pharmacokinetics of 6-Diazo-5-Oxo-L-Norleucine in Malawian Adults With and Without Malaria: A Phase 1 Dose-Escalation Clinical Trial
BACKGROUND: Cerebral malaria (CM) is a common cause of febrile coma among African children. Despite treatment with highly efficacious intravenous artesunate, CM remains associated with high mortality and neurologic sequelae. In a mouse CM model, the glutamine antagonist 6-diazo-5-oxo-L-norleucine (DON) demonstrated robust efficacy as a potential adjuvant therapy. Before testing DON in children with CM, we investigated tolerability in African adults, including individuals with malaria. METHODS: We conducted an open-label, prospective, dose-escalation, phase 1 clinical trial of single-dose intravenous DON in Blantyre, Malawi. Participants included healthy adults and adults with uncomplicated malaria, enrolled in dose-cohorts of 10 and administered DON at 0.1, 1.0, 5.0, or 10 mg/kg. We assessed adverse events (AEs) and plasma pharmacokinetics. RESULTS: Forty healthy adults and 38 adults with uncomplicated malaria received DON. Transient, asymptomatic creatinine elevation occurred 12 hours postinfusion in 18% of all participants. Nausea and vomiting were uncommon at 0.1 and 1.0 mg/kg but occurred more frequently at 5.0 and 10 mg/kg DON. All DON-related AEs resolved rapidly, without sequelae, and did not significantly differ between healthy and uncomplicated malaria participants. In healthy adults, maximum plasma concentrations increased proportional to dose, but adults with malaria had greater than dose-proportional increases. Terminal half-life ranged from 1.7 to 4.1 hours. CONCLUSIONS: DON was well tolerated in healthy adults and adults with uncomplicated malaria. Higher doses were associated with transient gastrointestinal AEs. Pharmacokinetics were minimally influenced by malaria disease. These results provide critical data to guide dosing strategies for adjunctive DON in children with CM. Clinical Trials Registration. NCT05478720.
18 May 2026
Read appraisal →Lancet (London, England)
Ticagrelor with aspirin dual antiplatelet therapy combined with intravenous thrombolysis in patients with ischaemic stroke in China (TAPIS): a multicentre, double-blind, randomised controlled trial
BACKGROUND: Evidence supporting the early addition of antiplatelet therapy to intravenous thrombolysis in patients with acute ischaemic stroke remains inconclusive. We aimed to investigate the efficacy and safety of early oral dual antiplatelet therapy (DAPT), started within 6 h of onset, as an adjunct to intravenous thrombolysis. METHODS: TAPIS was a randomised, double-blind, placebo-controlled trial done in 60 hospitals across China. We enrolled patients treated with intravenous thrombolysis for ischaemic stroke, with a National Institutes of Health Stroke Scale score of 4-10. We randomly assigned (1:1) patients to receive oral aspirin plus ticagrelor (DAPT group) or corresponding placebo within 6 h of stroke onset, either before, during, or after receiving thrombolysis. Ticagrelor or placebo was continued for days 2-7 in each group, with open-label aspirin administered for days 2-90. Patients, clinicians, and investigators were masked to the group assignment. The primary efficacy outcome was an excellent functional outcome (modified Rankin Scale score 0-1) at 90 days. The primary safety outcome was symptomatic intracranial haemorrhage within 36 h. This trial was registered with ClinicalTrials.gov (NCT06316570) and is completed. FINDINGS: Between April 3, 2024, and Sept 30, 2025, we randomly assigned 1382 patients to the early DAPT (n=690 [49·9%]) or placebo (n=692 [50·1%]) groups. The median age was 65·6 years (IQR 58·3-72·0), 991 (71·7%) were men, and 391 (28·3%) were women. At 90 days, 474 (68·7%) patients in the early DAPT group and 429 (62·0%) in the placebo group achieved excellent functional outcomes (risk ratio 1·11 [95% CI 1·03-1·20; p=0·0089). Symptomatic intracranial haemorrhage within 36 h occurred in six (0·9%) patients in the early DAPT group versus five (0·7%) in the control group (risk ratio 1·20 [95% CI 0·37-3·93; p=0.76). INTERPRETATION: Among patients treated with intravenous thrombolysis for moderate ischaemic stroke, initiation of oral DAPT within 6 h of onset improved the likelihood of excellent functional outcomes at 90 days. Although no significant between-group difference in symptomatic intracranial haemorrhage was detected, wide CIs precluded exclusion of a small increased risk. FUNDING: National Natural Science Foundation of China, Capital's Funds for Health Improvement and Research, Noncommunicable Chronic Diseases-National Science and Technology Major Project, Beijing Municipal Science & Technology Commission, and the New Cornerstone Science Foundation.
18 May 2026
Read appraisal →Gut and liver
Global Prevalence of Functional Abdominal Pain Disorder among Children and Adolescents According to the Rome III and IV Criteria: A Systematic Review and Meta-Analysis
BACKGROUND/AIMS: Despite the global burden of functional abdominal pain disorder (FAPD) and its four subtypes (irritable bowel syndrome [IBS], functional dyspepsia [FD], abdominal migraine [AM], and functional abdominal pain not otherwise specified [FAP-NOS]), studies involving an estimation of FAPD prevalence based on the Rome III or IV criteria are limited. Therefore, we aimed to estimate the prevalence of FAPD and its four subtypes. METHODS: A comprehensive literature search was conducted in PubMed/MEDLINE, Embase, Google Scholar, and the Cochrane Library. Studies that were performed with the general population and applied Rome III or IV diagnostic criteria were included. Data were extracted to estimate the prevalence of FAPD using a random-effects model with 95% confidence intervals (CIs). Heterogeneity was assessed using the I2 statistic. The study protocol was preregistered with PROSPERO (CRD420251004116). RESULTS: The overall prevalence of FAPD was 10.89% (95% CI, 9.51% to 12.48%), with pooled prevalence rates of 11.84% (10.29% to 13.61%) with the Rome III criteria and 8.42% (6.10% to 11.62%) with the Rome IV criteria. With the Rome III criteria, IBS had the highest prevalence, while FD had the lowest. In contrast, with the Rome IV criteria, the prevalence of IBS, AM, and FAP-NOS decreased, whereas FD became the most prevalent, affecting approximately one in 23 individuals, compared to one in 51 individuals affected by IBS, one in 68 individuals affected by AM, and one in 115 individuals affected by FAP-NOS. Additionally, females exhibited higher prevalence rates of FAPD and all its subtypes than males. CONCLUSIONS: The diagnostic criteria of Rome IV are stricter than those of Rome III, which likely affects the estimated prevalence of FAPD and its subtypes.
18 May 2026
Read appraisal →Science advances
Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring-A wearable polygraph device
Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.
18 May 2026
Read appraisal →European journal of pediatrics
Prevalence of common respiratory viruses of infants with respiratory tract infections in European countries in the past decade: a systematic review and meta-analysis comparing between the pre-COVID-19, pandemic, and post-COVID-19 periods
UNLABELLED: The purpose of this study is. to determine the leading cause of respiratory tract infections over the decade; this review examined the proportions of airway viruses in infants with respiratory tract infections in Europe before, during, and after the COVID-19 pandemic. The protocol for this systematic review was registered in the PROSPERO database (CRD42024497097). Literature searches in PubMed, Embase, Scopus, and Web of Science (WoS) identified 20,453 studies reporting respiratory viral testing in Europe over the past decade (2012/13-2022/23). Eligible studies were English-language full-text or grey articles with low risk of bias and complete data, and reports that explicitly provided the number of positive airway virus cases (n, virus) and the total infant population (N). Risk of bias was assessed using the Hoy et al. scoring system. Pooled prevalence estimates (pooled proportion [95% CI]) were calculated for pre-, during-, and post-COVID-19 periods using proportional meta-analysis with the MetaProp package in R. Fifty studies before, ten studies during, and 18 studies after the COVID-19 pandemic were eligible for proportional meta-analyses of viral testing in infants. Before the pandemic, the predominant viruses in infants with respiratory infections were Respiratory Syncytial Virus (RSV) (0.48 [0.41-0.56]), Human rhinovirus (HRV) (0.24 [0.18-0.29]), and Influenza virus (IV) (0.08 [0.04-0.12]). Post-pandemic, the leading viruses were RSV (0.63 [0.51-0.75]), HRV (0.25 [0.11-0.40]), and IV (0.10 [0.00-0.20]) again. CONCLUSIONS: RSV and HRV remained the two most identified viruses in infants with respiratory infections, pre- and post-COVID-19 pandemic. Interestingly, both RSV and IV were suppressed during the pandemic. The trajectory dynamics of respiratory viruses were divergent, with resilient viruses (RSV, HRV, and IV) regaining dominance more rapidly than persistently suppressed viruses. WHAT IS KNOWN: • The COVID-19 pandemic induced an infection precautions policy, which reduced the spread of respiratory infectious diseases. • Respiratory Syncytial Virus (RSV) was estimated as one of the most common causes of respiratory infections in infants. Still, there are no reports summarising the geographical distributions of the various viral agents of infant respiratory diseases. WHAT IS NEW: • This meta-analysis shows that the prevalence of RSV and HRV infections in infants remains high in Europe following the COVID-19 pandemic and the associated precautionary policy. Furthermore, RSV proportionally experienced a marked reduction during COVID-19 pandemic (lockdown periods). However, the immunological debt may have led to a re-emergence in RSV positivity after the lockdown periods. • Our proportional meta-analysis indicates the heterogeneous post-pandemic recovery patterns of each respiratory virus commonly identified in infants, where HRV was shown to be resilient whilst RSV was rapidly rebound.
18 May 2026
Read appraisal →Computers in biology and medicine
AI unleashed: A meta-analysis transforming radiological insights in diagnosing abdominal infections
INTRODUCTION: Artificial intelligence (AI)-based imaging modalities are next-generation diagnostic devices for abdominal infections that promise to provide enhanced diagnostic speed and accuracy. This systematic review and meta-analysis critically analyze the diagnostic accuracy of AI-assisted imaging modalities, including for appendicitis, pneumoperitoneum, and cholecystitis, to present a balanced estimate of their clinical utility. METHODS: A systematic literature search was undertaken in PubMed, Scopus, and Cochrane databases to search for studies that assessed the diagnostic accuracy of AI-based imaging modalities for abdominal infections. Eleven studies were included based on the inclusion criteria, and data were pooled for analysis. Diagnostic performance was measured by estimating sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and area under the curve (AUC) using a random-effects model. Subgroup analyses were done to investigate the effect of infection type on diagnostic accuracy. RESULTS: AI-assisted imaging demonstrated an overall sensitivity of 0.891 (95% CI: 0.824-0.944) and specificity of 0.860 (95% CI: 0.784-0.922) for diagnosing abdominal infections. Subgroup analysis revealed that AI-aided computed tomography (CT) exhibited a sensitivity of 0.902 (95% CI: 0.850-0.948), while ultrasound (US) showed a sensitivity of 0.864 (95% CI: 0.792-0.922). The highest AUCs were observed for pneumoperitoneum (0.985) and appendicitis (0.947), underscoring AI's robust diagnostic capabilities across multiple pathologies. CONCLUSION: AI-imaging modalities significantly enhance diagnostic accuracy for abdominal infections, particularly for appendicitis and pneumoperitoneum. This meta-analysis underscores AI's clinical potential, though future research should prioritize multicenter studies to validate AI models' generalizability and ensure consistent performance across diverse healthcare settings.
17 May 2026
Read appraisal →Computers in biology and medicine
A unified multimodal framework for chest X-ray retrieval and disease prediction for clinical decision support
Recent advances in medical imaging and natural language processing enable new opportunities for automated diagnostic support. Chest X-rays (CXRs) remain the most common imaging modality for screening pulmonary, cardiovascular, and systemic diseases; however, the growing volume of studies and free-text reports can overwhelm clinicians. We propose a unified multimodal retrieval and prediction framework that jointly leverages DICOM-format CXRs and radiology reports by projecting visual and textual features into a shared semantic space. Trained with contrastive and multi-label objectives, the system supports disease classification, case-based retrieval, and explainable AI. Experiments on the Open-I dataset demonstrate strong performance, achieving a Macro AUROC of 0.95 and Macro F1-score of 0.71 across 22 diagnostic categories. The retrieval module attains high ranking quality (MRR and nDCG >0.93) with sub-millisecond query latency. Quantitative explainability analysis further shows strong agreement between attention- and gradient-based attribution maps (Pearson ρ≈0.92), supporting trustworthy clinical decision-making.
17 May 2026
Read appraisal →Journal of affective disorders
Adverse events related to buspirone: a real-world pharmacovigilance study using the FAERS database
Buspirone, a widely used anxiolytic, has incompletely characterized adverse effects. This study aimed to comprehensively characterize buspirone's safety profile by a large-scale analysis of real-world data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, covering Q1 2006 to Q1 2025. We collected 1744 buspirone-related adverse event reports and analyzed them utilizing various signal detection techniques, including proportional reporting ratio (PRR), reporting odds ratio (ROR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). The most common adverse events were categorized into nervous system disorders and psychiatric disorders, with "dizziness" (n = 141), "anxiety" (n = 133), "drug interaction" (n = 86), "serotonin syndrome" (n = 83), and "tremor" (n = 71) being the most frequently reported. Notably, several safety issues linked to suicide exhibited considerable signals. In addition to the adverse events already listed on the package insert, our research identified unanticipated adverse events associated with improper buspirone use, including "drug abuse" (n = 43), "intentional overdose" (n = 30), and "drug level increased" (n = 17). These findings indicated a potential risk of abuse with buspirone exposure during therapeutic use. Moreover, the study detected a relatively rare but noteworthy signal associated with abnormal body weight, such as "obesity" (n = 19), which merits further attention. The median time to onset for adverse events was 10 days (IQR:16). In conclusion, this pharmacovigilance research underscored the safety hazards related to prolonged buspirone usage, confirmed known risks and identified novel safety signals. These findings enriched the awareness of buspirone's safety profile and provided evidence-based guidance in clinical settings.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with computational complexity and real-time, heterogeneous data. To address these challenges, we propose a hybrid intelligent agent combining an enhanced Double Deep Q-Network (D2QN-JDA) with large language models (LLMs). The D2QN-JDA improves learning stability and adaptability through joint state-action inputs, dynamic exploration rate, and adaptive reward normalization. The LLM component uses Retrieval-Augmented Generation (RAG) to integrate structured and unstructured data for real-time decision support. Experiments based on data from the Hong Kong COVID-19 outbreak show that the D2QN-JDA outperforms dynamic programming, greedy algorithms, genetic algorithms, and Q-learning, achieving reductions in cost. The LLM component also outperforms manual and regex methods in both single- and multi-point data extraction, enhancing accuracy, recall, F1 score, cost, and time. Our framework effectively addresses complex, multi-objective resource allocation in public health crises.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits considerable heterogeneity, presenting substantial challenges for clinicians in establishing realistic goals. While integrative AI paradigms have shown success in other complex medical domains, existing models often rely on single-modality data, limiting their predictive accuracy and clinical utility. This study aims to develop and validate a multimodal predictive model integrating CT imaging, clinical data, and rehabilitation assessments to simultaneously predict motor recovery and global rehabilitation outcomes following cerebral hemorrhage. METHODS: We conducted a retrospective study involving 739 patients (315 for motor function prediction and 424 for rehabilitation outcome assessment) who received rehabilitation therapy after cerebral hemorrhage. To predict motor function, we constructed a late-fusion deep learning model leveraging 3D-DenseNet for CT neuroimaging and Multi-Layer Perceptron (MLP) for clinical and laboratory features. To predict rehabilitation outcomes, a Gradient Boosting Decision Tree (GBDT) model was developed and validated using 5-fold and 10-fold cross-validation, comparing it against other machine learning algorithms, including SVR, Random Forest and AdaBoost. Model performance was assessed using metrics including AUC and R². Additionally, univariate and multivariate regression analysis were performed to identify significant factors influencing motor recovery and rehabilitation outcomes. RESULTS: A total of 739 patients were included. The multimodal fusion model achieved an AUC of 0.856 (95 % CI: 0.741-0.971) and an F1 score of 0.897 (95 % CI: 0.819-0.975), significantly outperforming the imaging-only (AUC: 0.833) and clinical-only (AUC: 0.749) models. For rehabilitation outcome prediction, the GBDT model achieved an R2 of 0.849 (95 % CI: 0.803-0.887), demonstrating superior stability and accuracy over other models. Additionally, multivariate analysis revealed that serum albumin (ALB), neutrophil percentage (NEUT%), triglycerides (TG), and thrombin time (TT) were independent predictors of motor recovery, while age, admission mBI, and time to start rehabilitation significantly influenced functional outcomes. CONCLUSION: This study confirms that a multimodal deep learning framework integrating routinely available CT imaging and clinical biomarkers provides high predictive value for simultaneously forecasting motor recovery and global functional outcomes after ICH. This proof-of-concept approach offers a reproducible, data-driven tool for early risk stratification, facilitating the formulation of individualized rehabilitation strategies and optimizing resource allocation in clinical workflows.
17 May 2026
Read appraisal →PloS one
Multi class photoplethysmography-based deep model for cardiovascular disease classification
BACKGROUND: cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact. METHODS: A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min-Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR). RESULTS: The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen's Kappa of 0.8968, indicating "almost perfect" agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with <5 ms per segment on consumer-grade hardware, confirming feasibility for real-time applications. CONCLUSION: The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.
17 May 2026
Read appraisal →PloS one
D-dimer and lower limb ultrasound as prognostic factors for recurrent deep venous thrombosis and pulmonary embolism: A systematic review and meta-analysis
Venous thromboembolic disease is a chronic, recurrent condition. The optimal duration of anticoagulation therapy remains uncertain. We aim to evaluate D-dimer and lower limb ultrasonography as prognostic tools for the recurrence of venous thromboembolism. A search was conducted on May 28, 2022, in the Medline, Embase, and Cochrane databases. Inclusion criteria encompassed cohort studies, case-control studies, and clinical trials. Two reviewers independently screened all records and analyzed studies for inclusion/exclusion criteria, as well as risk of bias, using a structured framework (PROSPERO ID: CRD42022341082). The initial search yielded 4652 titles and abstracts. After removing 777 duplicates and reviewing 3875 titles and abstracts, 48 articles providing information on D-dimer and/or lower limb ultrasonography as prognostic factors were finally included. Very low-certainty evidence suggests that both residual vein thrombosis (RVT) at anticoagulation discontinuation and a positive post-treatment D-dimer are significantly associated with an increased risk of recurrent venous thromboembolism (VTE). For RVT, the pooled analysis showed a two-fold increase in risk (OR 2.00, 95% CI 1.02 to 3.94; I² = 85.7) while for positive D-dimer, the risk was similarly elevated (OR, 2.48; 95% CI, 1.85-3.33; I² = 56.0). In conclusion, very low-quality evidence suggests that abnormal D-dimer and RVT are associated with recurrent VTE; however, this association is inconsistent due to significant heterogeneity and wide prediction intervals. These biomarkers should be interpreted with caution as isolated predictors in clinical practice.
16 May 2026
Read appraisal →African journal of reproductive health
Domestic violence during pregnancy and its maternal and reproductive health consequences in mainland China: A systematic review and meta-analysis
Domestic violence during pregnancy represents a serious public health concern. This study conducted a systematic review of quantitative data to examine its prevalence, factors influencing it, and associated health outcomes in Mainland China. Following PRISMA 2020 guidelines, a meta-analysis of 25 studies was performed using random-effects models, alongside assessments of heterogeneity (I²) and publication bias. The analysis revealed a pooled prevalence of approximately 20%, with psychological violence identified as the most prevalent form. Key contributing factors included socio-economic disadvantage, restricted decisionmaking autonomy, and risky behaviors by partners. Exposure to domestic violence substantially heightened the likelihood of antenatal depression or anxiety, pregnancy complications, and negative birth outcomes such as low birth weight and preterm delivery. Although some heterogeneity was noted, the associations were consistently observed. These findings highlight the significant prevalence and severe health impacts of domestic violence during pregnancy, emphasizing the urgent need for comprehensive maternal healthcare approaches in China. Such strategies should integrate domestic violence screening, mental health services, and initiatives promoting socio-economic empowerment. La violence domestique pendant la grossesse constitue un problème de santé publique grave. Cette étude a réalisé une revue systématique des données quantitatives pour examiner sa prévalence, les facteurs l'influençant et les conséquences sanitaires associées en Chine continentale. Conformément aux directives PRISMA 2020, une méta-analyse de 25 études a été effectuée en utilisant des modèles à effets aléatoires, ainsi que des évaluations de l'hétérogénéité (I²) et du biais de publication. L'analyse a révélé une prévalence globale d'environ 20 %, la violence psychologique étant identifiée comme la forme la plus répandue. Les principaux facteurs contributifs comprenaient les désavantages socio-économiques, une autonomie décisionnelle restreinte et les comportements à risque des partenaires. L'exposition à la violence domestique augmentait considérablement la probabilité de dépression ou d'anxiété anténatales, de complications de la grossesse et d'issues défavorables à la naissance, telles qu'un faible poids de naissance et un accouchement prématuré. Bien qu'une certaine hétérogénéité ait été notée, les associations ont été constamment observées. Ces résultats soulignent la prévalence significative et les impacts sanitaires graves de la violence domestique pendant la grossesse, mettant en évidence le besoin urgent d'approches globales en matière de soins de santé maternelle en Chine. De telles stratégies devraient intégrer le dépistage de la violence domestique, des services de santé mentale et des initiatives favorisant l'autonomisation socio-économique.
16 May 2026
Read appraisal →Journal of global health
Impact of travel time to health facilities on perinatal outcomes: a systematic review with narrative synthesis and meta-analysis
BACKGROUND: Evidence-based global guidance on safe travel time for small or sick newborns who require transfer to health facilities after birth is lacking. A two-hour threshold is frequently cited in low- and middle-income countries (LMICs), while 30 minutes is commonly used in high-income countries (HICs). Although these thresholds are widely referenced, their empirical basis and consistency across levels of newborn care and journey types have not been systematically examined. This study synthesises the evidence linking travel time and perinatal outcomes. METHODS: We conducted a systematic review with narrative synthesis and meta-analysis to assess the impact of travel time or distance (home-to-facility or interfacility) on stillbirth, perinatal mortality, and neonatal mortality. We searched Embase, MEDLINE, and Cochrane Central Register of Controlled Trials for published studies from 2014 to 2023. Given substantial methodological heterogeneity, we used narrative synthesis as the primary analytical approach and conducted random-effects meta-analyses where studies were sufficiently comparable (≥2 with similar definitions and outcome windows), pooling effect estimates for travel time thresholds of 30 minutes, 1 hour, or 2 hours and travel distances of 5, 10, and 15 km. We assessed bias using the Newcastle-Ottawa Scale for cohort and case-control studies. RESULTS: Of 8317 screened records, 166 were eligible for full-text review, with 37 studies meeting the inclusion criteria- All but two had low or moderate risk of bias. Most studies (n = 26) came from LMICs and documented higher perinatal survival with shorter journeys. Studies from HICs demonstrated lower out-of-hospital birth, lower morbidity, and lower mortality with shorter journeys though associations were weaker. Across the narrative synthesis, shorter travel times were consistently associated with better outcomes. Exploratory pooling suggested a greater than 3-fold higher odds of survival for interfacility journeys under 30 minutes (odds ratio (OR) = 3.25; 95% confidence interval (CI) = 1.90-5.57) and over 2-fold higher odds of survival for journeys from any location to hospitals at all thresholds (OR = 2.06; 95% CI = 1.60-2.65 for 2 hours; OR = 2.20; 95% CI = 1.46-3.33 for 1 hour; OR = 1.92; 95% CI = 1.10-3.34 for 30 minutes), though prediction intervals were wide, reflecting methodological and contextual diversity. CONCLUSIONS: We found that shorter journeys were associated with better perinatal outcomes, with the highest survival rates observed for journeys under 30 minutes to the hospital. Due to substantial contextual and methodological heterogeneity, pooled estimates should be interpreted as illustrative, rather than definitive. A travel time norm of 30 minutes or one hour is preferable to the two-hour threshold currently used in LMICs. To safeguard perinatal survival rates, any travel-time standard should be balanced with corresponding quality of care standards. REGISTRATION: PROSPERO: CRD42023460423.
16 May 2026
Read appraisal →European heart journal
Foundation models for electrocardiogram interpretation: clinical implications.
BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model. METHODS: Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements. RESULTS: DeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs). CONCLUSIONS: This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.
16 May 2026
Read appraisal →European heart journal
Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department
BACKGROUND AND AIMS: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusive electrocardiogram (ECG) findings or biomarker results. A deep learning model to detect ECG patterns associated with revascularization likelihood was developed, aiming to guide further assessment and reduce diagnostic uncertainty. METHODS: A convolutional neural network model was trained on 144 691 ED visits from a US cohort (60 ± 19 years; 53% female; 0.6% revascularization), tested on a separate test cohort (n = 35 995), and benchmarked against clinician ECG interpretation and cardiac troponin T (TnT). External validation was performed for the outcomes revascularization and Type 1 myocardial infarction (MI) on 18 673 ED visits from Europe (55 ± 21 years; 49% female; 1.5% revascularization; 1% Type 1 MI). Primary performance metric was area under the receiver operating characteristic curve (AUROC). RESULTS: In the test cohort, the model achieved an AUROC of 0.91 (95% confidence interval [CI] 0.91-0.91), outperforming clinician ECG interpretation (AUROC 0.65, 95% CI 0.54-0.76) and conventional cardiac TnT (AUROC 0.71). In the external validation cohort, ECG model AUROC was 0.81 (95% CI 0.81-0.82) for revascularization, and 0.85 (95% CI 0.84-0.85) for Type 1 MI, compared with 0.70 (95% CI 0.57-0.83) and 0.74 (95% CI 0.56-0.92) for clinician interpretation, and 0.85 and 0.87 for high-sensitivity (hs)-TnT, respectively. The ECG model had higher specificity but lower sensitivity compared with high-sensitivity-troponin T. CONCLUSIONS: The model was able to detect revascularization and Type 1 MI with competitive performance, suggesting a potential role to complement current clinical assessment.
16 May 2026
Read appraisal →JMIR formative research
Use of Commercially Available Large Language Models to Generate Information Leaflets on Post-Intensive Care Syndrome: Clinical Utility Assessment
BACKGROUND: Patients and their families without medical knowledge may find professional health care information difficult to understand. The use of large language models (LLMs) to simplify and translate complex medical content holds promise for improving comprehension while reducing the burden on health care providers tasked with delivering explanations. OBJECTIVE: This study aims to evaluate the quality of information leaflets generated using commercially available LLMs. METHODS: Informational texts on post-intensive care syndrome were generated using 6 different LLMs and 4 prompt designs with varying levels of instructional guidance. Clinical practice guideline documents were uploaded and provided to the models as reference context, reflecting a pragmatic clinical scenario without model tuning or advanced retrieval pipelines. In total, 72 texts were generated (6 models × 4 prompts × 3 outputs). After excluding texts shorter than 500 characters (n=16) and those without explicit mention of post-intensive care syndrome (n=3), 53 texts remained. To enable balanced human evaluation across model-prompt combinations, the longest eligible response from each pair was selected (4 prompts × 4 models; n=16). Following independent expert review by 2 medical specialists, 7 texts were excluded, leaving 9 texts for final analysis. Ten individuals, including health care professionals and nonmedical personnel, assessed the texts using a 10-point Likert scale across multiple quality domains. An LLM-based parallel assessment was also conducted, and scores were compared across models and evaluator groups. RESULTS: In the human evaluation of the selected 9 texts, the generated texts achieved an average score of 6.8 or higher across all evaluation criteria, with no potentially harmful content identified. The text generated by LLaMA 3 70B, using a step-by-step approach combined with text-augmented prompting based on clinical guidelines, received the highest overall score, whereas the lowest-rated text was produced using a simple prompt without text augmentation. Although no consistent trends were observed across LLMs or prompt engineering strategies, text-augmented prompting was generally associated with higher evaluation scores. Ratings differed between professional and nonprofessional evaluators. Given the feasibility-driven screening process and the resulting limited sample size, the findings should be interpreted as exploratory and descriptive rather than definitive estimates of overall model performance. CONCLUSIONS: Among the selected texts included in the final human evaluation, informational materials generated using commercially available LLMs were generally rated as acceptable by human evaluators, and none contained harmful content. These findings suggest that LLMs may support the development of patient-facing informational materials under feasibility-constrained conditions, although further validation with larger and more diverse samples is warranted.
16 May 2026
Read appraisal →Spine
Automated Classification of Cervical Spinal Stenosis Using Deep Learning on Computed Tomography Scans
STUDY DESIGN: Retrospective study. OBJECTIVE: To develop and validate a computed tomography-based deep learning (DL) model for diagnosing cervical spinal stenosis (CSS). BACKGROUND: Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize health care resource allocation. MATERIALS AND METHODS: Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The 2-stage model architecture employed: (1) A Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI), (2) Comparison of 16 Convolutional Neural Network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience. RESULTS: In the multiclass classification task, 4 high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians. In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All 4 models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, 3 models (excluding DL4-26d) exhibited consistency rates exceeding 90%. CONCLUSIONS: The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.
16 May 2026
Read appraisal →The Journal of infectious diseases
Cytomegalovirus DNAemia in Hospitalized Adults With SARS-CoV-2 Infection Requiring Supplemental Oxygen: Virologic and Clinical Characteristics and Association With Outcomes
BACKGROUND: Cytomegalovirus (CMV) reactivation occurs in the context of coronavirus disease 2019 (COVID-19); however, the viral kinetics, risk factors, and clinical outcomes are poorly defined. METHODS: We examined the association of CMV DNAemia with clinical outcomes among participants of a randomized trial of remdesivir with or without baricitinib (National Institute of Allergy and Infectious Diseases [NIAID], Adaptive COVID-19 Treatment Trial 2 [ACTT-2]). Plasma CMV DNAemia from CMV-seropositive participants with COVID-19 (NIAID ordinal scale [OS] 5, 6, or 7 at entry) were assessed longitudinally by quantitative polymerase chain reaction. Factors associated with CMV DNAemia, and clinical outcomes were analyzed by Cox regression and proportional odds models. RESULTS: Of 772 trial participants with available samples, 643 (83%) were CMV seropositive. Baseline CMV serostatus was not associated with COVID-19 outcomes. The cumulative incidence of CMV DNAemia among seropositive persons by day 28 was overall 11% (baseline OS 5, 6.3%; OS 6, 16.4%; OS 7, 24.7%), and was associated with older age, baseline OS, male sex, lymphopenia, and systemic corticosteroid use, while remdesivir and baricitinib did not affect risk. CMV DNAemia was associated with a lower probability of improvement by day 29 (adjusted hazard ratio, 0.3 [95% confidence interval, .17-.56]), with a more pronounced delay of recovery with higher CMV viral load. CMV DNAemia was also associated with higher severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and death. CONCLUSIONS: In hospitalized adults with COVID-19 requiring oxygen, CMV viremia occurs within well-defined clinical risks and is independently associated with delayed recovery from illness, higher SARS-CoV-2 viral load, and increased mortality.
16 May 2026
Read appraisal →Journal of neurointerventional surgery
Long-term outcomes of the Woven EndoBridge device for intracranial aneurysms: systematic review and meta-analysis
BACKGROUND: The Woven EndoBridge (WEB) device is widely recognized as a safe and effective treatment for intracranial aneurysms (IAs). However, data on the long-term evolution of aneurysm occlusion remain limited. This meta-analysis evaluates the long-term (≥1 year) safety and efficacy outcomes of patients treated with the WEB device. METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 1, 2011 and August 1, 2024 following PRISMA guidelines. Studies reporting outcome for IAs treated with the WEB device and a follow-up period of ≥1 year were included. Data were pooled using a random-effects model to account for variability across studies. RESULTS: A total of 25 studies including 1735 patients and 1768 aneurysms were analyzed (30.1% ruptured). The pooled adequate occlusion rates were 87.3% (95% CI 83.5% to 91.1%) at 1 year, 90.7% (95% CI 86.2% to 95.2%) at 1-2 years, 93.3% (95% CI 87.7% to 99.0%) at 3 years, and 88.4% (95% CI 80.5% to 96.4%) at 5 years. Adequate occlusion was achieved in 89.2% (95% CI 83.0% to 93.2%) of unruptured aneurysms and 85.9% (95% CI 80.2% to 90.1%) of ruptured aneurysms. The overall retreatment rate at the last long-term follow-up was 7.2% (95% CI 4.8% to 9.6%). No procedure- or device-related adverse events were reported beyond 1-year follow-up. Additionally, no cases of delayed rupture or aneurysm rebleeding were observed. CONCLUSION: This systematic review and meta-analysis confirms the high long-term efficacy and safety of the WEB device in treating both ruptured and unruptured aneurysms. The findings support the WEB device as a reliable and effective treatment option for IAs.
16 May 2026
Read appraisal →Journal of neurointerventional surgery
Intravenous tenecteplase is associated with a higher incidence of thrombus movement before endovascular treatment for acute ischemic stroke: a secondary analysis of the AcT randomized controlled trial
BACKGROUND: Thrombus migration can occur in patients receiving IV thrombolysis for acute stroke. This study compared the effects of IV tenecteplase (0.25 mg/kg) versus alteplase (0.9 mg/kg) on thrombus movement in patients undergoing endovascular treatment (EVT) in the AcT trial. METHODS: Patients with intracranial occlusion on baseline CT angiography (CTA) who underwent EVT were included. Thrombus movement from baseline CTA to first angiographic EVT run was classified as no thrombus movement, thrombus extension (TE, movement to a proximal location on DSA compared with CTA), thrombus migration (TM, movement to a more distal location), or recanalization. Outcomes were 90-day modified Rankin Scale (mRS) score 0-2 and final extended Thrombolysis in Cerebral Infarction score 2b-3. Mixed-effects logistic regression was performed. RESULTS: Of the 1577 patients in the AcT trial, 496 patients with intracranial occlusions underwent EVT (median age 73 years, 50.8% female). No thrombus movement was seen in 398 patients (80.2%), TE in six (1.2%), TM in 77 (15.5%), and complete recanalization in 15 (3.0%). Tenecteplase recipients (n=252) had higher TM rates than alteplase recipients (19.4% vs 11.5%, adjusted OR (aOR) 1.83, 95% CI 1.10 to 3.07). TM did not impact final successful reperfusion (aOR 0.76, 95% CI 0.40 to 1.38) but was associated with increased odds of 90-day mRS 0-2 (aOR 1.77, 95% CI 1.05 to 3.06). Thrombolytic type did not affect the relationship between thrombus movement and study outcomes (P>0.05). CONCLUSION: IV tenecteplase is associated with higher thrombus migration rates before EVT compared with alteplase, which is linked to better functional outcomes. However, the type of thrombolytic agent did not affect final reperfusion and functional outcomes.
16 May 2026
Read appraisal →Journal of affective disorders
Systematic review and meta-analysis of intranasal esketamine for treatment-resistant depression: Evidence from real-world studies
BACKGROUND: Intranasal esketamine has demonstrated efficacy and safety for treatment-resistant depression in randomized controlled trials, however the generalizability of these findings to clinical practice remains unclear. This systematic review and meta-analysis aimed to synthesize real-world evidence of intranasal esketamine in patients with treatment-resistant depression. METHODS: A systematic search was conducted in PubMed, Embase, and PsycINFO up to April 2025 to identify observational studies reporting clinical outcomes of intranasal esketamine in adults with treatment-resistant depression. Standardized mean changes in depressive symptoms and odds ratios for remission were calculated. Random-effects meta-analyses were performed, followed by meta-regression to explore potential moderators. The study protocol was prospectively registered in PROSPERO (CRD420251025075). RESULTS: Nine studies were included in the final synthesis. The analyses demonstrated that intranasal esketamine showed substantial effectiveness, with an effect size of Hedges' g = -1.98 (95% CI -2.94 to -1.03; p < 0.0001). Patients were about five times more likely to achieve remission at three months compared with the induction phase (OR = 5.1, 95% CI 2.29-11.39). Treatment effectiveness was not influenced by factors such as gender or the presence of comorbidities, such as anxiety, personality, or substance use disorders. The pooled prevalence of any adverse event, based on studies that reported safety data, was 82% (95% CI 69-92). The most frequent was dissociation, reported in 49% of patients (95% CI 37-62). CONCLUSIONS: This study provides consistent real-world evidence supporting that Intranasal esketamine was associated with meaningful symptom reduction and rising remission rates over time in real-world settings. Given the observational nature of the evidence and the absence of control groups, effect sizes should be interpreted with caution. Nonetheless, these findings provide valuable insight into esketamine's effectiveness and tolerability in routine practice and highlight the need for controlled comparative studies to guide long-term treatment strategies.
15 May 2026
Read appraisal →Neurology. Clinical practice
Optimizing Antiseizure Medication Taper in the Epilepsy Monitoring Unit
BACKGROUND AND OBJECTIVES: Inpatient video-EEG epilepsy monitoring aims to provide a safe environment for antiseizure medication (ASM) tapering to precipitate seizures, which is often required for diagnosis and treatment. However, there is no standardized taper protocol across institutions, which may contribute to increased morbidity through longer hospitalizations if the taper is too mild and bilateral tonic-clonic seizures (BTCSs) if too aggressive. This study investigates how ASM taper influences length of stay (LOS) and patient outcomes in the epilepsy monitoring unit (EMU), aiming to develop guidelines for medication taper strategies to maximize diagnostic yield while minimizing patient risk. METHODS: We used natural language processing to extract data from electronic medical records of 639 patients admitted to the University of Pennsylvania EMU between 2017 and 2023. We estimated ASM load, the total normalized sum of all ASM serum concentrations based on dose-dependent pharmacokinetic models, throughout each hospitalization. We applied LASSO (Least Absolute Shrinkage and Selection Operator) regression and mixed-effects models to identify predictors of LOS and BTCS risk as a function of taper speed and percentage of baseline ASM load in patients with (hBTCS) and without (nBTCS) a history of BTCS. RESULTS: Taper speed did not correlate with BTCS risk, although absolute ASM load did. Median ASM loads at seizure occurrence were 51% of baseline in the hBTCS group, 41% in the nBTCS group, and 39% in the unclear group. BTCS occurred at higher ASM loads in patients with hBTCS vs nBTCS. Median ASM loads at first EMU seizure were 33% for both hBTCS and nBTCS groups, while the unclear group had first seizures at lower loads (25% of baseline). Time to first seizure and number of baseline ASMs were positively correlated with longer hospital stays, suggesting that sequential medication taper delayed seizures and prolonged LOS without altering BTCS risk. DISCUSSION: Patient history and ASM load are critical factors in predicting BTCS. Rapid taper to approximately 50% baseline ASM load may more efficiently produce seizures without increased BTCS risk in patients without BTCS history. Individualized taper protocols remain advised, particularly for severe epilepsies. This study provides a foundation for quantitative, evidence-based guidelines to optimize ASM reduction strategies for safely precipitating seizures in EMUs.
15 May 2026
Read appraisal →Renal failure
Comparative efficacy and safety of apixaban and rivaroxaban versus warfarin in atrial fibrillation patients with end-stage renal disease: a systematic review and meta-analysis
This study aimed to evaluate the comparative efficacy and safety of apixaban and rivaroxaban versus vitamin K antagonists (VKAs) in anticoagulation management in a dialysis population. PubMed, Embase, and the Cochrane Library were searched for studies comparing apixaban or rivaroxaban with VKAs in patients with atrial fibrillation (AF) undergoing dialysis. The primary efficacy endpoints included stroke/systemic embolism (SSE) and all-cause mortality. Safety outcomes encompassed major bleeding, intracranial hemorrhage, and gastrointestinal bleeding. Risk ratios (RR) with 95% confidence intervals (CI) were synthesized using random-effects models. The meta-analysis included three randomized controlled trials (RCTs) and eight observational studies. Pooled analyses showed that apixaban and rivaroxaban were associated with lower risks of major bleeding (RR 0.57, 95% CI: 0.51-0.63), gastrointestinal bleeding (RR 0.66, 95% CI: 0.57-0.76), and intracranial hemorrhage (RR 0.54, 95% CI: 0.36-0.83) compared with VKAs. Additionally, apixaban and rivaroxaban were associated with reduced risk of SSE (RR 0.57, 95% CI: 0.46-0.72) and all-cause mortality (RR 0.73, 95% CI:0.63-0.83), although substantial heterogeneity was present. Exploratory dose-stratified analyses suggested both standard- and low-dose apixaban regimens were associated with favorable efficacy and hemostatic safety relative to warfarin. Consistent numerical trends were observed in the RCT-only analysis, though none reached statistical significance owing to limited sample size. In conclusion, apixaban and rivaroxaban are associated with lower risks of bleeding compared with VKAs in patients with AF and ESRD. However, evidence regarding their efficacy in preventing SSE, all-cause mortality and the optimal apixaban dosing regimen remains inconclusive and requires validation in large, dedicated RCTs.
15 May 2026
Read appraisal →Nature communications
A deep learning ECG model for identification and localization of occlusion myocardial infarction.
Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we present a trained and validated deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model has a C-statistic of ≥0.95 for OMI and ≥0.87 for non-OMI infarctions and can localize culprit lesions in the three main coronary branches, which can guide the angiographer. Performance is similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for the identification and localization of OMI has the potential to shorten the time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.
15 May 2026
Read appraisal →BMJ open
Prevalence of anxiety and depression in adults who are high users of healthcare services and magnitude of associated costs: a systematic review
OBJECTIVES: Anxiety and depression are common and associated with higher use of general healthcare services. The aims of this systematic review were to (1) estimate the prevalence of anxiety and depression in adults who are high or costly users of general healthcare services in comparison to routine users and (2) estimate the magnitude of healthcare costs associated with the presence of anxiety and depression. DESIGN: Systematic review of the available literature. DATA SOURCES: MEDLINE, PsycINFO, EMBASE, CINAHL, PROSPERO and Cochrane Library were systematically searched without language restriction from inception to 1 April 2019 and updated on 25 October 2022, 16 October 2024 and 18 February 2026. ELIGIBILITY CRITERIA: Eligible studies described adults aged ≥18 years who were defined as high or costly general healthcare users and where the prevalence and/or associated costs of anxiety and/or depression were quantified. DATA EXTRACTION AND SYNTHESIS: Three reviewers independently extracted information on study characteristics, exposure and outcomes. RESULTS: From the 38 412 identified articles, 27 studies from 10 countries (in Europe, North America and Asia) involving 6 145 907 participants met eligibility criteria and were included. There were wide variations in the estimated prevalence of anxiety (3.8-67.2%) and depression (4.7-77.9%) among high healthcare users. The prevalence of both disorders was higher among high healthcare users than routine users in all studies with non-high user comparator groups. Only four studies investigated healthcare costs associated with depression. These uniformly reported that general healthcare costs are higher for those with depression than those without. No studies investigated costs associated with anxiety. CONCLUSIONS: Anxiety and depression are over-represented among high or costly healthcare users, although accurate quantification of the magnitude of difference is precluded by significant methodological heterogeneity and variability in definitions used. Improved identification of covert mental health problems is essential for the provision of effective interventions for patients and healthcare expenditure reduction. Future research should prioritise a standardised approach, with agreed definitions for high and/or costly healthcare use in different contexts. PROSPERO REGISTRATION NUMBER: CRD42018102628.
15 May 2026
Read appraisal →The New England journal of medicine
Ensitrelvir for Covid-19 Postexposure Prophylaxis in Household Contacts
BACKGROUND: Ensitrelvir, an oral inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease, is approved in Japan for the treatment of mild-to-moderate coronavirus disease 2019 (Covid-19). Previously, no antiviral agents were approved for postexposure prophylaxis in household contacts of patients with Covid-19. METHODS: In this double-blind, randomized, placebo-controlled trial, we randomly assigned persons who were SARS-CoV-2-negative on local diagnostic testing but were household contacts of a patient with Covid-19 (the index patient) to receive either ensitrelvir (375 mg on day 1 and 125 mg daily on days 2 through 5) or placebo within 72 hours after symptom onset in the index patient. The primary end point was Covid-19 (defined by a central laboratory-confirmed positive reverse-transcriptase-polymerase-chain-reaction assay and the presence of ≥1 of 14 prespecified Covid-19 symptoms lasting ≥48 hours) by day 10 in a household contact in the modified intention-to-treat population (all the participants who underwent randomization, had a central laboratory-confirmed negative RT-PCR test for SARS-CoV-2 at baseline, and received at least one dose of the trial drug or placebo). RESULTS: The modified intention-to-treat population included 1030 participants in the ensitrelvir group and 1011 in the placebo group. The mean age of the participants was 42.4 years; 71.1% had undergone randomization within 48 hours after symptom onset in the index patient, and 37.0% had at least one risk factor for severe Covid-19. The incidence of Covid-19 was lower in the ensitrelvir group than in the placebo group (2.9% vs. 9.0%; risk ratio, 0.33; 95% confidence interval [CI], 0.22 to 0.49; P<0.001). The incidence of adverse events during the trial was similar in the two groups (15.1% in the ensitrelvir group and 15.5% in the placebo group), as was the incidence of serious adverse events (0.2% in each group). No Covid-19-related hospitalizations or deaths were reported. CONCLUSIONS: Ensitrelvir administered to household contacts of a patient with Covid-19 within 72 hours after symptom onset in the index patient was effective in preventing Covid-19 in the contacts. (Funded by Shionogi; SCORPIO-PEP Japan Registry for Clinical Trials number, jRCT2031230124; ClinicalTrials.gov number, NCT05897541.).
15 May 2026
Read appraisal →JMIR medical informatics
Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study
BACKGROUND: Antibiotic therapies are the main treatment for bacterial infections, but growing antibiotic resistance is a major global health threat, severely impacting patients with sepsis. Rapid selection of the most effective antibiotic therapy is critical for survival and for preventing further resistance. Physicians must consider numerous factors for proper empiric treatment selection. A clinical decision support system (CDSS) aims to support physicians in this process, facilitating rapid and targeted therapy. OBJECTIVE: The purpose of this work is to explore the extent to which the realization of a CDSS is possible based on the data available to us and to document insights gained during the development of a foundational model designed to assist physicians in determining empiric treatment options for patients with sepsis. In this regard, rather than aiming to develop a CDSS for clinical application, we highlight the importance of close interprofessional collaboration between scientists from various disciplines and analyze the effects of data quality and quantity on the performance of our statistical models. METHODS: Empirical scientists conducted interviews with medical practitioners to acquire the medical knowledge required to develop sound statistical models. We developed and applied 2-step cross-sectional, as well as time-series classification models, to carefully preprocessed data of patients with sepsis admitted to the intensive care unit of a German hospital. RESULTS: We identified several factors as crucial information for valid decisions on empiric therapy for treating patients with sepsis. These include the patients' core data, especially the infection focus. To prevent further resistance, individual risk factors such as travel history and professional background should be considered. The evaluation of a therapy's effectiveness is mainly based on the patient's general condition and blood values such as procalcitonin and interleukin 6. One key factor in the acceptance of a CDSS is the explainability of the results produced by the applied methods. Our models demonstrated mainly weak predictive ability for all considered empiric antibiotic therapies. However, they are not yet suitable for use in clinical practice, especially as they are based on prescribing habits rather than on optimal treatment decisions. CONCLUSIONS: This work highlights the importance of interprofessional collaboration between medical experts and model developers, ensuring that data quality and clinical relevance are central to the process. It emphasizes the urgent need for high-quality, comprehensive data to overcome challenges such as data discontinuity and improve model performance, particularly through enhanced digitization in health care. This feasibility study will facilitate future efforts to develop a CDSS for treating patients with sepsis and to translate it into clinical use.
15 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Speed and quality of complex strategic decisions
Many strategic decisions involve both substantial complexity and time pressure, but the association between decision speed and decision quality of cognitively demanding strategic decisions is not well understood. This paper presents evidence on this question using a setting with exceptionally detailed and precise information about decision times and decision quality-it analyses move-by-move data from in-person professional chess tournaments. Decision quality is measured by comparing actual moves to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results show that faster decisions are associated with higher decision quality, even after accounting for computational complexity, distinctiveness between alternatives, and time pressure. Greater computational complexity and lower distinctiveness between move alternatives are associated with longer decision times, whereas greater time pressure is associated with shorter decision times. All three factors are associated with lower decision quality. We discuss the findings against the predictions of different decision models in which individuals sequentially acquire information about alternatives with uncertain valuations, extending theories originally developed in the context of nonstrategic decisions to a strategic environment.
15 May 2026
Read appraisal →The New England journal of medicine
Endovascular Treatment of Medium-Vessel-Occlusion Strokes
BACKGROUND: Endovascular thrombectomy for acute ischemic stroke due to medium-vessel occlusion has had varying results across trials. Whether thrombectomy improves functional outcomes in patients with medium-vessel occlusion and moderate-to-severe deficits is unclear. METHODS: We conducted an open-label, randomized trial with blinded outcome assessment at 48 centers in China. Eligible patients were adults who presented within 24 hours after the onset of a moderate-to-severe stroke (National Institutes of Health Stroke Scale [NIHSS] score, ≥6; scale, 0 to 42, with higher scores indicating greater neurologic deficits) due to occlusion of a medium vessel. Patients were assigned in a 1:1 ratio to thrombectomy plus medical management (thrombectomy group) or medical management alone (control group). The primary outcome was functional disability as measured by the shift in the modified Rankin scale score (scale, 0 [no disability] to 6 [death]) at 90 days. Violation of the proportional-odds assumption precluded the use of shift in the modified Rankin scale score, so as prespecified, functional independence (modified Rankin scale score of 0, 1, or 2) at 90 days was used as the primary outcome. Safety outcomes were symptomatic intracranial hemorrhage and 90-day mortality. RESULTS: Among 280 patients in the thrombectomy group and 283 in the control group, the median age was 71 years, the median NIHSS score was 10 (range, 3 to 36), and 42.8% were women; 36.6% received intravenous thrombolysis. Functional independence at 90 days was seen in 58.6% of the patients in the thrombectomy group and in 46.6% of those in the control group (adjusted rate ratio, 1.24; 95% confidence interval, 1.07 to 1.44; P = 0.004). The incidence of symptomatic intracranial hemorrhage was 4.7% in the thrombectomy group and 2.2% in the control group; 90-day mortality was 11.1% and 10.2%, respectively. CONCLUSIONS: Among patients with acute ischemic stroke due to medium-vessel occlusion and moderate-to-severe deficits, thrombectomy led to a greater likelihood of functional independence than medical management alone but also to a higher risk of symptomatic intracranial hemorrhage. (Funded by the National Natural Science Foundation of China and the Noncommunicable Chronic Diseases-National Science and Technology Major Project; ORIENTAL-MeVO ClinicalTrials.gov number, NCT06146790.).
15 May 2026
Read appraisal →Journal of clinical microbiology
Diagnostic performance of β-(1→3)-D-glucan, two Candida antigen, and five anti-Candida antibody assays in ICU patients with sepsis and high risk for invasive candidiasis: a secondary endpoint of the CandiSep randomized clinical trial
Invasive Candida infection (ICI) is the most common fungal infection in critically ill patients. This study analyzed the performance of various biomarkers in sera from the CandiSep trial, a randomized, multicenter trial including 342 sepsis patients at high risk for ICI across 18 German intensive care units. ICI and candidemia were diagnosed in 48 (14.0%) and 14 (4.1%) patients, respectively. Sera collected on 2 consecutive days at sepsis onset were analyzed for β-(1→3)-D-glucan (BDG; Fungitell), mannan (Platelia-Candida-Ag-Plus [Platelia-Mn] and Serion-ELISA-antigen-Candida [Serion-Mn]), and anti-Candida antibodies (Platelia-Candida-Ab-Plus, Serion-ELISA-Candida albicans-IgA/IgM/IgG, and Virclia-Candida albicans-germ-tube-antibody-IgG-Monotest). Only antigen levels (BDG, Platelia-Mn, Serion-Mn), but not anti-Candida antibody levels, were significantly elevated in ICI patients. Antigen levels were unaffected by Candida colonization, while antibody levels were significantly increased. Sensitivity and specificity at the manufacturer's cutoffs were unsatisfactory. Performance improved by adjusting cutoffs: BDG required a 3-fold increase, while all others required lowering. At 80% specificity, sensitivities (95% confidence intervals) for the diagnosis of ICI and candidemia were as follows: BDG (cutoff >280 pg/mL), 46% (31.4-60.8) and 64% (35.1-87.2); Platelia-Mn (cutoff >50 pg/mL), 38% (24.0-52.6) and 64% (35.1-87.2); Serion-Mn (cutoff >0.7 U/mL), 38% (24.0-52.6) and 50% (23.0-77.0), and below 28% and 43% for all antibody assays. Area under the receiver operating characteristic curve comparisons showed no significant differences between antigen assays. Combining biomarkers, either simultaneously or sequentially, offered no diagnostic advantage over single-biomarker use. In conclusion, anti-Candida antibody assays are likely influenced by colonization and have limited diagnostic utility. Antigen assays offer similar diagnostic value but require cutoff optimization. Combining biomarkers did not enhance diagnostic yield in our cohort.IMPORTANCEOur main findings based on our specific ICU cohort were as follows: (i) only Candida antigen levels, not anti-Candida antibody levels, were significantly elevated in ICI and candidemia. (ii) In patients without ICI, Candida colonization was not associated with altered antigen levels, but with elevated antibody levels. (iii) Sensitivity and specificity at the manufacturer's cutoffs were unsatisfactory, and cutoffs should be significantly adjusted. Potential optimal cutoff values are proposed by us. (iv) The evaluated antigen assays demonstrated overall comparable diagnostic performance in this study. (v) Anti-Candida antibody assays did not provide a meaningful diagnostic contribution. (vi) The combination of biomarkers offered no diagnostic advantage over the use of individual biomarkers, neither simultaneously nor sequentially. Our results suggest that the use of a single Candida antigen test with a cohort-adapted cutoff value may be sufficient. Furthermore, avoiding biomarker combinations could potentially reduce healthcare costs. The clinical implications of these findings should be interpreted with caution, given the limited data with associated large confidence intervals for candidemia and the cohort-specific nature of the study. Our results should therefore be confirmed in larger studies and additionally with other patient groups.
15 May 2026
Read appraisal →Physiological measurement
A teacher-student deep learning framework for enhanced clinical screening of heart failure from 12-lead electrocardiograms
Objective.Identifying heart failure (HF) from electrocardiograms (ECGs) is challenging due to the lack of definitive features. This study aims to develop a deep learning-based clinical decision support system, CTTSnet, for accurate and automated HF screening using ECGs.Approach.We propose a novel teacher-student framework, CTTSnet, which synergistically integrates a transformer as the teacher and a convolutional neural network as the student. This architecture combines global context-awareness with efficient local feature extraction. The model was trained and evaluated on a large-scale, real-world dataset of 27 018 ECGs and further validated on two external public datasets to assess generalizability.Main results.CTTSnet achieved an AUROC of 0.941 on the primary clinical dataset, surpassing strong baselines. The model attained an accuracy of 87.3%, with a recall of 88.6% and specificity of 85.8%. External validation on two additional public datasets further confirmed the model's generalizability.Significance.CTTSnet provides a scalable, high-accuracy tool for HF triage from routine ECG recordings. Its potential clinical value lies in assisting early identification of patients at risk of HF, thereby helping reduce missed diagnoses and facilitating timely referral for further evaluation.
14 May 2026
Read appraisal →BMJ open
Cluster-randomised-controlled trial evaluating the impact of implementing the Canadian C-Spine Rule in prehospital cervical spine trauma care by the French Emergency Medical Call Center: IPSTRAUC study - a protocol
INTRODUCTION: In France, cervical spine trauma frequently results in systematic prehospital immobilisation with a cervical collar, despite a low incidence of clinically significant injuries. This practice may lead to unnecessary discomfort, delayed care and emergency department (ED) overcrowding. The Canadian C-Spine Rule (CCR) is a validated tool for identifying patients at low risk of cervical injury but its use is currently limited to ED settings. An adapted version (CCRa) has been validated in Canada for prehospital use, demonstrating safe reduction in immobilisation rates. This study aims to evaluate the feasibility, safety and clinical impact of CCRa implementation in the French prehospital emergency setting. The objectives are to assess whether the implementation of the CCRa by French prehospital emergency teams can safely reduce unnecessary cervical spine immobilisation in stable trauma patient, optimise imaging use and decrease ED overcrowding while maintaining patient safety. METHODS AND ANALYSIS: This is multicentre, cluster-randomised-controlled trial involving 16 Emergency Medical Call Center and their affiliated prehospital emergency services in France. Each centre will be randomised to either implement the CCRa or usual care with standard immobilisation practices. Alert adult patients (18-65 years) with suspected closed cervical spine trauma and Glasgow Coma Scale score of 15 will be included. The primary outcome is the proportion of patients with suspected cervical spine trauma who are transported to the ED. Secondary outcomes include the appropriateness of cervical spine immobilisation, use of cervical imaging, ED visits for neck pain, immobilisation-related complications, healthcare costs at 30 days, and feasibility and acceptability of CCRa implementation among prehospital professionals (effectiveness-implementation hybrid type 1 design). Quantitative data will be collected during prehospital care, ED admission and follow-up at days 14 and 30, while implementation outcomes will be assessed at the end of the intervention period using questionnaires and qualitative interviews. ETHICS AND DISSEMINATION: The study has been approved by a national ethics committee (CPP), the French competent authority and registered to the French National Authorities (ID-RCB 2024-A00636-41). It is funded by the French Ministry of Health via the 2023 PREPS programme (DGOS). Results will be published in a peer-reviewed journal and presented at relevant scientific meeting. The sponsor of the study is Rennes University Hospital. The findings may support broader CCRa implementation in European EMS systems. TRIAL REGISTRATION NUMBER: NCT06983873.
14 May 2026
Read appraisal →Current psychiatry reports
The Type of Providers Delivering Practice Elements Used in Child Mass Trauma Interventions: A Systematic Review
PURPOSE OF REVIEW: This review identified the relative frequency of use of various types of providers delivering practice elements in 130 child mass trauma interventions and compared the use of these providers across events, populations, and settings. RECENT FINDINGS: Mental health professionals (MHPs) more frequently delivered cognitive, self-monitoring, exposure, and narrative elements than other providers while non-specialist providers (NSPs) and teachers and school personnel (TSPs) delivered support networking and social skills training more frequently. MHPs administered interventions to targeted populations more frequently, and TSPs administered interventions to universal populations more frequently. Only MHPs administered interventions at clinical facilities; NSPs delivered a majority of interventions at community sites. Understanding the specific practice elements delivered by providers with different backgrounds and training can inform efforts to enhance the reach and impact of mass trauma services. Future publications should report greater detail about providers and their training and about specific practice elements used.
14 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers
BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cardiovascular disease diagnosis and risk stratification. However, their integration into predictive models remains limited by challenges in feature selection, model robustness, and interpretability. This study aims to develop an interpretable machine learning framework for predicting stroke incidence using serum miRNA signatures. METHODS: We analyzed serum miRNA expression profiles from 1,785 human samples (173 stroke patients and 1,612 non-stroke controls) obtained from the GEO dataset GSE117064. Differential expression analysis was performed to identify significantly dysregulated miRNAs using linear modeling and empirical Bayes moderation. LASSO logistic regression was then applied to select predictive miRNA features. Five supervised machine learning classifiers-logistic regression, support vector machine (SVM), random forest, XGBoost, and k-nearest neighbors-were evaluated using 10-fold cross-validation. The best-performing model (SVM) was further interpreted using Shapley Additive exPlanations (SHAP) to assess individual miRNA contributions to prediction. To validate the robustness and clinical generalizability of the identified signatures, quantitative real-time PCR (qPCR) and Next-Generation Sequencing (NGS) were performed on independent sets of serum samples. The pre-trained SVM model was applied to the NGS data to verify its classification performance in an external cohort. RESULTS: A total of 604 miRNAs were differentially expressed between stroke and non-stroke groups, including 206 downregulated and 398 upregulated candidates. LASSO regression identified 66 non-zero-coefficient miRNAs with potential predictive value. Among the classifiers tested, the SVM model achieved the highest average accuracy of 0.9983 and perfect AUC (1.0000), demonstrating superior performance and stability. SHAP analysis revealed that a subset of miRNAs, including hsa-miR-3648, hsa-miR-1290, and hsa-miR-6765-3p, had the greatest impact on classification outcomes, providing mechanistic insights and enhancing model interpretability. qPCR results preliminarily supported the differential expression patterns of key miRNAs. Furthermore, NGS analysis of an independent validation cohort (n = 10) demonstrated distinct hierarchical clustering of the selected biomarkers. The pre-trained SVM model preliminarily supported its cross-platform validity by classifying these external samples with an overall accuracy of 80% (sensitivity 80%, specificity 80%), suggesting the potential generalizability of the identified miRNA panel across different quantification platforms. CONCLUSIONS: This study demonstrates the utility of integrating differential expression analysis, regularized feature selection, and machine learning with SHAP-based interpretation to identify and validate serum miRNA signatures predictive of stroke. The successful external validation using NGS highlights the robustness of the identified biomarkers and their potential for cross-platform application. The results provide a transparent, data-driven framework for biomarker discovery in clinical risk prediction and support the development of non-invasive diagnostic tools for stroke detection.
14 May 2026
Read appraisal →Gut
Simvastatin in the prevention of recurrent pancreatitis: a triple-blinded randomised clinical trial (the SIMBA trial)
BACKGROUND: Recurrent acute pancreatitis (RAP) or acute-on-chronic flares in chronic pancreatitis (CP) have limited preventive options beyond addressing the underlying aetiology. Statins, due to their anti-inflammatory properties, have been proposed as a potential prophylactic treatment. OBJECTIVE: We aimed to evaluate whether simvastatin could reduce the recurrence of pancreatitis. DESIGN: At 23 centres, we conducted a triple-blind, randomised, controlled, superiority trial enrolling patients with at least two episodes of RAP or CP flares in the previous 12 months. Participants were randomly assigned to receive simvastatin or placebo for 1 year. The primary endpoint was the recurrence of pancreatitis. The target sample size was 144 patients; however, an interim analysis was planned in the event of slow recruitment. RESULTS: A total of 85 patients (42.1% women) were included in the interim analysis. In the intention-to-treat analysis, no significant differences were observed regarding recurrence: 46.2% simvastatin versus 44.4% placebo; OR 1.07, 95% CI 0.43 to 2.66; p=0.88, or time to recurrence. No statistically significant differences were observed in recurrence in per-protocol analysis (35.5% simvastatin vs 41.9% placebo; OR 0.76, 95% CI 0.27 to 2.12; p=0.60). Development of diabetes mellitus was more frequent in the simvastatin group (4 vs 0 patients; OR not calculable, p=0.04). CONCLUSION: This trial, evaluating simvastatin versus placebo for the prevention of pancreatitis, did not demonstrate a reduction in recurrence rate, although results might be underpowered due to early termination. The relationship between statins in these patients and new-onset diabetes requires further investigation. TRIAL REGISTRATION NUMBER: NCT04021498.
14 May 2026
Read appraisal →PloS one
Effects of nebulized dexmedetomidine for premedication on the parameters of oxidative and inflammatory stress in children undergoing tonsillotomy and adenoidectomy: A pilot randomized controlled trial
BACKGROUND: This prospective randomized clinical pilot study aimed to evaluate the effect of inhaled Dexmedetomidine in premedication in children after tonsillectomy with adenoidectomy on oxidative and inflammatory stress and the drugs impact on clinical parameters such as perioperative sedation, hemodynamics, respiratory function, postoperative delirium, analgesia, opioid consumption and frequency of complications. METHODS: Children aged 4-13 years with an indication for tonsillotomy with adenoidectomy were randomized into two groups: (D) - Dexmedetomidine treated group and (N) - control, non-treated group. Ramsey's sedation score, Bispectral index (BIS), and vital signs (heart rate, respiratory rate, noninvasive blood pressure and hemoglobin oxygen saturation) were monitored perioperatively in at defined time points. After 15 minutes of extubation, the postoperative delirium score (PEAD) and pain score (VAS) were noticed. Pain scores were monitored for up to 12 postoperative hours every three hours on the ward. Venous blood samples were taken before intervention and after surgery to evaluate redox homeostasis (levels of malondialdehyde - MDA, nitrogen monoxide, glutathione and sulfhydryl groups and the activity of superoxide dismutase and catalase) and inflammatory parameters (blood count, neutrophile/lymphocyte ratio, C-reactive protein and Interleukin 6). Perioperative complications in the induction phase, intraoperative and postoperative period, as well as the consumption of opioids, were noted. RESULTS: 16 patients in the D and 15 in the N groups participated in the study. There was no difference in age, sex, body weight and duration of surgery between the two groups. The obtained results of oxidative stress and inflammatory response markers showed that there was a statistically significant difference in the MDA level between compared groups (postoperative MDA 88.93 ± 1.19 vs. 95.05 ± 1.58, ΔMDA 0.014), with no difference observed in other investigated oxidative and inflammatory stress in plasma samples. Patients in both groups were hemodynamically and respiratory stable. Still, patients in the treated (D) group achieved the desired level of sedation compared to the control evaluated by the Ramsey sedation score (p < 0.001). The BIS values were statistically significantly different between the D and N groups 30 minutes after inhalation (86.6 ± 11.6 vs. 98.1 ± 1.2, P < 0.001), but showed no differences during and after the procedure. The two groups had a significant difference in pain control upon awakening and one hour after the operation, respectively (pain on awakening p = 0.024; pain during the first hour of stay in the ward p = 0.039). The consumption of intraoperative opioids was slightly lower in the D group than in the N group (D vs. N group: fentanyl in µg/kg mean 3.67 ± 0.78 vs. 3.94 ± 1.12, p = 0.445, i.e., alfentanil in µg/kg mean 2.02 ± 1.85 vs. 2.30 ± 2.08, p = 0.691). Also, there were no differences in observed perioperative complications between groups. CONCLUSION: Inhaled Dexmedetomidine at a dose of 2 µg/kg in premedication reduces oxidative stress. However, the statistical significance of this drugs effect on the level of markers of antioxidant defense and inflammation has not been proven in children operated on for tonsillar hypertrophy. This dose regimen and route of application of Dexmedetomidine significantly affect the degree of perioperative sedation and BIS values, provides perioperative hemodynamic and respiratory stability, significant immediate postoperative analgesia, and reduces the consumption of analgesics with minimal complications, which makes it useful for shorter surgical interventions.
14 May 2026
Read appraisal →European journal of pediatrics
Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.
UNLABELLED: Smartphone-based lung auscultation, combined with artificial intelligence (AI), may offer a promising alternative for pediatric respiratory telemonitoring. The aim of our study was to evaluate the performance of an AI model on wheeze detection from pediatric respiratory sounds recorded via smartphone. An observational cross-sectional study was conducted at the Pediatric Department of a Portuguese tertiary hospital, including children aged 0-17 years (pre-school, school-aged, and adolescent), with or without respiratory disease. Respiratory sounds were recorded at four locations using smartphone microphones and independently classified by at least 2 blinded annotators for quality and presence of wheezes. A hybrid convolutional-recurrent neural network (CNN + LSTM) trained on public electronic stethoscope databases was used to detect wheezes. AI model performance was assessed using positive predictive value, sensitivity, specificity, accuracy, and F1-score. A total of 217 children (59.9% male; median 10 [Q1-Q3 4.5-13] years) were included. From 2020 respiratory sound recordings, 1500 (74.3%) met quality criteria. Manual annotation identified 271 recordings with wheezes, whereas AI detected 217, most frequently in pre-school children (64.5% AI; 53.9% manual). The model achieved an overall accuracy of 87% (95% CI 86-89) and an F1-score of 61% (95% CI 56-66). The best accuracy was found in adolescents (92%, 95% CI 90-94) and the best global F1-score in pre-school children recordings (64%, 95% CI 58-71). CONCLUSION: This study supports the feasibility of AI-assisted analysis of smartphone-recorded pediatric respiratory sounds in real-world settings. Future multicenter studies with larger datasets and model fine-tuning using smartphone recordings are expected to improve AI model performance and generalizability. WHAT IS KNOWN: • Continuous monitoring of pediatric respiratory diseases is essential to improve outcomes and reduce healthcare and family burden. • Smartphone lung auscultation is feasible in children but AI wheeze detection models have only been applied to recordings from electronic stethoscopes. WHAT IS NEW: • First study applying an AI model to detect wheezes in smartphone-recorded lung sounds. • Quality recordings were obtained across all pediatric age groups and the AI model performance showed promising results, comparable to studies using electronic stethoscopes.
14 May 2026
Read appraisal →JAMA
Intrawound Tobramycin Plus Vancomycin to Prevent Surgical Site Infection in Tibial Fractures: The TOBRA Randomized Clinical Trial
IMPORTANCE: Previous research has suggested that intrawound vancomycin powder reduces deep surgical site infections among patients with periarticular tibial fractures at high risk of infection. It is unknown whether the addition of tobramycin powder further decreases infection rates. OBJECTIVE: To compare whether the combination of tobramycin plus vancomycin vs vancomycin alone delivered as intrawound powder at the time of definitive fixation reduces deep surgical site infections. DESIGN, SETTING, AND PARTICIPANTS: Open-label, assessor-masked, randomized clinical trial conducted at 39 US trauma centers. Eligible patients were adults with an operatively treated periarticular tibial fracture (either tibial plateau or pilon) who met 1 of 3 criteria for elevated infection risk. Enrollment occurred between June 18, 2021, and December 12, 2024 (final follow-up, July 15, 2025). INTERVENTIONS: Intrawound tobramycin (1.2 g) plus vancomycin (1.0 g) powder vs intrawound vancomycin (1.0 g) powder delivered at the time of definitive fixation. MAIN OUTCOMES AND MEASURES: The primary outcome was a deep surgical site infection requiring surgical management within 182 days of definitive fracture fixation. Secondary outcomes included deep surgical site infections with pathogens that were gram-negative only, deep surgical site infections with at least 1 pathogen that was gram-positive, deep surgical site infections with polymicrobial cultures, deep surgical site infections with negative culture results, and cellulitis or skin infections treated only with antibiotics. RESULTS: Among the 1660 participants randomized, 1528 (mean age, 47.0 [SD, 14.3] years; 603 female [39.5%]; 925 male [60.5%]) were included in the primary analysis. Deep surgical site infections occurred in 51 of 753 participants (182-day probability, 7.4%) in the tobramycin plus vancomycin group and 47 of 775 participants (182-day probability, 6.6%) in the vancomycin alone group (hazard ratio, 1.11; 95% bayesian credible interval, 0.75-1.66; posterior probability of superiority, 29.7%). The threshold required for superiority was not reached for any secondary outcome. CONCLUSIONS AND RELEVANCE: Among patients with operatively treated periarticular tibial fractures at high risk of infection, adding intrawound tobramycin powder to vancomycin powder at the time of definitive fixation did not reduce deep surgical site infections compared with vancomycin powder alone. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02227446.
14 May 2026
Read appraisal →Hernia : the journal of hernias and abdominal wall surgery
Obturator hernia in adults: a systematic review of CT diagnosis and surgical management
PURPOSE: Obturator hernia is a rare but life-threatening pelvic floor hernia occurring predominantly in elderly, underweight women and is associated with delayed diagnosis and high perioperative mortality. This systematic review evaluated the diagnostic performance of computed tomography (CT), factors associated with bowel resection, and short-term outcomes of open versus laparoscopic repair in adults. METHODS: A systematic review was conducted per PRISMA 2020. PubMed/MEDLINE, Scopus, and Web of Science were searched from January 2004 to January 2026. Studies reporting at least three surgically confirmed adult cases were eligible. Three consecutive institutional cases were incorporated as an illustrative clinical adjunct. RESULTS: Twenty-two studies including 876 patients were analysed. The mean age across included studies was 79.7 years; 91.5% were female. Reported preoperative CT sensitivity across included studies was 93.2% (range 66.7-100%). Bowel resection was required in 31.6% and was associated with higher 30-day mortality (19.6% vs. 3.9%). Laparoscopic repair appeared to be associated with lower wound morbidity, shorter hospital stay (5.3 vs. 12.6 days), and lower recurrence (0% vs. 2.8%) in selected patient cohorts, although case-mix differences preclude direct comparison. Mesh was used in 44.3% overall. In three institutional cases (all elderly cachectic women presenting emergently), one underwent TAPP and two underwent open midline laparotomy; primary suture repair was performed in all owing to contamination, bowel compromise, or extreme frailty. CONCLUSION: CT remains the diagnostic cornerstone in obturator hernia. Surgical strategy should be individualised based on haemodynamic status, contamination, and frailty. Reported outcomes represent descriptive trends rather than pooled statistical estimates due to heterogeneity of available data.
14 May 2026
Read appraisal →Archives of gynecology and obstetrics
Clinical outcomes and practical management implications in rudimentary horn pregnancies: a systematic review and pooled analysis (2010-2025)
PURPOSE: Rudimentary horn pregnancies are rare and carry significant maternal and fetal risks. This study aims to systematically review published cases of rudimentary horn pregnancies describing data about time of diagnosis, time of rupture, and live birth rate (LBR), with exploratory comparisons between communicating (CHP) and non-communicating horn pregnancies (NCHP). METHODS: A PRISMA-guided systematic search of PubMed, MEDLINE, and Cochrane (October 1, 2025) was performed with terms "unicornuate uterus pregnancy" OR "rudimentary horn pregnancy." Case reports, series, and reviews with defined clinical data were included; abstracts and unclear reports were excluded. Three independent reviewers extracted data using a standardized protocol. Descriptive statistics (means, medians, standard deviations, and ranges) and exploratory group comparisons (Fisher's exact test, Mann-Whitney U and Student's t test with p < 0.05 denoting significance) were performed. RESULTS: From 132 articles, 190 cases were included: 27 CHP (14.2%) and 163 NCHP (85.8%). Time of diagnosis was earlier in NCHP (15.9 ± 8.9 weeks) than CHP (22.1 ± 10.0 weeks, p = 0.01). Rupture occurred in 68 cases (35.8%), similarly in CHP (48.1%) and NCHP (33.7%, p = 0.19). Time of rupture was earlier in NCHP (18.7 ± 6.6 weeks) than CHP (23.0 ± 9.2 weeks, p = 0.02). Four cases attempted to continue pregnancy; all resulted in premature rupture. Reported live births were infrequent (11.6%) and more common in published CHP (25.9%) vs. NCHP cases (9.2%; p = 0.02). CONCLUSIONS: This descriptive synthesis indicates uterine horn communication may be associated with later diagnosis/rupture and reported live births were significantly more frequent in published CHP vs. NCHP cases. However, all comparisons are exploratory and must be interpreted with extreme caution, as data limitations preclude causal inference. Exhaustive imaging and diagnosis are essential to characterize horn type. Surgical termination is advised, but if expectant management is chosen, close monitoring and multidisciplinary care, including weekly ultrasounds, are advisable to mitigate complications.
14 May 2026
Read appraisal →Hernia : the journal of hernias and abdominal wall surgery
Chronic pain after Shouldice versus Lichtenstein inguinal hernia repair: a systematic review and meta-analysis
PURPOSE: The Lichtenstein (mesh) and Shouldice (non-mesh) techniques are the main options for open inguinal hernia repair. This systematic review and meta-analysis compared the prevalence of chronic postoperative pain between them. METHODS: A systematic search was conducted in Medline, Embase and Cochrane databases for studies comparing the prevalence of chronic pain after Shouldice and Lichtenstein techniques in adult patients undergoing inguinal hernia repair. Meta-analysis was performed using RevMan and the effect model was determined based on heterogeneity, assessed by the I² statistic. Subgroup analyses were conducted by mean follow-up duration and study design. RESULTS: Ten studies with 4,122 patients (59.8% Lichtenstein) were included. Shouldice repair was associated with a 34% relative risk reduction in chronic postoperative pain compared to Lichtenstein (RR 0.66; 95%CI 0.56-0.78; p < 0.00001; I² = 5%). Conversely, the Shouldice technique demonstrated a higher risk of long-term hernia recurrence (RR 2.54; 95%CI 1.15-5.63; p = 0.02). There were no statistically significant differences between the two techniques regarding early postoperative complications (RR 1.01; 95%CI 0.85-1.19; p = 0.95) or mean operative time (MD 5.96 min; 95%CI -0.79 to 12.71; p = 0.08). CONCLUSION: Although the Lichtenstein repair is more widely practiced, the Shouldice technique yields significantly lower rates of chronic postoperative pain, albeit with a higher risk of long-term recurrence. This trade-off highlights Shouldice as a potential alternative in selected patients.
14 May 2026
Read appraisal →World journal of urology
Complications of thulium fiber laser lithotripsy: a systematic review and meta-analysis by the EAU-YAU endourology and urolithiasis working group
BACKGROUND: Thulium Fiber Laser (TFL) has emerged as a promising alternative to Holmium: YAG for lithotripsy, yet its safety profile remains fragmented across various trials. This study aims to synthesize available evidence to determine the pooled complication rates associated with TFL lithotripsy . METHODS: A systematic review was conducted following PRISMA guidelines. We searched PubMed, Cochrane CENTRAL, and ClinicalTrials.gov for studies reporting complications of TFL in adult patients with urolithiasis. The primary outcome was the overall complication rate. Secondary outcomes included Clavien-Dindo grading, specific adverse events (fever, sepsis, injury), and stone-free rate (SFR). A random-effects meta-analysis was performed using the Freeman-Tukey double-arcsine transformation. RESULTS: Thirty-eight studies involving 3,329 patients were included. The pooled overall complication rate was 10.42% (95% CI: 8.01%-13.10%; I²=81.9%). Major complications (Clavien-Dindo III-V) were rare, with a pooled rate of 1.19% (95% CI: 0.68%-1.84%). Specific event rates included fever (2.45%), ureteral injury (1.37%), bleeding (1.07%), urosepsis (0.77%), ureteral strictures (0.70) and acute pyelonephritis (0.59%). Meta-regression identified operative time as a significant predictor of complications (p = 0.041), whereas stone size was not (p = 0.358). Subgroup analysis showed higher complication rates in pure renal stone treatments (13.83%) compared to ureteral stones (10.64%). The pooled Stone-Free Rate (SFR) was 87.61% (95% CI: 83.61%-91.12%). CONCLUSION: Thulium Fiber Laser lithotripsy demonstrates a pretty favorable safety profile with a very low incidence of high-grade complications (< 1.2%) and high efficacy.
14 May 2026
Read appraisal →European journal of heart failure
Effect of influenza vaccination in patients with decompensated heart failure: a systematic review and meta-analysis
AIMS: In patients with heart failure (HF) influenza vaccination has shown beneficial effects in preventing cardiac decompensations. However, no conclusive results have been achieved in the few studies that have evaluated the impact of vaccination during episodes of acute HF (AHF) decompensation. We conducted a systematic review and meta-analysis to determine the possible effects of influenza vaccination on all-cause mortality in patients diagnosed with AHF. METHODS: PubMed, Medline, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews databases were searched for longitudinal studies comparing patients with AHF vaccinated against influenza with unvaccinated patients. The primary outcome selected for meta-analysis was 1-year all-cause mortality, and secondary outcomes consisted of other outcomes reported in at least in two different studies. Statistical heterogeneity was determined by calculating the I² statistic. Individual adjusted results were pooled using a random effects model. Sensitivity analysis was run for the primary outcome by removing each individual study and then re-doing the meta-analysis. RESULTS: Up to 30 June 2025, five observational cohort studies examining the effect of influenza vaccination on 1-year all-cause mortality in AHF patients had been published. Statistical heterogeneity was low (I2 = 33.7%), meaning that between-study results were consistent. Pooled analysis of confounder-adjusted hazard ratio (HR) for all-cause mortality in vaccinated patients was 0.89 (95% CI 0.83-0.96) compared with unvaccinated patients. All sensitivity analyses rendered very similar results. In-hospital and 90-day mortality were reported in three and two studies and showed similar reductions in risk, with an adjusted odds ratio of 0.85, 95% CI 0.70-1.01, and adjusted HR of 0.86, 95% CI 0.76-0.96; respectively. Isolated data from single studies suggest no effect on hospitalization following discharge after the AHF episode. CONCLUSIONS: Influenza vaccination is associated with a lower short- and long-term all-cause mortality in patients with decompensated HF; however, as all the studies included in this meta-analysis were observational, these results could be subject to residual confounding and causality cannot be directly inferred from them.
14 May 2026
Read appraisal →BMC psychiatry
N-acetylcysteine for patients with alcohol use disorder, post-traumatic stress disorder, and their co-occurrence: a systematic review of placebo-controlled randomized trials
INTRODUCTION: Alcohol use disorder (AUD) and post-traumatic stress disorder (PTSD) are thought to be significant global contributors to the burden of mental illness. According to recent WHO epidemiological estimates, 3.9% of people worldwide have experienced PTSD at some point in their lives; this number rises to roughly 5.6% among those who have experienced trauma. NAC has shown promise in reducing PTSD symptoms and cravings in veterans, according to recent trials. Our analysis aims to resolve the conflict between results and determine whether NAC is an effective add-on therapy for PTSD, AUD, and co-occurring PTSD/AUD. METHODS: We conducted this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, following our protocol (CRD420251170956). We searched PubMed, Scopus, Cochrane CENTRAL, Embase, EBSCO, and the Web of Science for relevant studies. Only randomized placebo-controlled trials were included. The primary outcomes were severity of PTSD and alcohol craving, while secondary outcomes were anxiety, depression, and alcohol use patterns and biomarkers. RESULTS: Seven randomized, double-blind, placebo-controlled trials were included in this meta-analysis, enrolling a total of 566 participants. There were no significant differences between NAC and placebo in craving (SMD= -0.40, 95% CI [-1.05, 0.25], P = 0.22, I² = 56.1%). Regarding PTSD severity, the PCL scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = 0.04, 95% CI [-0.40, 0.48], P = 0.8) (I² = 65.9%, P = 0.05), while the CAPS scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = -0.13, 95% CI [-0.50, 0.24], P = 0.49) (I² = 54.8%, P = 0.109). Leave-one-out sensitivity analysis showed that exclusion of the Back et al. (2025) study resulted in a significant effect favoring NAC (SMD = -0.35, 95% CI [-0.70, -0.01], P = 0.04), with no observed heterogeneity (I² = 0%). CONCLUSION: Our meta-analysis indicates that N-acetylcysteine, when compared to placebo, does not demonstrate consistent efficacy in reducing the primary symptoms of AUD or PTSD in broad patient populations. However, the treatment is relatively safe. A noteworthy signal of potential efficacy for PTSD symptoms emerged in a sensitivity analysis, suggesting that the therapeutic promise of NAC should not be entirely dismissed. CLINICAL TRIAL NUMBER: Not applicable.
14 May 2026
Read appraisal →European journal of clinical pharmacology
Comparison of loop diuretics for chronic heart failure: a systematic review and meta-analysis
BACKGROUND: Loop diuretics relieve congestion in chronic heart failure (CHF). We systematically reviewed randomised clinical trials (RCTs) evaluating their effects. METHODS: A systematic search was conducted across major databases. We included RCTs that compared a loop diuretic to another loop diuretic, placebo, or "no intervention". The primary outcome was all-cause mortality, and secondary outcomes were all-cause hospitalisation, serious adverse events (SAEs), and change in body weight (kg). RESULTS: This review included 23 RCTs that enrolled 4,902 patients. Treatment with alternative loop diuretics (torsemide, azosemide and piretanide) compared with furosemide did not reduce all-cause mortality (risk ratio 1.00, 95% CI 0.89, 1.12); I2 = 0%; p = 0.95), nor did it reduce all-cause hospitalisations (risk ratio 0.99, 95% CI 0.84, 1.17); I2 = 53,46%, p = 0.92). In a pooled analysis of alternative loop diuretics (including torsemide and piretanide) the risk of SAEs was higher compared to furosemide (risk ratio 1.32, 95% CI 1.03, 1.68); I2 = 0%), although this finding was based on a limited number of trials with inconsistent definitions and should therefore be interpreted cautiously. The pooled meta-analysis of change in body weight did not show a difference between loop diuretics (mean difference 0.00 kg, 95% CI -1.19, 1.20; I2 = 92.43), indicating substantial heterogeneity. Azosemide resulted in a significant weight loss reduction compared to furosemide (mean difference - 1.00 kg, 95% CI -1.42, -0.58) based on a single small study. CONCLUSIONS: The choice of loop diuretic did not seem to influence the risk of mortality. In a pooled analysis of torsemide and piretanide, the risk of SAEs was higher when compared to furosemide, but the certainty of this finding is limited. Azosemide was associated with weight loss compared to furosemide in one small trial.
14 May 2026
Read appraisal →Journal of medical Internet research
Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study
BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the optimal integration of these models with existing screening practices remains unclear. OBJECTIVE: The objective of this study was to compare parallel and serial testing strategies that combine an ML suicide risk model and the Ask Suicide-Screening Questions (ASQ) against using the ASQ alone. To achieve this, we conducted a retrospective secondary analysis of electronic health record data. The cohort consisted of adult emergency department visits at an Indian Health Service facility between October 1, 2019, and October 2, 2021. METHODS: Sensitivity, specificity, predictive values, and 95% CIs were averaged across 10 cross-validated patient-level folds. The final sample included 7897 American Indian patients with 26,896 visits, 824 (3.1%) of which had a positive ASQ result and 102 (0.4%) of which had the outcome of suicide attempt or death within 90 days of the visit. The logistic regression ML model previously developed using Indian Health Service-specific data was operationalized at the 95th and 75th percentiles to evaluate high-risk and medium-risk thresholds, respectively. A sensitivity analysis was performed to evaluate identification approaches across all emergency department visits during this period. RESULTS: The ML medium-risk threshold alone identified the most true positives (sensitivity: 0.782, 95% CI 0.648-0.915; specificity: 0.751, 95% CI 0.725-0.777; positive predictive value [PPV]: 0.012, 95% CI 0.009-0.014; negative predictive value [NPV]: 0.999, 95% CI 0.998-0.999) in comparison to the ML high-risk threshold alone (sensitivity: 0.429, 95% CI 0.287-0.572; specificity: 0.955, 95% CI 0.948-0.961; PPV: 0.035, 95% CI 0.022-0.048; NPV: 0.998, 95% CI 0.997-0.999) or the ASQ alone (sensitivity: 0.178, 95% CI 0.073-0.282; specificity: 0.970, 95% CI 0.968-0.971; PPV: 0.022, 95% CI 0.010-0.034; NPV: 0.997, 95% CI 0.996-0.998). Combining the ML high-risk threshold with the ASQ in series yielded the greatest positive predictive ability (PPV: 0.050, 95% CI 0.014-0.086) at the cost of reduced sensitivity (0.129, 95% CI 0.036-0.221). Finally, the parallel testing approach using the ML medium-risk threshold yielded the greatest sensitivity (0.795, 95% CI 0.671-0.920; specificity: 0.742, 95% CI 0.716-0.767; PPV: 0.012, 95% CI 0.009-0.014; NPV: 0.999, 95% CI 0.998-0.999) without missing any cases identified through screening. CONCLUSIONS: Unlike existing studies that evaluate ML and screening tools in isolation, this study innovates by assessing combined parallel and serial testing strategies in a real-world setting. We demonstrated that, while serial testing maximizes predictive accuracy, it is often infeasible. Instead, parallel testing brings value as a clinical "safety net" to catch at-risk patients missed by standard practices. Ultimately, integrating ML in suicide prevention requires balancing statistical accuracy with setting-specific, real-world workflows.
13 May 2026
Read appraisal →Annals of medicine
Ceftriaxone for methicillin-susceptible Staphylococcus aureus bloodstream infections is associated with increased short-term mortality: a systematic review and meta-analysis
BACKGROUND: Bloodstream infections (BSIs) by methicillin-susceptible Staphylococcus aureus (MSSA) are a significant cause of morbidity and mortality, traditionally treated with antistaphylococcal penicillins (ASPs) or cefazolin. Ceftriaxone has emerged as an alternative due to its once-daily dosing regimen and favourable safety profile; however, its efficacy compared to the standard of care (SoC) remains controversial. This evidence synthesis aimed to assess the role of ceftriaxone in treating MSSA-BSIs. METHODS: A systematic literature search was conducted in PubMed, Embase, and Scopus up to December 31, 2025 (PROSPERO protocol CRD42024595748). Studies comparing ceftriaxone to ASPs or cefazolin for MSSA-BSIs were included. Primary outcomes were 30-day and 90-day all-cause mortality . Pooled effect sizes with their 95% confidence intervals (CIs), were calculated using random-effects models, odds ratios (ORs) and mean differences (MDs) according to the type of outcome. RESULTS: Eleven studies totalling 2,568 patients were included. Ceftriaxone was associated with significantly increased 30-day mortality (OR 3.33; 95% CI: 2.17-5.10), although differences at 90 days were not significant (OR 1.71; 95% CI: 0.75-3.90). No significant differences were noted for clinical success (OR 0.49; 95% CI: 0.19-1.26), microbiological clearance (OR 1.66; 95% CI: 0.73-3.82). Adverse event rates were similar between groups. CONCLUSION: Given the availability of various alternatives and the consistent short-term mortality signal observed, routine use of ceftriaxone for MSSA-BSIs, especially as initial therapy, is not supported by current evidence. Only novel findings from randomized studies may change the place in therapy of the drug in this context.
13 May 2026
Read appraisal →Pediatric surgery international
Selective non-operative management of post-appendectomy abscesses in children: a comprehensive systematic review and pairwise meta-analysis
Post-appendectomy intra-abdominal abscess remains a common complication following pediatric appendicitis. Although drainage has traditionally been considered standard treatment, increasing evidence suggests selected patients may be successfully managed with antibiotics alone. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251075191). PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched through June 20, 2025. Eligible studies included pediatric patients (0-18 years) with post-appendectomy abscess managed with antibiotic vs. invasive drainage. The primary outcome was failure of first-line treatment requiring escalation. Secondary outcomes included recurrence and length of hospital stay. Ten observational studies involving 363 pediatric patients were included (152 drainage, 211 conservative). Treatment success was 80.9% with drainage and 89.1% with antibiotics. Pooled analysis demonstrated no statistically significant difference in treatment success (RR 1.00; 95% CI 0.95-1.05) or recurrence (RR 0.99; 95% CI 0.57-1.74). Drainage was associated with longer hospital stays (MD 3.40 days; 95% CI 0.65-6.14; p = 0.02). Subgroup analyses demonstrated consistent findings. All included studies demonstrated a serious risk of bias due to confounding by indication. An antibiotics-first approach appears viable for clinically stable pediatric patients, achieving comparable outcomes with shorter hospitalization.
13 May 2026
Read appraisal →PloS one
Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors
BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is common, yet tools for predicting poor recanalization remain limited. We aimed to develop and compare machine learning models for predicting poor recanalization in patients with IDDVT and to identify the most informative predictors. METHODS: A total of 1600 patients with IDDVT were retrospectively enrolled. The dataset was randomly divided into a development set (n = 1280) and an independent test set (n = 320) using stratified sampling. Six predictive models were developed and compared: logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and a Voting Ensemble. Model training and hyperparameter tuning were performed in the development set using five-fold stratified cross-validation, and optimal classification thresholds were determined using the Youden index. Model performance was evaluated by discrimination, calibration, and classification metrics, with 95% confidence intervals estimated by bootstrap resampling (10,000 iterations). SHAP analysis was applied to interpret the final model. RESULTS: In the independent test set, all models showed acceptable to strong discrimination, with AUC values ranging from 0.808 to 0.908. XGBoost achieved the best overall performance, with an optimal threshold of 0.183, an AUC of 0.908 (95% CI, 0.855-0.952), a Brier score of 0.077 (95% CI, 0.058-0.096), an accuracy of 0.900 (95% CI, 0.866-0.931), a precision of 0.650 (95% CI, 0.529-0.767), a recall of 0.803 (95% CI, 0.686-0.906), an F1-score of 0.717 (95% CI, 0.615-0.806), and a specificity of 0.918 (95% CI, 0.884-0.950). The calibration intercept and slope of the XGBoost model were 0.149 (95% CI, -0.192 to 0.454) and 1.410 (95% CI, 1.098-1.809), respectively, indicating acceptable overall calibration. SHAP analysis identified D-dimer rate, provoking-factor-related variables, anticoagulant use, and age group as the most influential predictors. CONCLUSION: Among six candidate models, XGBoost showed the best overall performance for predicting poor recanalization in patients with IDDVT. This study establishes an interpretable machine learning-based prediction framework focused specifically on poor recanalization in IDDVT and highlights the contribution of dynamic laboratory information, particularly D-dimer rate. The model may support early risk stratification and individualized follow-up planning, but external validation is required before routine clinical implementation.
13 May 2026
Read appraisal →Vaccine
Factors influencing rabies vaccines immunogenicity during post-exposure prophylaxis: a systematic literature search and meta-analysis
BACKGROUND: Rabies post-exposure prophylaxis (PEP) requires timely appropriate wound management and vaccination (including concomitant rabies immunoglobulin [RIG], depending on vaccination history and wound severity) to ensure rapid increases in rabies virus neutralizing antibody (RVNA) titers, a surrogate marker of protection. In this systematic review and meta-analysis, we assessed factors influencing the immunogenicity of rabies vaccines administered as PEP. METHODS: PubMed, Embase, and The Cochrane Library were systematically searched for randomized controlled trials published in English, French, Spanish, or Portuguese from January 1, 1983 to March 28, 2022 reporting the immunogenicity of rabies vaccines, specifically rabies human diploid cell vaccine (Imovax Rabies), purified Vero cell rabies vaccine (Verorab), and purified chick embryo cell rabies vaccine (RabAvert/Rabipur), as part of PEP in healthy individuals. Pooled RVNA geometric mean titers (GMTs) and seroconversion rates at Day 14 (D14) post-vaccination, determined by rapid fluorescent focus inhibition test, were estimated using random effects meta-analyses. RESULTS: We identified 1040 studies, of which 67 (9689 participants across 140 intervention groups) were included. The pooled RVNA GMT across all vaccines at D14 post-vaccination was 8.19 (95% CI: 6.73-9.98) and did not differ significantly by vaccine, administration route, or RIG use; however, there were significant interlaboratory differences. Meta-regression analysis, including titration year, demonstrated a negative association with log-transformed RVNA GMTs, equating to a 4.18% reduction (95% CI: -5.89 to -2.44) in log-transformed GMTs per year during the period assessed. Across all vaccines assessed, the pooled seroconversion rate by D14 post-vaccination was 98% (95% CI: 97-98; n = 7023; I2 = 64%) and was not impacted by vaccine, administration route, RIG use, test laboratory, or titration year. CONCLUSIONS: Although RVNA GMTs appear to have decreased by titration year, there was no clinically meaningful impact on immunogenicity, and the three licensed rabies vaccines assessed are expected to provide similar protection.
13 May 2026
Read appraisal →PloS one
Systematic evaluation of the DeepSeek large language model for clinical diagnostic reasoning
BACKGROUND: Artificial intelligence (AI) is undergoing an era of transformative advancement, particularly through the emergence of Transformer-based large language models (LLMs). While these systems demonstrate strong reasoning and generalization capabilities, their clinical applicability, particularly in emergency and critical care decision-making, remains underexplored.. In time-sensitive settings, diagnostic reasoning must align rigorously with evidence-based standards and ensure the relevance of timing to clinical decisions. OBJECTIVE: This study aims to provide a preliminary evaluation of the decision-support performance of the DeepSeek model in acute medical scenarios. We systematically evaluate its diagnostic reasoning, temporal consistency of recommendations, and adherence to evidence-based critical care protocols using standardized case-based assessments. METHODS: Twenty-nine representative clinical cases were extracted from the Merck Manual of Diagnosis and Therapy, a widely used medical reference providing standardized case descriptions. The model's outputs were evaluated across four decision-making dimensions: differential diagnosis, diagnostic testing, final diagnosis, and management planning. Human raters scored each response for accuracy, and multivariable linear regression was applied to assess associations between performance and case parameters (age, gender, and Rapid Emergency Medicine Score [REMS]). RESULTS: DeepSeek achieved an overall mean accuracy of 82.9% (95% CI: 80.2-85.6%) across all cases. Accuracy peaked in final diagnosis (97.7%), but declined in differential diagnosis (73.0%). Model performance showed no significant variation across demographic or severity strata. CONCLUSIONS: DeepSeek shows promising performance in structured case-based diagnostic tasks, particularly in confirmatory diagnostic reasoning. However, its early-stage reasoning and handling of ambiguous cases require enhancement. Future studies using larger and more diverse clinical datasets are needed to further evaluate the model's robustness and potential clinical applicability.
13 May 2026
Read appraisal →Med (New York, N.Y.)
Evaluation of recombinant human prourokinase for acute pulmonary embolism: A phase 2, randomized clinical trial
BACKGROUND: Recombinant human prourokinase (rhPro-UK) is a novel plasminogen activator under investigation for acute pulmonary embolism (aPE). We aimed to explore the efficacy and safety of rhPro-UK vs. alteplase (recombinant tissue plasminogen activator [rt-PA]) in patients with aPE. METHODS: In this phase 2, randomized, single-blind, multicenter, active-controlled, randomized trial involved eighteen centers from university-affiliated tertiary hospitals across China. Patients aged 18-75 years with high or intermediate-to-high risk aPE were randomized to receive intravenous rhPro-UK (40 mg, n = 37; 50 mg, n = 35) or rt-PA (n = 35) and were followed for 7-30 days. The primary efficacy outcome was the change from baseline in systolic pulmonary artery pressure (sPAP) at 24 h post-treatment. Secondary outcomes include changes in right ventricular function parameters. Safety outcomes include all-cause mortality, recurrent PE, hemodynamic deterioration within 7 days, and bleeding events within 30 days. FINDINGS: All treatment groups showed a reduction in sPAP at 24 h (mean change: 40 mg rhPro-UK, -13.40 mmHg [95% confidence interval (CI): -24.10 to -2.71]; 50 mg rhPro-UK, -15.42 mmHg [95% CI: -25.93 to -4.91], and rt-PA: -16.02 mmHg [95% CI: -25.53 to -6.51]), with improvements sustained through 30 days. The incidence of non-major bleeding events was numerically lower in the rhPro-UK groups (40 mg: 63.9%; 50 mg: 55.6%) compared to the rt-PA group (82.9%; p = 0.04). Two deaths occurred, each in the 40 mg rhPro-UK group and rt-PA group. CONCLUSION: rhPro-UK tended to result in early hemodynamic improvement comparable to rt-PA and seemed to have a numerically lower risk of non-major bleeding events. The ClinicalTrials.gov identifier is NCT03108833. FUNDING: This work was funded by Tasly Biopharmaceuticals Co., Ltd.
12 May 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Carbohydrate supplementation for endurance exercise in the heat: a systematic review with practical recommendations
BACKGROUND AND OBJECTIVES: Endurance exercise performance is impaired by heat stress. Although the mechanisms are not yet fully understood, heat-induced increases in glycogenolysis may play a role. Therefore, carbohydrate supplementation recommendations may differ for endurance exercise in hot environments. This systematic review aims to investigate the efficacy of carbohydrate supplementation for endurance performance in hot environments. METHODS: Electronic databases, including PubMed, the Cochrane Library, MEDLINE, and SPORTDiscus were searched through to April 14, 2026 for studies evaluating endurance performance outcomes under heat stress in response to carbohydrate supplementation. Studies were included if the participants were healthy, aged 18-65 years, and at least recreationally active. A hot environment was defined as an ambient temperature >23 °C. Studies were required to involve an endurance-based and continuous exercise protocol lasting >30 min and a direct performance measure. This cutoff was selected based on evidence indicating glycogen stress at ~40 min under heat stress. In total, 3151 records were identified, and nine randomized, crossover studies met the inclusion criteria. Risk of bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias. Primary outcomes (endurance performance and carbohydrate oxidation rates) and secondary outcomes (including gastrointestinal symptoms, thermoregulation, and hydration markers) were extracted. Mean carbohydrate intake rates were calculated, and a narrative synthesis was performed. RESULTS: Mean carbohydrate ingestion rates ranged between 14 and 140 g·h-1, and the exercise trial duration ranged between ~50 and 152 min. Carbohydrate supplementation resulted in equivocal benefits to endurance performance: five studies found significant (p < 0.05) positive effects for carbohydrates (increases of 13.4%-19.3% in time to exhaustion and 3.3%-12.7% in time trial), and four studies found no significant effects. Although nonsignificant, three studies reported average improvements of 6.7%-11.6%, which may be meaningful in elite athletes. Most studies reported no differences in the respiratory exchange ratio between trials, indicating a preferential reliance on glycogen, which is in line with the literature. Carbohydrate ingestion during exercise in the heat did not influence markers of hydration, thermoregulation, or fatigue compared with placebo. Given that prior research shows gastrointestinal symptoms is common in endurance events in hot environments, the absence of symptom investigations in seven out of nine studies reviewed has important implications for interpreting findings. Overall, the results suggest that carbohydrate intake may not reduce glycogen breakdown during endurance performance in heat. Future research should help to better understand the underlying reasons, including any moderating effects of gastrointestinal distress. CONCLUSIONS: Carbohydrate intake during endurance exercise in the heat does not consistently improve exercise performance. Athletes may focus on maintaining hydration during exercise and practice gut-training, based on the wider literature. Future studies should fill the gaps in research, namely, the measurement of gastrointestinal symptoms, the mechanisms of exogenous carbohydrate use in heat, and different modes of exercise, including running, to enable the development of robust, evidence-based recommendations.
12 May 2026
Read appraisal →International health
Neglected tropical diseases in conflict-related humanitarian emergency settings: a systematic review of the literature
Neglected tropical diseases (NTDs) are a group of 21 conditions affecting an estimated 1 billion people worldwide, causing significant mortality and morbidity. WHO has targeted their control, elimination or eradication by 2030, while highlighting that conflict and humanitarian emergencies are risks to achieving this. A systematic review of peer-reviewed literature was undertaken, using terms related to conflict-related humanitarian emergencies (CRHE), based on the Armed Conflict Location & Event Data Project codebook terms for defining emergencies and terms including all WHO-defined NTDs. After a two-stage screening process, 26 papers were included. Common challenges for NTD programmes in CRHE were healthcare access, infrastructure, population movement and data quality. Multisectoral collaboration between actors in conflict-settings was offered as a learning for NTD programmes, along with community participation and decentralisation. Evidence gaps were identified in the knowledge base for certain NTDs and in high-quality evidence for interventions in CRHE. Collaboration is a key area for focused improvement for NTD programmes in CRHE. This should be across sectors and extend to research and analysis approaches, to ensure the most effective interventions are identified and implemented and that NTD road map targets can be achieved.
12 May 2026
Read appraisal →BMJ open
Understanding patient preferences, experiences and engagement with ambulatory heart rhythm monitoring: a scoping review.
OBJECTIVE: To review the literature reporting patient preferences for ambulatory heart rhythm monitoring (AHRM) and what factors affect experience and engagement. BACKGROUND: The prevalence of arrhythmia continues to rise and contributes significantly to outpatient care burden. There is limited understanding of patient experience and compliance with monitoring. As innovative technologies are developed and healthcare strategies move towards surveillance and prevention, understanding this is key. METHODS: A scoping review was conducted using guidance from the Joanna Briggs Institute and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The review included studies of adults under investigation or surveillance for arrhythmia with a range of devices (Holter monitor, patch device, event recorder, mobile cardiac telemetry, external and implantable loop recorders, wearables and other implantable cardiac devices) in ambulatory care settings worldwide. The final search was conducted on 3 January 2026 across Medline (PubMed), Embase (Ovid), Web of Science (Clarivate Analytics), Cumulative Index to Nursing and Allied Health Literature (EBSCOhost), PsycINFO (Ovid) and Google Scholar. Quantitative, qualitative, mixed methods, multiple methods and any type of review articles were included. RESULTS: 54 studies were eligible for inclusion from the initial search that identified 1320 articles. Two overarching themes emerged from the quantitative and qualitative data: patient factors and device factors affecting experience and engagement. Patient factors included clinical and demographic factors, education and expectations, experience and preferences and impact on daily life and healthcare. Device factors could be common to several devices, for example, skin irritation or device specific, for example, the nature of activation. CONCLUSION: Patient and device factors influence preferences for and experience and engagement with AHRM. While existing literature is incomplete and heterogeneous, it identifies key considerations that should be integrated into the development and testing of novel approaches for arrhythmia surveillance in healthcare contexts. REGISTRATION: https://doi.org/10.17605/OSF.IO/6K3W8 (Open Science Framework).
12 May 2026
Read appraisal →Psychological medicine
Cognitive effects of intravenous Ketamine in treatment-resistant depression: A systematic review
Intravenous (IV) low-dose ketamine has emerged as a promising treatment for patients with treatment-resistant depression (TRD). However, its impact on cognitive functioning remains unclear. This systematic review examines the cognitive and executive effects of IV ketamine in TRD, focusing on their relationship to depressive and suicidal outcomes. A systematic search of Cochrane, MEDLINE, Embase, and PsycINFO databases was conducted up to May 15, 2025, using the terms depression, cognition, and ketamine. This review was conducted in accordance with the PRISMA guidelines, and the protocol was registered in PROSPERO (ID: 1160487). Risk of bias was evaluated using the Cochrane RoB 2 tool for randomized trials and the ROBINS-I tool for non-randomized studies. Twenty-one studies, comprising approximately 900-1,180 participants with TRD, assessed cognitive domains of processing speed, working memory, attention, verbal and visual memory, cognitive flexibility, and executive control. Procognitive effects were frequently observed in processing speed and working memory, while attention results were preserved or modestly improved, and verbal and visual memory results were heterogeneous. Executive control, particularly inhibitory performance on Stroop paradigms, improved in several trials. Two studies directly examined cognition as it relates to suicidal behaviors. No cognitive deterioration was reported. Subanesthetic IV ketamine appears to preserve and enhance specific cognitive functions in TRD, notably across processing speed, working memory, and executive control. These procognitive effects, particularly in executive control, may mediate ketamine's antisuicidal action. Standardized longitudinal studies are warranted to clarify their durability and clinical significance.
12 May 2026
Read appraisal →European journal of pediatrics
Diagnostic accuracy of artificial intelligence versus 263 pediatric clinicians for childhood exanthems
Pediatric exanthematous diseases pose diagnostic challenges because clinical presentations overlap. To determine whether current artificial intelligence (AI) models achieve diagnostic accuracy within or above the performance distribution of pediatric residents and specialists for common rash-associated diseases. Participants and AI models were evaluated against definitive diagnoses confirmed by clinical features, laboratory findings, and consensus of two pediatric infectious disease specialists. A volunteer sample of 263 pediatric clinicians: 107 residents (years 1 through 4) and 156 specialists. Each clinician completed a blinded multiple-choice questionnaire with a clinical photograph and accompanying clinical data per case. The same cases were presented to three AI models: ChatGPT, Gemini, and Copilot. Among 263 clinicians (107 residents, 156 specialists), specialists scored higher than residents (median, 46 [IQR, 42-50] vs 41 [IQR, 36-46]; P < .001; r = 0.32). ChatGPT correctly diagnosed 53 of 61 cases (86.9%), Gemini 50 (82.0%), and Copilot 44 (72.1%). Both ChatGPT and Gemini exceeded the upper bound of the specialist population median 95% CI (47.17). All three AI models scored above the resident 95% CI upper bound (42.76). Disease-level accuracy ranged from 0% (insect bites, all models) to 100% (9 conditions, all models). Fourth-year residents scored higher than first- and second-year residents (P = .001; ε2 = 0.13).Conclusions: AI models given clinical data alongside images matched or exceeded specialist-level performance for pediatric exanthems. Accuracy varied by disease; failures clustered in conditions that require contextual reasoning. Physician oversight remains necessary where AI accuracy is lowest. What is Known • Childhood exanthematous diseases pose significant diagnostic challenges due to their overlapping clinical presentations. • Artificial intelligence models are becoming increasingly proficient in accurately diagnosing these conditions. What is New • In this diagnostic accuracy study of 61 cases evaluated by 263 clinicians and 3 artificial intelligence models, ChatGPT (86.9%) and Gemini (82.0%) exceeded the 95% CI upper bound of the specialist population median. Disease-level accuracy ranged from 0 to 100% across models • Artificial intelligence models given clinical data alongside images can match or exceed specialist-level accuracy for common pediatric exanthems, but failures in context-dependent diagnoses require physician oversight.
12 May 2026
Read appraisal →Neurology
IV Fosphenytoin for Acute Trigeminal Neuralgia: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial
BACKGROUND AND OBJECTIVES: Trigeminal neuralgia (TN) is a debilitating condition characterized by sudden, severe facial pain. IV medications for acute TN exacerbations have not been investigated in randomized controlled trials (RCTs). Oral medications are often insufficient for acute attacks, and surgery is not immediately available. We conducted an RCT to evaluate IV fosphenytoin therapy (IFT) as a rapid-acting rescue treatment for acute TN exacerbations. We hypothesized that, compared with placebo, IFT would reduce acute TN pain. METHODS: This multicenter, double-blind, placebo-controlled phase 3 trial was conducted at 6 institutions (April 2023-April 2024). Patients with classical or idiopathic TN who were experiencing TN exacerbation (numerical rating scale [NRS] score ≥5) were recruited from emergency and outpatient settings, and all study drugs were administered during hospital admission. Participants were randomized 1:1 to IFT (initial dose: 18 mg/kg, maintenance: 7.5 mg/kg for 4 days) or placebo. The primary end point was the change in NRS pain scores from baseline to 120 minutes after the initial dose. Secondary end points included attack frequency, the proportion achieving ≥50% NRS score improvement, and adverse events. Data were collected by blinded personnel and analyzed using repeated-measures mixed-effects models. RESULTS: Of 31 participants screened, 22 were randomized; 1 placebo-assigned participant did not meet the NRS ≥5 criterion and was excluded, leaving 21 for analysis (IFT: 11; placebo: 10). Participants had a mean age of 65.2 years (range 42-83; 8 men, 13 women). Compared with placebo, IFT significantly reduced pain (NRS change: -6.1 vs -2.4; between-group difference -3.8; 95% CI -6.4 to -1.1; p = 0.008), decreased attack frequency within 24 hours (2.5 ± 5.3 vs 16.1 ± 19.6, difference -13.6; 95% CI -26.4 to -0.7), and increased the proportion achieving ≥50% NRS improvement (90.9% vs 40.0%, difference 50.9%; 95% CI 5.6%-81.6%). Adverse events (somnolence, hypotension, and nausea) were mild and transient. DISCUSSION: IFT provided rapid and well-tolerated pain relief in acute TN exacerbations. Although findings are preliminary because of the small sample size, IFT may be a viable short-term treatment option, meriting further investigation. TRIAL REGISTRATION INFORMATION: Japan Registry of Clinical Trials, jRCT2011220043. Registered March 3, 2023. First enrollment April 25, 2023. jrct.mhlw.go.jp/en-latest-detail/jRCT2011220043. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that in patients with acute TN exacerbations, IV fosphenytoin is superior to placebo in reducing pain intensity at 120 minutes.
12 May 2026
Read appraisal →The New England journal of medicine
Left Ventricular Unloading in High-Risk Percutaneous Coronary Intervention
BACKGROUND: Complex percutaneous coronary intervention (PCI) in patients with severely impaired left ventricular function carries a high risk of death and complications. Whether percutaneous left ventricular unloading improves outcomes remains unclear. METHODS: We randomly assigned 300 patients with severe left ventricular dysfunction and extensive coronary artery disease in a 1:1 ratio to a strategy of elective unloading with a microaxial flow pump or to standard care during planned complex PCI. The primary outcome was a hierarchical composite that included death from any cause, disabling stroke, spontaneous myocardial infarction, hospitalization for cardiovascular causes, or periprocedural myocardial injury at a minimum of 12 months, as analyzed according to a win ratio. RESULTS: A total of 148 patients were assigned to receive a microaxial flow pump and 152 to receive standard care. At a median of 22 months (interquartile range, 16 to 30), 36.6% of pairwise comparisons favored the microaxial flow pump, and 43.0% favored standard care (win ratio, 0.85; 95% confidence interval [CI], 0.63 to 1.15; difference, -6.4 percentage points; P = 0.30). Death from any cause occurred in 47 patients in the microaxial-flow-pump group and 33 in the standard-care group (hazard ratio, 1.54; 95% CI, 0.99 to 2.41). There was no material between-group difference in the risk of bleeding or vascular complications. CONCLUSIONS: Among patients with severely impaired left ventricular function undergoing complex PCI, elective left ventricular unloading with a microaxial flow pump did not reduce the risk of major adverse clinical outcomes at a minimum of 12 months. (Funded by the U.K. National Institute for Health and Care Research; CHIP-BCIS3 ClinicalTrials.gov number, NCT05003817.).
12 May 2026
Read appraisal →PloS one
Smartphone-based accurate pothole depth estimation using monocular RGB and LiDAR-guided deep learning
The maintenance of the road infrastructure is vital to ensure public safety and the efficiency of transportation, but the unpredictable and widespread occurrence of potholes often hinders continuous monitoring. Traditional detection approaches typically rely on expensive equipment or manual inspections, rendering them impractical for large-scale, real-time deployment. To overcome these limitations, we present a low-cost and scalable pothole assessment framework that leverages the built-in LiDAR sensors available in modern smartphones in combination with a hybrid deep learning pipeline. The proposed system fuses RGB imagery with LiDAR-guided refinement to achieve precise depth estimation and accurate pothole segmentation, further enhanced through post-processing for improved boundary alignment. A comprehensive dataset of 25,000 RGB-LiDAR image pairs, collected under various road and lighting conditions, was curated to train and evaluate the framework. Extensive experiments demonstrate that our approach achieves high accuracy in both depth estimation and pothole delineation, significantly outperforming existing methods. This work highlights the potential of lightweight smartphone-based solutions for real-time automated road condition assessment, offering a practical and economical tool for modern infrastructure management.
12 May 2026
Read appraisal →European radiology experimental
Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study
OBJECTIVE: To evaluate the effects of arm positioning and reconstruction algorithms on radiation dose and image quality of abdominal CT. MATERIALS AND METHODS: Abdominal CT scans were performed using a customized body phantom at 100 kVp with automatic tube current modulation based on noise indices (NI) of 9, 11, and 13 across seven arm positions: arms up (AU), arms down (AD), arms down with one or two-layered air cushions (ADAC, ADAC2), arms on the belly (AB), and arms on the belly with one or two-layered air cushions (ABAC, ABAC2). Images were reconstructed using filtered back projection (FBP), iterative reconstruction, and deep learning-based reconstruction (DLIR). Radiation dose was recorded. Quantitative image quality metrics included image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), sharpness, and structural similarity. Qualitative assessment of sharpness, noise, artifacts, and overall image quality was performed using fixed-dose AU-FBP images as the reference standard. RESULTS: Compared with the AU position, radiation dose increased by up to 83% in non-AU configurations, with similar trends across all NI settings. In contrast, air cushion use caused only minor, configuration-dependent dose changes. Qualitatively, non-AU FBP images showed marked degradation, whereas DLIR enabled selected non-AU configurations to approach near-reference diagnostic quality. Air cushion-related benefits were modest overall but more apparent in AB than AD configurations. CONCLUSION: Arm positioning is the primary determinant of radiation dose and image quality in abdominal CT. When arm elevation is not feasible, DLIR effectively mitigates image quality degradation, while air cushions provide limited, configuration-dependent benefits. RELEVANCE STATEMENT: When arms cannot be raised during abdominal CT, avoiding arms-on-belly and using air cushions may help maintain image quality without substantially increasing radiation dose, while deep learning image reconstruction further enhances diagnostic performance under restricted positioning conditions. KEY POINTS: Arm positioning is a major determinant of radiation dose and image quality in abdominal CT. Arms-on-belly positioning produces poorer image quality despite increased radiation dose. DLIR improves image quality across arm positions but does not eliminate positioning-related degradation.
12 May 2026
Read appraisal →JMIR medical informatics
Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study
BACKGROUND: Coughing is a common clinical symptom and a protective respiratory reflex closely associated with various respiratory system diseases. The acoustic characteristics of cough sounds are influenced by underlying pathological factors, with distinct acoustic signatures corresponding to different etiologies. Through rigorous analysis of these sounds, rapid identification and preliminary diagnosis of related conditions may be achieved. This approach holds great potential for broad application in mobile health and ubiquitous health platforms. OBJECTIVE: This study aimed to explore the application of acoustic analysis of cough sounds in the diagnosis of respiratory diseases to enhance the diagnostic efficiency of health care professionals. METHODS: In this study, we conducted extensive data collection, including voluntary cough audio recordings from patients diagnosed with respiratory diseases (eg, chronic obstructive pulmonary disease, lung cancer, COVID-19, and pneumonia) and from healthy participants. A total of 2610 audio samples were collected. We incorporated a channel attention mechanism (CAM) into the final convolutional block of each residual block in the ResNet18 neural network, thereby constructing the CAM-ResNet18 neural network model. The recorded cough audio samples were converted into spectrograms to form the input dataset for model training. The CAM-ResNet18 model was trained on the training set of this dataset, with iterative parameter adjustments until convergence was achieved. Finally, spectrograms from the test set were fed into the pretrained model for accurate classification of the cough-related conditions. RESULTS: Experimental results on the collected audio dataset demonstrate that the proposed CAM-ResNet18 model achieves an accuracy of 83.9% and an average F1-score of 82.52% in classifying 5 types of cough sounds. In comparison, the traditional ResNet18 model achieves an accuracy of 78.16% and an average F1-score of 78.29%, indicating a clear performance improvement with the integration of the CAM. CONCLUSIONS: The experimental results validate the effectiveness of the proposed method, highlighting its significant potential for application in clinical diagnosis.
12 May 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Vocal Biomarkers of Childhood Trauma: A Machine-Learning Approach to Speech Analysis
PURPOSE: Childhood trauma can disrupt communication, yet early signs often go unrecognized in regions affected by ongoing war, where immediate physical needs take precedence. Vocal biomarkers-acoustic features linked to emotional and motor regulation-offer a promising, noninvasive means of detecting trauma-linked speech disruptions. This study applied a hybrid framework to distinguish trauma exposure in Arabic-speaking children living amid active conflict. The aim was to support scalable, speech-based tools for early trauma identification in low-resource, humanitarian settings. METHOD: We analyzed 200 publicly available recordings of spontaneous speech from Arabic-speaking girls (ages 8-12 years): 100 trauma-exposed participants from Gaza (Palestinian) and 100 non-exposed controls from Jordan. Core acoustic features (fundamental frequency [F0], jitter, shimmer, harmonics-to-noise ratio [HNR], voice onset time [VOT], first formant, second formant) informed statistical testing and theory-driven composite indices. Exploratory features-including Mel-frequency cepstral coefficients and eGeMAPSv02 descriptors-were used to train binary classification models. Three classifiers (random forest, ridge regression, and logistic regression) were evaluated using nested cross-validation and bootstrap resampling. Composite indices were combined into a 0-10 Trauma Risk Score. Generalizability was assessed using an independent Lebanese cohort (n = 80), with the trained classifier applied using fixed exploratory features and preprocessing parameters. Core features were tested post hoc for cross-cohort stability. RESULTS: Trauma-exposed children showed reduced F0 and HNR, elevated shimmer and jitter, and prolonged VOT (Cohen's d > 1.2). Binary classification models achieved strong performance (area under the curve [AUC] = .89-.92); logistic regression reached AUC = .996 under cross-validation. Composite indices (AUCs > .90) stratified 68% into Moderate/High Trauma Risk. Lebanese validation confirmed generalizability, with theory-driven features showing stable predictive patterns. CONCLUSIONS: Vocal biomarkers reliably distinguished trauma exposure in Arabic-speaking children using a simple logistic regression model. This strong performance highlights the potential of speech-based tools as scalable, noninvasive methods for early trauma detection. Further validation is needed to support their use in diverse humanitarian and conflict-affected settings.
12 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Thrombolysis in spinal cord infarction: Case report and systematic review
INTRODUCTION: Spinal cord infarction (SCI) is a rare but severe cause of acute myelopathy, and evidence supporting reperfusion therapies remains limited. METHODS: We report a 73-year-old man with spontaneous cervical SCI treated with intravenous alteplase and perform a systematic review (database search from inception to 13 November 2025) of case reports/series describing intravenous (IV) or intra-arterial (IA) thrombolysis for SCI, extracting data about clinical presentation, diagnostic and therapeutic work-up and functional outcomes. RESULTS: Our patient improved after thrombolysis and achieved functional independence at 3 months (modified Rankin Scale [mRS] 2). The review identified 21 studies (19 case reports, 2 case series) totaling 25 patients; including our case, 26 patients were analyzed. Mean age was 57.4 years (range 14-83), and anterior spinal artery (ASA) syndrome was the most common presentation. Most patients received IV thrombolysis; 4 underwent IA therapy (alone or combined). Considering the last available assessment, 14/25 patients (56.0%) had a favorable outcome (mRS 0-2), 8/25 (32.0%) had mRS 3, and 3/25 (12.0%) had mRS 4-5. No symptomatic treatment-related hemorrhagic complications were reported. DISCUSSION AND CONCLUSION: Published experience suggests that thrombolysis in carefully selected patients with suspected SCI may represent a reasonably safe therapeutic option, but evidence is limited and subject to major bias. Prospective registries and multicenter studies are needed to clarify safety, efficacy, and selection criteria.
12 May 2026
Read appraisal →PloS one
Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province
The differential understanding of the landslidetriggering mechanisms across various geomorphic units is vital for enhancing regional disaster prevention. This study investigates the Loess Plateau area in northern Shaanxi and the Qinba Mountain Area in southern Shaanxi as the study areas, and constructs a 15dimensional evaluation factor system covering topography, geology, vegetation, and human activities. After eliminating collinearity factors via double tests of the Pearson correlation coefficient and variance inflation factor, Bayesian optimization is used to optimize hyperparameters for CatBoost, Random Forest, LightGBM, and XGBoost, and the SHAP framework is combined to perform global attribution, single-factor dependency analysis, and interaction-effect quantification. The results demonstrate that CatBoost performs best across both geomorphic areas, achieving AUCs of 0.8307 and 0.8252 in the test sets. In northern Shaanxi, a discrete patch pattern driven by "human-water" coupling is observed, with population density and the topographic moisture index contributing 37.2%. In contrast, Southern Shaanxi exhibits a continuous, band-shaped distribution controlled by "structure-topography" is observed, and elevation and lithology dominate the model's decision-making. The SHAP-dependent map identified cross-geomorphic differences in the population density threshold of 99.83 people/km2, the slope threshold of 2.21° in northern Shaanxi, and the elevation threshold of 1194.73m in southern Shaanxi. Furthermore, the interaction network revealed an antagonistic effect of DEM and POP in northern Shaanxi and of DEM and LULC in southern Shaanxi. The framework proposed a quantitative, explainable basis for differentiated disaster prevention strategies in this research.
11 May 2026
Read appraisal →International health
Global trends in travel-related antimicrobial resistance: a systematic review, 2020-2024
Antimicrobial resistance (AMR) is a growing global health threat, with international travel playing a key role in the spread of resistant bacteria. This systematic review examines trends in travel-associated AMR from 2020 to 2024. A search of PubMed, Scopus and Web of Science identified 10 studies involving 359 AMR isolates. Using the Newcastle-Ottawa scale, the study quality was assessed and findings were synthesised to identify patterns in prevalence, diversity and geographic spread. Results revealed a consistent rise in travel-associated AMR, particularly from regions such as Southeast Asia and Africa, which acted as major sources of diverse resistant pathogens. These include extended spectrum beta-lactamase-producing Escherichia coli, multidrug-resistant (MDR) Corynebacterium diphtheriae and colistin-resistant Enterobacterales. The number of MDR strains increased over time, making up 15.3% of cases by 2024. Healthcare exposure during travel emerged as a significant risk factor. Overall, the prevalence and diversity of AMR bacteria linked to travel have risen steadily, highlighting the urgent need for global cooperation. Enhanced surveillance, antimicrobial stewardship, infection control measures and international collaboration are essential to curb the spread of these dangerous pathogens.
11 May 2026
Read appraisal →American journal of epidemiology
Predicting first onset of suicide attempt among children with suicidal ideation or non-suicidal self-injury using machine learning: a prospective population-based cohort study
While children with suicidal ideation or non-suicidal self-injury (NSSI) are at high risk of suicide, most do not attempt suicide. This study aims to identify predictors of first suicide attempts among children with suicidal thoughts or NSSI. We utilized longitudinal data from the Adolescent Brain and Cognitive Development study to examine a broad range of risk factors in children reporting suicidal ideation (N = 344, mean age = 9.93) or NSSI (N = 261, mean age = 9.88) at baseline, predicting first attempt of suicide during a 4-year follow-up. Predictive models were developed through bivariate feature selection followed by Discrete-Time Random Survival Forest machine learning, identifying predictors of subsequent suicide attempts. During the follow-up, 40 of 344 children (11.6%) with suicidal ideation, and 32 of 261 children (12.3%) with NSSI at baseline initiated suicide attempts. For suicidal ideation, risk for attempting suicide increased with caregiver-reported NSSI, witnessing domestic violence, severity of suicidal thoughts, being female, online social screen use, and less parental supervision. For NSSI, risk of suicide attempt increased with witnessing domestic violence, anxiety disorders, caregiver-reported NSSI, being female, and disruptive, impulse control, and conduct disorders. Our findings shed light on the development of population-based suicide prevention strategies for children.
11 May 2026
Read appraisal →Environmental health : a global access science source
Building the model: a review of input structures in extreme temperature-health
BACKGROUND: The health impacts of extreme temperatures have been extensively studied through epidemiological models. However, limited attention has been paid to the specification of these models, particularly regarding input structure and model selection. Although exposure metrics and statistical techniques have evolved over time, a comprehensive synthesis of the variables included in these models, and the rationale behind their inclusion, is still lacking. This gap limits the comparability of studies and may compromise the robustness of temperature-health evidence. METHODS: We conducted a systematic review of peer-reviewed studies published between 2014 and 2024 that employed quantitative epidemiological models to estimate the association between extreme temperatures and health outcomes. Following PRISMA guidelines, we selected 119 studies through searches conducted in Scopus, PubMed, and Web of Science. Each study was analysed in terms of spatial coverage, modelling framework, and model inputs. Inputs were classified into six functional groups: thermal exposures; environmental covariates (including both non-thermal meteorological variables and air pollutants); temporal controls; socio-demographic factors; health system indicators; and built environment characteristics. RESULTS: Substantial heterogeneity was observed in both input selection and model specification. Daily mean temperature was the dominant exposure metric, though rarely justified over alternatives. Environmental covariates were inconsistently included: while relative humidity was frequent, other meteorological modifiers and air pollutants were often omitted without clear rationale. Temporal adjustments were common but heterogeneous. Distributed lag non-linear models were the prevailing framework, varying greatly in lag structure, spline specification, and covariate integration. Socio-economic, health, and infrastructural indicators appeared in less than one third of studies, typically as effect modifiers in meta-regression analyses, highlighting uneven integration of contextual determinants. No consensus currently exists on what constitutes a minimum model specification necessary to ensure reliable and interpretable effect estimates. CONCLUSIONS: Current temperature and health modelling remains fragmented, with notable variability in input specification and transparency. Strengthening methodological coherence through clearer guidance on input selection is essential. Greater integration of socio-economic and infrastructural variables would further enhance models' capacity to capture contextual vulnerability. To ensure reliability and policy relevance, future research should develop shared guidelines for input specification, define minimum modelling standards, and promote transparent reporting of analytical decisions.
11 May 2026
Read appraisal →Journal of burn care & research : official publication of the American Burn Association
Hyperbaric Oxygen Therapy in Burn Care: A Systematic Review of Current Evidence
Hyperbaric oxygen therapy (HBOT) involves inhaling 100% oxygen at pressures exceeding one atmosphere within a chamber. It is used across several specialties and has been proposed as an adjunct in burn management to enhance healing and improve outcomes. Despite increasing interest, evidence supporting its efficacy in burn care remains inconsistent, with no clear consensus in practice. A literature search was conducted in September 2025 across PubMed, Cochrane Library, and Embase without date restrictions to identify studies evaluating HBOT for burn treatment. Inclusion criteria were human studies, English-language articles, and full-text availability. Study selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two reviewers screened titles and abstracts, and a third resolved discrepancies. Eligible studies described HBOT as a burn treatment and reported clinical outcomes. Thirteen studies met inclusion criteria: 5 randomized controlled trials, 7 cohort studies, and 1 case-control study, evaluating 566 burn patients. Burn severity, HBOT protocols, and outcomes varied substantially. Several studies reported reduced need for surgery and shorter hospital stays with HBOT. Trends toward improved healing and lower infection risk were noted, while mortality benefits were inconsistent. Heterogeneity in design and treatment regimens limited synthesis via meta-analysis. Hyperbaric oxygen therapy shows promise as an adjunct in burn care, improving healing and reducing complications. However, variability and inconsistent outcomes limit definitive conclusions. Well-designed randomized trials are needed to establish standardized protocols and clarify their clinical role in burn management. Until then, burn centers may consider HBOT for acute injuries and threatened grafts.
11 May 2026
Read appraisal →Journal of burn care & research : official publication of the American Burn Association
FGF2 in Burn Wound Healing: From Molecular Function to Clinical Application-A Systematic Review
Burn injuries trigger complex inflammatory and metabolic cascades that impair wound healing and increase morbidity. Fibroblast growth factor 2 (FGF2), a potent mediator of cellular proliferation, angiogenesis, and extracellular matrix remodeling, has emerged as a promising therapeutic agent in burn care. This systematic review evaluated the biological mechanisms, delivery strategies, and clinical outcomes associated with FGF2 in burn wound healing to clarify its therapeutic value and translational potential. Following PRISMA 2020 standards, a structured literature search was conducted across PubMed, EMBASE, Web of Science, Cochrane, and Google Scholar to identify studies investigating FGF2 in thermal injuries. Eligible records included clinical trials, in vivo models, and in vitro experiments. Methodological quality and risk of bias were assessed using the Newcastle-Ottawa Scale, the SYRCLE tool, and the Oxford Levels of Evidence. Thirty-three studies (1992-2025) met inclusion criteria, encompassing randomized controlled trials, animal models, and mechanistic analyses. Fibroblast growth factor 2 accelerated repair by stimulating fibroblast proliferation, keratinocyte migration, angiogenesis, and matrix organization. Topical FGF2 formulations shortened healing time and improved scar quality in partial-thickness burns. Innovative carriers, including hydrogels, liposomes, and gene-activated matrices, enhanced bioavailability and sustained local effects. In diabetic and complex burn models, FGF2 mitigated inflammation, preserved barrier integrity, and promoted re-epithelialization. Efficacy depended on dosage and wound characteristics. Studies reported favorable safety profiles with minimal and mild adverse events. Fibroblast growth factor 2 demonstrates regenerative and immunomodulatory potential in burn management. Broader adoption requires harmonized regulatory evaluation, optimized delivery systems, and multicenter validation to define its role in precision-guided burn care.
11 May 2026
Read appraisal →Journal of burn care & research : official publication of the American Burn Association
Metabolic and Immunomodulatory Effects of α-Ketoglutarate in Burn Injuries: A Systematic Review
Severe burns unleash a hyper-catabolic and immunosuppressive state that erodes lean tissue and delays repair. Alpha-ketoglutarate (α-KG), usually delivered enterally as the dipeptide ornithine α-ketoglutarate (OKG), feeds the tricarboxylic-acid cycle, donates nitrogen for amino-acid synthesis, and modulates collagen formation and immunity. We systematically appraised the biological mechanisms, delivery strategies, and clinical outcomes associated with α-KG/OKG in burn care to clarify its therapeutic value and translational readiness. A PRISMA-2020-compliant search of PubMed, EMBASE, Web of Science, Cochrane, and Google Scholar up to May 2025 identified clinical, in vivo, and relevant in vitro studies linking α-KG or OKG with thermal injury. Study quality was assessed using the Newcastle-Ottawa Scale, SYRCLE Risk-of-Bias tool, and Oxford Levels of Evidence. Fifteen studies published between 1984 and 2024 met inclusion criteria, including clinical, animal, and mechanistic work, most conducted in France. Across studies, OKG supplementation replenished glutamine and arginine pools, improved nitrogen balance, preserved muscle mass, and promoted wound healing through enhanced collagen synthesis and immune modulation. Clinical data confirmed improved nitrogen retention, reduced muscle breakdown, and faster wound closure. Preclinical studies further showed that α-KG preconditioning enhances stem cell-driven regeneration and vascularization. Additional effects, such as improved glucose tolerance and hepatic enzyme preservation, suggest some benefits occur independently of glutamine pathways. This review highlights α-KG and OKG as promising adjuncts to enhance metabolic recovery, wound repair, and immune competence after burns. Evidence supports improved nitrogen balance and healing, but findings remain limited by heterogeneity and small cohorts, warranting renewed and well-powered studies.
11 May 2026
Read appraisal →Journal of medical Internet research
AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges
The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Predicting Intracranial Aneurysm Rupture Risk and Intervention Outcomes Using Denoising-Enhanced CTA
BACKGROUND AND PURPOSE: Our aim was to develop a comprehensive multimodal framework for assessing the rupture risk of intracranial aneurysms and predicting intervention outcomes. In addition, it seeks to a novel denoising algorithm to enhance the quality of CTA images, thereby improving morphologic profiling. MATERIALS AND METHODS: This retrospective multicenter study included 352 patients with intracranial aneurysms who underwent CTA. A multimodal framework was developed, integrating 3 complementary feature sets: clinical variables, radiomic texture features, and deep learning-derived aneurysm morphologic data. A novel denoising algorithm was applied to improve image quality, thereby enhancing prediction performance. Model validation was performed through cross-validation, using multiple end points, including the Hunt-Hess, World Federation of Neurosurgical Societies (WFNS), and mRS grading systems. RESULTS: The multimodal framework demonstrated robust performance, achieving an area under the curve (AUC) of 0.896 [0.819-0.973] for aneurysm rupture prediction, outperforming conventional single-technique models (radiomics-based model: 0.752 [0.693-0.809]; deep learning-based model: 0.823 [0.789-0.827]). Incorporating the denoising technique further enhanced performance, with the AUC for rupture prediction increasing to 0.908 [0.836-0.981]. In clinical grading tasks, the model showed strong efficacy, achieving an AUC of 0.907 [0.845-0.968] for Hunt-Hess grading, 0.883 [0.662-0.988] for World Federation of Neurosurgical Societies grading, and 0.926 [0.879-0.973] for mRS. CONCLUSIONS: Our system demonstrates promising performance in predicting aneurysm rupture and clinical grading assessments, indicating its potential for comprehensive aneurysm evaluation. Moreover, the proposed denoising method effectively mitigates noise interference, enhances morphologic edge features, and improves the accuracy of existing models.
11 May 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Screening for high-risk drinking and alcohol use disorder: update of the 2023 national clinical practice guideline
BACKGROUND: Current screening tools for harmful alcohol consumption have fallen out of step with recent guidance on the health risks of alcohol. To address this gap, the Canadian Research Initiative in Substance Matters updated the screening recommendations for high-risk drinking and alcohol use disorder (AUD) in the 2023 national clinical practice guideline. METHODS: Following a systematic review of literature published between Jan. 1, 2013, and Feb. 24, 2023, that examined screening tools for high-risk drinking and AUD, the updated recommendations were developed by a multidisciplinary national committee, which included people with lived and living experience. We scored the recommendations and certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation tool. We used the Appraisal of Guidelines for Research and Evaluation II instrument and the Guidelines International Network's principles for disclosure of interests and management of conflicts to ensure the update met international standards for transparency, high quality, and methodological rigour. RECOMMENDATIONS: Acknowledging that time constraints are the most commonly reported barrier to universal alcohol screening, we developed 5 recommendations involving a simple screening method, to identify and address both unhealthy alcohol consumption and more serious problems related to alcohol. The recommendations include asking all patients about alcohol consumption and providing educational support to all those who drink above Canada's Guidance on Alcohol and Health's low-risk threshold. We propose a simple screening algorithm to optimize and tailor further intervention, including when to assess for possible AUD. INTERPRETATION: The revised screening recommendations represent a timesaving and pragmatic approach intended to be a resource for universal screening for alcohol risks and problems. The recommendations streamline the process of identifying and addressing the health needs of those who consume alcohol in a hazardous way or may have more serious problems related to alcohol.
11 May 2026
Read appraisal →European journal of pediatrics
Oral dextrose gel vs standard care for the treatment of hypoglycemia in high-risk neonates: an open-label randomized controlled trial
UNLABELLED: To determine whether 40% dextrose gel with oral feeds reduces NICU admission for asymptomatic hypoglycemia compared with standard care with oral feeds in at-risk neonates within 48 h of life. Secondary objectives were to assess rebound and recurrent hypoglycemia, hyperglycemia, and exclusive breastfeeding at discharge and at 6 weeks. Open-label randomized controlled trial conducted at a tertiary hospital in South India between April 2023 and May 2024. Neonates born at ≥ 35 weeks of gestation with asymptomatic hypoglycemia within the first 48 h of life were randomly assigned to receive either 200 mg/kg of oral 40% dextrose gel with oral feeds or standard care with oral feeds alone. The primary outcome was NICU admission for hypoglycemia. A total of 193 neonates were enrolled, with 98 in the standard care group and 95 in the gel group. Baseline characteristics were comparable. NICU admission for hypoglycemia occurred in 4.2% in gel group and 12.2% in standard care group (RR 0.34, 95% CI 0.11-1.03; p = 0.06), with a number needed to treat of 13. Exclusive breastfeeding was higher in gel group at discharge (97% vs 76%; p < 0.001) and at 6 weeks (90% vs 69%; p < 0.001). Rebound hypoglycemia, recurrent hypoglycemia, and hyperglycemia were similar between groups. CONCLUSION: Oral dextrose gel was associated with fewer NICU admissions, but the primary outcome did not reach statistical significance. Improved breastfeeding rates were observed as secondary outcomes. These findings should be interpreted cautiously and confirmed in adequately powered studies. TRIAL REGISTRATION: Clinical Trials Registry of India (CTRI/2023/02/050027). WHAT'S KNOWN: • Oral 40% dextrose gel is an eff ective fi rst-line treatment for neonatal hypoglycemia and has been shown to reduce treatment failure and intravenous glucose use in previous trials. • Existing evidence is predominantly from high-income settings, with limited randomized data from low- and middle-income countries assessing pragmatic outcomes such as NICU admission. WHAT IS NEW: • In this open-label randomized controlled trial, oral dextrose gel was associated with fewer NICU admissions for hypoglycemia (4.2% vs 12.2%) and higher exclusive breastfeeding rates at discharge and 6 weeks compared with standard care. • These fi ndings support the potential utility of dextrose gel as a simple, low-cost intervention for managing neonatal hypoglycemia in resource-limited settings.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Neuroimaging Predictors of Futile Recanalization in Anterior Circulation Stroke: A Systematic Review and Meta-Analysis
BACKGROUND: Mechanical thrombectomy (MT) has transformed the management of large vessel occlusion stroke; however, a considerable proportion of patients fail to achieve functional recovery despite successful recanalization. This futile recanalization underscores the need to better understand imaging biomarkers that may predict poor outcomes after endovascular therapy. PURPOSE: To systematically review and synthesize evidence on neuroimaging biomarkers associated with futile recanalization after MT in acute ischemic stroke. DATA SOURCES: PubMed, Scopus, Web of Science, and EMBASE databases were searched up to March 2025, supplemented by manual reference screening. STUDY SELECTION: Eligible studies enrolled adult patients with anterior-circulation stroke who underwent thrombectomy, reported pretreatment neuroimaging findings, and evaluated functional outcomes despite successful reperfusion. Case reports, reviews, abstracts, and nonhuman studies were excluded. DATA ANALYSIS: Two reviewers independently screened, extracted data, and assessed study quality using the Prediction Model Risk of Bias Assessment Tool. Random-effects meta-analysis was performed where appropriate. Heterogeneity, publication bias, and sensitivity analyses were undertaken. DATA SYNTHESIS: Fifty-one studies were included. Lower ASPECTS scores were strongly associated with futile recanalization (OR = 0.81). Core infarct volume showed inconsistent associations, whereas perfusion and collateral markers, including elevated hypoperfusion intensity ratio, delayed venous outflow, and poor collateral scores, demonstrated strong predictive value. Chronic white-matter injury, leukoaraiosis, and cerebral atrophy were also consistently associated with worse outcomes. LIMITATIONS: Limitations included heterogeneity in imaging protocols and thresholds; predominance of retrospective single-center cohorts; limited direct comparison across imaging modalities; and variable adjustment for confounders. CONCLUSIONS: Neuroimaging biomarkers, particularly ASPECTS, perfusion-derived metrics, collateral status, and markers of chronic small vessel disease, provide important prognostic information for identifying patients at risk of futile recanalization despite successful thrombectomy.
11 May 2026
Read appraisal →European heart journal
Temporal modulation of antiplatelet therapy in high-risk patients undergoing complex percutaneous coronary intervention: the TAILORED-CHIP randomized clinical trial
BACKGROUND AND AIMS: Limited data exist on optimal antiplatelet strategies for high-risk patients undergoing complex percutaneous coronary intervention (PCI). This study aimed to investigate the efficacy and safety of tailored antiplatelet treatment with temporal modulation of the intensity of platelet inhibition in patients undergoing complex high-risk PCI. METHODS: We randomly assigned 2018 patients with high-risk anatomical or clinical characteristics undergoing complex PCI to a tailored antiplatelet strategy with early escalation (low-dose ticagrelor at 60 mg twice daily plus aspirin <6 months) and late de-escalation (clopidogrel monotherapy >6 months) or dual antiplatelet therapy (clopidogrel plus aspirin for 12 months). The primary outcome was a composite of death from any cause, myocardial infarction, stroke, stent thrombosis, unplanned urgent revascularization, and clinically relevant bleeding (Bleeding Academic Research Consortium Type 2, 3, or 5) at 12 months. RESULTS: The mean age was 64.0 years, 22.6% had left main PCI, 19.5% had complex bifurcation PCI, 84.1% had diffuse long lesions, 93.7% had multivessel PCI, and 36.7% had medically treated diabetes. At 12 months, a primary outcome event occurred in 105 patients (10.5%) assigned to tailored antiplatelet therapy and in 89 patients (8.8%) assigned to dual antiplatelet therapy [hazard ratio, 1.19; 95% confidence interval (CI), 0.90-1.58; P = .21]. The incidence of major ischaemic events appeared to be similar in both groups. The incidence of clinically relevant bleeding at 12 months was 7.2% in the tailored-therapy group and 4.8% in the dual-therapy group (difference, 2.45% points; 95% CI, 0.37-4.53). CONCLUSIONS: Among high-risk patients undergoing complex PCI, tailored antiplatelet strategy with early escalation and late de-escalation, as compared with dual antiplatelet therapy, did not decrease the incidence of primary net adverse events at 12 months. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov number, NCT03465644.
11 May 2026
Read appraisal →PloS one
Development and evaluation of an ontology for non-invasive respiratory support in acute care
Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive mechanical ventilation. However, despite the rapidly expanding use of NIRS, there remains a significant challenge to its optimal use across all medical circumstances. It lacks a unified ontological structure, complicating guidance on NIRS modalities across healthcare systems. In this study, we introduced NIRS ontology to support knowledge representation in acute care settings by providing a unified framework that enhances data clarity and interoperability, laying the groundwork for future clinical decision-making research. We developed NIRS ontology using the Web Ontology Language (OWL) and Protégé to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning using a sample of 6 patient scenarios and used SPARQL queries to retrieve and test targeted inferences. The ontology has 145 classes, 11 object properties, and 18 data properties across 949 axioms that establish concept relationships. To standardize clinical concepts, we added 392 annotations, including descriptive definitions based on controlled vocabularies. SPARQL queries successfully validated all test cases (rules) by retrieving appropriate patients' outcomes: for instance, a patient treated with HFNC (high-flow nasal cannula) for 2 hours due to acute respiratory failure may avoid endotracheal intubation. This NIRS ontology formally represents domain-specific concepts, including ventilation modalities, patient characteristics, therapy parameters, and outcomes. SPARQL query evaluations across clinical scenarios confirmed the ontology's ability to support rule‑based reasoning and therapy recommendations, providing a foundation for consistent documentation practices, integration into clinical data models, and advanced analysis of NIRS outcomes. In conclusion, this proof-of-concept NIRS ontology demonstrates how clinical concepts can be formally represented and queried, offering a foundation for future validation, EHR integration, and evidence-based refinement of NIRS practices.
11 May 2026
Read appraisal →PloS one
Association between hemoglobin dynamic trajectories and 28-day mortality in elderly patients with sepsis: A retrospective cohort study using the MIMIC-IV database
BACKGROUND: Anemia is a common complication in sepsis and is associated with poor prognosis. However, there are few studies on the dynamic changes in hemoglobin (Hb) levels and their relationship with prognosis in elderly patients with sepsis. This study aims to retrospectively investigate the relationship between Hb trajectories and 28-day mortality in this population. METHODS: This retrospective observational study used data from 4,961 elderly patients with sepsis extracted from the MIMIC-IV database(2008-2019). Hb trajectories were identified using a Joint Latent Class Model (JLCM) for longitudinal pattern classification. Following validation of proportional hazards assumptions (Schoenfeld residual test), we assessed the prognostic relationship between Hb trajectories and 28-day mortality using multivariable Cox regression models and Kaplan-Meier survival curves. Sensitivity analyses included sex-stratified multivariable Cox regression, subgroup analysis, and E-value assessment. RESULTS: Hb trajectories were classified into three patterns: Class 1 with persistently low levels, Class 2 with medium levels and a gradual decline, and Class 3 with a rapid decline from higher levels. Using Class 2 as the reference, multivariable Cox regression showed that Class 1 (HR = 1.38, 95% CI = 1.18-1.62, p < 0.001) and Class 3 (HR = 1.21, 95% CI = 1.03-1.42, p = 0.020) were associated with higher 28-day mortality. Kaplan-Meier curves indicated the lowest survival probability in Class 1 and the highest in Class 2. Gender-specific analysis revealed consistent trajectory patterns between males and females, but higher Hb values in males. The predictive power of the rapid decline pattern for early mortality was stronger in males. CONCLUSION: The Hb trajectories in elderly sepsis patients are significantly associated with early mortality. Persistently low levels and rapid declines in Hb are indicators of poor prognosis.
10 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Healthcare utilization following hip fractures based on social vulnerability status in the US: an analysis of 2016-2020 nationwide readmissions data
INTRODUCTION: Social vulnerability (SV) influences rehabilitation and postoperative care for patients with hip fracture. However, most previous work relies on area-level measures that overlook interindividual variation. The recent adoption of ICD-10 Z-codes allows clinical identification of patient-level SV and may offer a better understanding of its impact. This study aimed to evaluate healthcare utilization, including readmissions, discharge disposition, and length of stay (LOS) in surgically treated hip fracture patients with and without clinically acknowledged SV. METHODS: Adults surgically treated for hip fracture between 2016 and 2020 were included from the Nationwide Readmissions Database. SV was defined as having at least one documented relevant ICD-10 Z-code. Primary outcome measures included complications, LOS, discharge disposition, and 30- and 90-day readmissions, stratified by SV and evaluated using chi-square analyses. Multivariable logistic regression assessed long LOS (≥ 5 days) and discharge to home, adjusting for age, insurance/income status, and substance use. RESULTS: Patients with SV were younger (35.6% with SV vs. 50.1% without SV were 81+), had a lower median household income (38.8% with SV vs. 25.7% without SV were in the lowest quartile), and were more often insured by Medicaid (19.3% vs. 3.8%). Alcohol/drug use disorders were significantly more prevalent in patients with SV (18.5% vs. 4.5%). SV was associated with 47% higher odds for long LOS (1.47, 1.41-1.54) and 23% higher odds for discharge to home (1.23, 1.16-1.30) but comparable 90-day readmissions (21.2% vs. 19.8%). CONCLUSION: Among surgically treated hip fracture patients, SV was associated with higher odds of long LOS and discharge to home but no meaningful difference in readmissions. The small number of patients with clinically documented SV highlights the limited reporting by healthcare workers. This analysis of a nationwide all-payer database highlights the need to identify these higher risk patients and implement appropriate care pathways to reduce healthcare utilization.
10 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Quality of life after tranexamic acid in subarachnoid hemorrhage: post-hoc analysis of the ULTRA trial
PURPOSE: The ULTRA trial evaluated the impact of ultra-early and short-term tranexamic acid (TXA) treatment in patients with subarachnoid hemorrhage (SAH) and found no clinical benefit at six months. This post-hoc analysis examines whether TXA improves quality of life (QoL) at three and six months. METHODS: The ULTRA trial was a randomized, controlled, multicenter study conducted from July 2013 to July 2019. Patients received either TXA or standard care. This analysis included patients who completed at least one QoL questionnaire. The primary endpoint was QoL, assessed using the EQ-5D-3L questionnaire at three and six months. Linear mixed models adjusted for confounders were used to analyze the association between TXA and QoL. RESULTS: Of the 955 ULTRA patients, approximately 25% died, and 63% completed at least one QoL questionnaire. At three months, the TXA group had a mean EQ-5D index score of 0.75 versus 0.71 in the control group (p = 0.11) and a mean EQ-5D Visual Analogue Scale (VAS) score of 89 versus 86 (p = 0.10). At six months, the mean EQ-5D index score was 0.84 in the TXA group compared to 0.82 in the control group (p = 0.23), and the mean VAS was 92 in the TXA group compared to 89 in the control group (p = 0.13). CONCLUSION: Ultra-early and short-term TXA did not result in a significant improvement in QoL at three or six months in patients with SAH. Given the lack of benefit on both clinical outcome and QoL, routine use of TXA is not recommended. TRIAL REGISTRATION: Netherlands Trial Register: NTR3272. CLINICALTRIALS: gov: NCT02684812.
10 May 2026
Read appraisal →Diagnostic and interventional radiology (Ankara, Turkey)
Imaging in chronic thromboembolic pulmonary hypertension: review of the current literature
Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe, life-threatening complication of pulmonary embolism with pulmonary hypertension (PH). The combination of insufficient resolution of thrombi following pulmonary emboli and accompanying microvascular disease results in PH. Advances in imaging can offer better insight into CTEPH diagnosis and management, but lack of disease awareness among radiologists has been shown to be a cause of CTEPH misdiagnosis or delayed diagnosis. This review highlights features pertinent to CTEPH diagnosis. The primary focus is on different modalities with their distinctive signs and newly developed technologies employing artificial intelligence systems.
10 May 2026
Read appraisal →World journal of urology
Clinical and procedural factors influencing ureteral stent dwell time and removal methods after endourological procedures: a systematic review.
INTRODUCTION: Ureteral stents are routinely used following endourological procedures to ensure adequate drainage and prevent obstruction. However, stent-related morbidity remains common, and optimal stent dwell time and removal methods are not well defined. This systematic review aimed to evaluate clinical and procedural factors influencing ureteral stent dwell time and the methods used for stent removal after endourological interventions. METHODS: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO. MEDLINE and Embase were searched from inception to October 2025. Randomized controlled trials and comparative observational studies evaluating ureteral stent dwell time and/or removal methods in adults undergoing endourological procedures were included. Risk of bias was assessed using RoB 2 and ROBINS-I tools. RESULTS: Thirty-two studies encompassing 4,373 patients were included. Reported stent dwell times varied widely, most commonly ranging between 10 and 14 days in uncomplicated cases, with longer durations associated with increased rates of encrustation and removal difficulty. Removal techniques included rigid cystoscopy (48.7%), flexible cystoscopy (19.9%), extraction strings (23.5%), and device-assisted methods (7.9%). Less invasive approaches, particularly flexible cystoscopy and extraction-string removal, were consistently associated with reduced pain scores and improved patient comfort, although extraction strings carried a small risk of premature dislodgement. CONCLUSIONS: While practice patterns vary, the evidence suggests that a 10-14 day dwell time might be the optimal window to balance healing with the prevention of encrustation. Less invasive removal approaches, particularly flexible cystoscopy and extraction-string techniques, were generally associated with lower pain scores and high procedural success rates in selected patients. While these methods are safe and better tolerated, extraction strings carried a small, reproducible risk of premature dislodgement. High-quality prospective studies are needed to define determinant-based, individualized stent management strategies.
10 May 2026
Read appraisal →Journal of wound care
Exudate management with a next-generation multilayered foam dressing in traumatic and surgical wounds: a prospective, multicentre study
OBJECTIVE: The aim of this study was to evaluate the efficacy, performance and safety of ConvaFoam (Convatec Ltd., UK), a next-generation multilayered foam dressing, in indicated surgical and traumatic wounds. METHOD: This prospective, multicentre, open-label study (ClinicalTrials.gov: NCT05632276) evaluated ConvaFoam dressings in surgical and traumatic wounds. Eligible patients were managed with ConvaFoam dressings in accordance with their instructions for use (maximum seven-day wear time) for ≤4 weeks. The primary endpoint was the proportion of patients and total dressings with excellent exudate management (defined as absence of strikethrough or periwound maceration). Secondary endpoints included: exudate management in dressings worn for seven days; healing rate; wear time; pain on application; usability; and safety. RESULTS: The study enrolled 52 patients (mean age: 48.2 years) with surgical (54%) or traumatic (46%) wounds. Median dressing wear time was five days (interquartile range: 1-7 days). Among patients who completed the study, 81% (38/47) were observed to have achieved excellent exudate management throughout all wound care visits (90% confidence interval (CI): 71, 90%). Excellent exudate management was also observed in 93% (209/224) of total dressings applied (90% CI: 90, 97%), including in 96% (47/49) of dressings worn for seven days. Only one patient in the study experienced periwound skin maceration at any study timepoint. Wound healing was observed in 93% of surgical wounds and 25% of traumatic wounds during the study period. Low levels of pain and high user satisfaction as per numeric pain scale and utility questionnaire, respectively, were reported. CONCLUSION: ConvaFoam dressings demonstrated excellent exudate management in >90% of dressing changes, and may promote an optimal environment for healing in surgical and traumatic wounds.
10 May 2026
Read appraisal →Journal of wound care
Comparison between foam dressings impregnated with ionic silver and silver sulfadiazine cream 1% in treating deep burns.
OBJECTIVE: The aim of this trial was to evaluate the effectiveness of foam dressings impregnated with ionic silver (AgF) compared to the traditional treatment of 1% silver sulfadiazine cream (SSD) in deep burns (deep dermal and full-thickness burns) and assess the cost-effectiveness ratio between the two treatments. METHOD: This was a randomised clinical trial of patients who were divided into two equal-sized treatment groups: Group 1-SSD or Group 2 - AgF. Inclusion criteria were patients aged between 18-60 years with burns of thermal origin affecting 10-30% of the body surface area, and with a comparable percentage of deep burns. The following outcomes were analysed: pain; discomfort; wound infection; need for new grafting; ease of dressing application; length of stay; and cost of treatment. RESULTS: A total of 40 patients took part (20 in each group). Regarding patients' experience of pain, data analysis revealed more effective pain control in patients treated with AgF. Additionally, the AgF dressing was considered safer, more comfortable and presented an average lower cost of 30.6% compared to the SSD. CONCLUSION: The findings of this trial showed that, compared to the SSD, the AgF dressings were more effective in treating deep burns, to prepare them for skin graft surgery.
10 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Defining orthoplastic limb salvage centers: a systematic review
INTRODUCTION: Limb salvage centers have increased in number over time, but lack standardized defining criteria. This systematic review aimed to assess organizational features of limb salvage centers and determine whether orthoplastic centers, in comparison to vascular limb salvage centers, represent a distinct care model that may benefit from standardization. METHODS: We conducted a systematic review of publications related to limb salvage centers by searching MEDLINE, Embase, Web of Science, and Cochrane databases from their inception through 2024. We quantified binary data extraction as a reporting score of 26 organizational features across six structural care domains for limb salvage centers, based on a validated quality measurement framework. Organizational features differentiating distinct center types were identified to establish a quality framework for orthoplastic centers. Statistical comparisons between center types were performed using appropriate tests (p < 0.05). RESULTS: Of 118 included studies, orthoplastic (n = 43) and vascular (n = 48) centers represented 77% of all studies. Recent increases in orthoplastic publications show substantial variability in organizational features. Orthoplastic center literature more frequently reported plastic surgery consultation criteria (p < 0.001), surgical outcomes (p < 0.001), and centralized network integration (p ≤ 0.006), highlighting acute reconstructive approaches. Vascular center studies documented significantly more organizational team features (p < 0.001) and quality systems (p = 0.033), reflecting established care frameworks for chronic disease management. Six organizational features characterized orthoplastic centers with > 70% prevalence, providing a benchmark framework with standardization priorities. CONCLUSION: Orthoplastic limb salvage centers demonstrate distinct care paradigms that benefit from standardization. Our findings suggest structural benchmarks to support the need for standardized development of orthoplastic limb salvage centers.
10 May 2026
Read appraisal →Annals of emergency medicine
Ambient Artificial Intelligence Scribe Adoption and Documentation Time in the Emergency Department
STUDY OBJECTIVES: To describe real-world adoption of an ambient artificial intelligence (AI) scribe in the emergency department (ED) and compare documentation time and note characteristics between ambient and standard encounters using electronic health record audit logs. METHODS: We performed a retrospective observational study of adult ED encounters at a tertiary academic medical center. Attending physicians could optionally use an ambient AI scribe to generate notes from patient-clinician conversations. We included single-attending encounters in core ED zones and excluded visits with human scribes. Electronic health record audit logs provided documentation of time during and after the shift, total electronic health record time, and note length. We summarized adoption by physician, zone, and acuity and compared medians between ambient and standard encounters. RESULTS: Among 8,740 eligible encounters, 976 (11.2%) used ambient AI. Thirty-five of 92 attendings (38%) used the tool, and a small group of high-frequency users accounted for most ambient encounters. Ambient use clustered in telemedicine and vertical-care zones (chair-based ambulatory care) and in lower-acuity patients, as well as those not requiring interpreters. Median on-shift documentation time was 2:45 min for ambient encounters versus 3:50 min for standard encounters (difference -1:05; -28%). Median total electronic health record time was 8:39 min versus 10:21 min (-16%), and ambient notes were shorter overall. CONCLUSION: Early ED implementation of ambient AI scribes showed low but highly skewed adoption, with physicians favoring lower acuity, noninterpreted encounters. When used, ambient AI was associated with shorter on-shift documentation time, total electronic health record time, and note length.
10 May 2026
Read appraisal →Journal of medical systems
Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.
INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness and seizure-related conditions, yet standardized criteria guiding its use remain limited. METHODS: We retrospectively analyzed 1,018 patients (mean age 66 ± 20 years; 48.4% female) undergoing emEEG at the ED of the Careggi Teaching Hospital (Florence, Italy) in 2023. Clinical, anamnestic, and neuroimaging data available at admission were used to train supervised machine-learning (ML) models. We evaluated tree-based ensembles (Random Forest and XGBoost) to predict abnormal and epileptiform emEEG, as well as confirmation or refutation of initial diagnosis. Ground-truth labels were derived from a multidisciplinary expert team including neurologists, neurophysiopathologists and intensivists. Model performance was assessed with 5 × 5 nested cross-validation, receiver operating characteristic (ROC) analysis, balanced accuracy, decision-curve analysis, and Shapley Additive Explanations (SHAP) interpretability. RESULTS: Abnormal emEEG occurred in 691 cases (67.9%), epileptiform activity in 192 patients (18.9%). emEEG ruled out the initial diagnostic suspicion in 514 cases (50.5%) and confirmed it in 188 cases (18.5%). Best performance was obtained with Random Forest for abnormal emEEG (AUC 0.79, 95% CI: 0.76-0.82) and diagnosis rule-out (0.84, 0.81-0.86), and with XGBoost for epileptiform emEEG (0.82, 0.78-0.85) and diagnosis confirmation (0.82, 0.79-0.85). Performance varied by initial diagnostic suspicion, but subgroup-stratified analyses showed overall consistent patterns. Key predictive features included altered consciousness, prior brain lesions, antiseizure therapy, and seizure-related presentations. Interpretability analyses revealed seizure-centric features drove confirmation, while systemic or nonspecific features favored refutation. CONCLUSIONS: Interpretable ML models using only admission data can predict emEEG outcomes and anticipate their diagnostic contribution, supporting triage and decision-making in emergency neurology without replacing clinical judgment. Models and explanations were easily usable on a freely-accessible website ( www.emergencyeeg.com ), where tools return probabilistic outputs for all four prediction tasks together with per-patient explanation plots, enabling transparent and reproducible use. CLINICAL TRIAL NUMBER: Not applicable.
9 May 2026
Read appraisal →Accident; analysis and prevention
Which approach better samples extreme traffic conflicts? Conventional- vs. machine learning-based sampling methods.
Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those sampled extremes. Although the existing body of research has encapsulated sampling methods within a predominant conventional technique, there is no universally accepted practice on how to efficiently select threshold values, nor on how to evaluate the sampled extreme conflicts alignment with the conceptual crash severity level framework. This research aims to address these issues by employing machine learning-based sampling methods, which do not require predefined thresholds, and by comparing the sampled extremes with the conceptual severity levels, to assess their alignment. After a review of recent developments in machine learning techniques in transportation and other engineering fields, two promising machine learning sampling models, autoencoder neural network and isolation forest, were investigated using a database of vehicle-to-pedestrian conflicts at urban signalized intersections. Sampled extreme conflicts using the machine learning and conventional sampling techniques-as a baseline -were assessed and compared using two criteria: their visual alignment with the conceptual severity level framework, and their compatibility with the extreme value distribution. The results demonstrate that the extreme conflicts selected based on the machine learning methods better mirror the conceptual severity levels than the conventional sampling technique. Moreover, extremes classified by the isolation forest more closely preserve the characteristics of the empirical tail distributions, demonstrating a better contextual representation for modeling with the extreme value distribution compared to the autoencoder neural network and conventional sampling methods.
9 May 2026
Read appraisal →Journal of medical systems
Prognostic Modeling Based on Post-Endovascular Thrombectomy Systolic Blood Pressure Trajectories Using Explainable Artificial Intelligence: A Secondary Analysis of the OPTIMAL-BP Trial
Blood pressure (BP) management following successful reperfusion after endovascular thrombectomy (EVT) is critical in achieving favorable clinical outcomes. Individualized BP management using predictive modeling by machine learning may further improve prediction of functional outcomes. This study was a retrospective analysis of data from the Outcome in Patients Treated with Intra-Arterial Thrombectomy-Optimal Blood Pressure Control (OPTIMAL-BP) trial, a randomized controlled trial comparing between intensive and conventional BP management during the 24 h after successful recanalization by EVT from June 18, 2020, to November 28, 2022. The trial was conducted across 19 centers in South Korea. Machine learning models were developed to predict functional independence (90-day modified Rankin Scale 0 to 2). Model performance was compared between clinical variables only and systolic blood pressure (SBP) metrics in addition to clinical variables. In addition, the Shapley additive explanations (SHAP) analysis was performed to provide model explanation and understand the importance of SBP metrics. A total of 288 patients (61.1% men, median age 75 years [interquartile range, 65-81]) were included. Among the six algorithms, the deep neural network model incorporating SBP metrics performed best on validation, achieving an area under the curve of 0.86 (95% confidence interval, 0.76-0.92) which was significantly better than the model using only clinical variables (area under the curve 0.80 [95% confidence interval, 0.69-0.88], P = .037). Among SBP metrics, SHAP analysis identified time rate of SBP and minimum SBP as important features, with time rate showing greater influence in the intensive group and minimum SBP in the conventional group. Integrating SBP metrics with clinical variables significantly improved machine learning performance in predicting functional outcomes after successful EVT. Explainable artificial intelligence (AI) identified time rate and minimum SBP as key predictors of outcome. Trial Registration Information: ClinicalTrials.gov (NCT04205305; registered December 17, 2019).
9 May 2026
Read appraisal →Computer methods in biomechanics and biomedical engineering
Modified mutual information feature selection algorithm to predict COVID-19 using clinical data
The COVID-19 pandemic has profoundly impacted health, emphasizing the need for timely disease detection. Blood tests have become key diagnostic tools due to the virus's effects on blood composition. Accurate COVID-19 prediction through machine learning requires selecting relevant features, as irrelevant features can lower classification accuracy. This study proposes Modified Mutual Information (MMI) for feature selection, ranking features by relevance and using backtracking to find the optimal subset. Support Vector Machines (SVM) are then used for classification. Results show that MMI with SVM achieves 95% accuracy, outperforming other methods, and demonstrates strong generalizability on various benchmark datasets.
6 May 2026
Read appraisal →Science (New York, N.Y.)
Performance of a large language model on the reasoning tasks of a physician.
More than 65 years ago, complex clinical diagnostic reasoning cases were introduced as the gold standard for the evaluation of expert medical computing systems, a standard that has held ever since. In this study, we report the results of a physician evaluation of a large language model (LLM) on challenging clinical cases across five experiments with a baseline of hundreds of physicians. We then report a real-world study comparing human expert and artificial intelligence (AI) second opinions in randomly selected patients in the emergency room of a major tertiary academic medical center. In all experiments, the LLM outperformed physician baselines and displayed continued improvement from prior generations of AI clinical decision support. Our study suggests that LLMs have eclipsed most benchmarks of clinical reasoning, motivating the urgent need for prospective trials.
5 May 2026
Read appraisal →Joint diseases and related surgery
High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging
OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral lesions of the talus (OLTs) on magnetic resonance imaging (MRI) and to compare its efficacy between T1- and T2- weighted sequences. MATERIALS AND METHODS: A total of 219 ankle MRI scans were reviewed retrospectively, including 60 with confirmed OLTs and 159 without lesions. From each study, coronal and sagittal T1- and T2-weighted images were extracted and standardized to 224 × 224 pixels. Augmentation techniques were applied to strengthen model training. Data were divided into training, validation, and test sets in a 60:20:20 split. A ResNet50 model initialized with ImageNet weights was fine-tuned using crossentropy loss with class weighting. Diagnostic performance was summarized with accuracy, precision, recall, and F1-scores. RESULTS: The model performed better on T1 sequences, achieving an accuracy of 94.1% (95% confidence interval [CI] 88.3-97.1%) and an area under the curve [AUC] of 0.93 (95% CI 0.87-0.97), with patient cases classified at 0.92 precision and 0.82 recall. Healthy controls in the T1 group were recognized with 0.95 precision and 0.98 recall. In contrast, T2 sequences were less reliable, showing an accuracy of 87.2% (95% CI 80.5-91.9%) and an AUC of 0.91 (95% CI 0.85-0.95). Precision for patient cases in the T2 group was notably lower (0.65) despite a recall of 0.81. Misclassifications were more frequent in the T2 dataset, as evidenced by the confusion matrices. CONCLUSION: Even with a relatively modest dataset, the ResNet50 model delivered strong results for T1-weighted MRI. While T2 images proved more challenging, suggesting that deep learning can add value to routine assessment of OLTs.
5 May 2026
Read appraisal →Biosensors & bioelectronics
Peptide-responsive photonic hydrogels integrated with deep learning assistance for early MMP-9 detection
Matrix metalloproteinase-9 (MMP-9) is crucial for extracellular matrix remodeling, and its dysregulation is associated with inflammatory diseases and different forms of cancer. Conventional MMP-9 detection methods such as enzyme-linked immunosorbent assay (ELISA) are limited by complexity, expensive equipment, and lengthy antibody incubation times. These limitations prevent their use in point-of-care testing. An MMP-9 responsive photonic crystal (PC/PEG-M9SP) hydrogel has been developed to address these challenges. The hydrogel is synthesized from 4-arm polyethylene glycol-acrylate and an MMP-9 sensitive peptide via Michael-type addition reaction. Upon MMP-9-specific enzymatic cleavage, the hydrogel undergoes a structural reconfiguration, resulting in a distinct color shift. Integrated with a deep learning-based smartphone app, this platform enables both visual and quantitative detection within 10 min, achieving high sensitivity (10.60 nm mL/ng) and a detection limit of 0.62 ng/mL. Validation in complex biological fluids demonstrated strong concordance with ELISA, confirming the analytical reliability of the hydrogel. This system provides a rapid, portable, and cost-effective solution for accurate MMP-9 detection, with strong potential for clinical and point-of-care applications.
5 May 2026
Read appraisal →Journal of vascular and interventional radiology : JVIR
A Deep Learning Model for Artery of Adamkiewicz Detection in Bronchial Artery Embolization: A Multicenter Retrospective Study
PURPOSE: To evaluate the effectiveness of a deep learning model for recognizing the artery of Adamkiewicz and anterior spinal artery (ASA) to prevent spinal artery non-target embolization during bronchial artery embolization (BAE). MATERIALS AND METHODS: This multicenter, retrospective study included 2036 patients from January 2019 to December 2023 with hemoptysis who underwent de novo BAE. A deep learning-based framework was proposed for spinal artery identification, comprising region of interest (ROI) perception and target spinal artery identification. The ROI perception extracted vessel-related regions to improve the conspicuity of the spinal artery. The target spinal artery identification utilized a progressive refinement learning network, localizing the artery from the global view, and progressively refined the identification results through cross-scale information interaction. RESULTS: Seventy-eight patients (3.8%) had an identifiable artery of Adamkiewicz and ASA on right intercostal-bronchial artery angiography. The sensitivity of the proposed method was 92.1% and the specificity was 84.6%, with no statistically significant difference from a fellow radiologist interpretation. CONCLUSIONS: The deep learning system for spinal artery detection during BAE exhibited high sensitivity and performance, comparable to radiology fellows.
4 May 2026
Read appraisal →NMR in biomedicine
Deep Learning Reconstruction Enables Diagnostic-Quality 0.4T Knee and Spine MRI in One-Third of the Time
There has been a growing interest in low-field MRI due to its lower costs, enabling an increase in accessibility of MRI worldwide. Long scan times are currently needed to obtain high-quality images. The aim of our study was to accelerate low-field 0.4T musculoskeletal MRI using deep learning and evaluate maximum acceleration via assessment by two expert radiologists. A cascades of independently recurrent inference machines (CIRIM) model was trained using knee and spine 2D multislice scans, retrospectively undersampled in k-space. The heterogeneous dataset included varied contrasts, orientations, and matrix sizes. To determine the maximum possible acceleration factor, the undersampling pattern and loss function of the CIRIM were optimized using the knee dataset. Models were trained for acceleration factors 1.5-4. The different acceleration factors were evaluated quantitatively and clinically, with radiologist scores compared using Kendall's Tau and Cohen's Kappa. To assess the model's generalizability, two prospectively undersampled scans were evaluated, and the knee-trained models were tested on spine data. CIRIM accurately reconstructed all data, with slight metric degradation at higher accelerations. Combining L1, structural similarity index measure (SSIM), and perceptual loss improved sharpness, while undersampling pattern differences were minimal. Clinical scores from both radiologists declined with acceleration, reflected in Kendall's Tau values of 0.64, 0.64, 0.54, and 0.55 for image quality, sharpness, anatomical conspicuity, and artifacts. Inter-observer agreement was low (Cohen's kappa: 0.227-0.299). The model generalized well, reconstructing spine data with a knee-trained model at comparable quality and successfully handling prospectively undersampled scans. CIRIM consistently delivered high-quality reconstructions, demonstrating excellent robustness to variations in contrast, orientation, matrix size, and anatomy. Its ability to generalize across anatomies and handle prospectively undersampled scans highlights its practical utility. The differences in reading for higher accelerations between radiologists suggest that the optimal acceleration factor is context-dependent and should be tailored to the specific diagnostic task.
4 May 2026
Read appraisal →Resuscitation
Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest
INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been associated with resuscitation outcomes. We aimed to develop a convolutional neural network (CNN) model to predict return of spontaneous circulation (ROSC) and identify novel ECG phenotypes in patients with out-of-hospital cardiac arrest (OHCA). METHODS: This retrospective cohort study used Korean OHCA Registry and ECG data from Seoul emergency medical services between July 2021 and December 2023. Adult patients with nontraumatic OHCA who had prehospital ECG signals were included. Five-second ECG segments obtained during resuscitation were analyzed. A one-dimensional CNN was developed to simultaneously predict the probability of ROSC within 2 min and to classify the concurrent ECG rhythm as shockable or non-shockable. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). ECG phenotypes were identified by applying K-means clustering to feature representations extracted from the trained CNN. RESULTS: 3452 patients (median age, 73 years [interquartile range, 61-81]; 34.4% female) were included. The AUCs for predicting ROSC within 2 min and shockable rhythm in the test set were 0.921 (95% confidence interval [CI], 0.897-0.945) and 0.983 (95% CI, 0.979-0.988), respectively. Five ECG phenotype categories with varying morphologies and ROSC probabilities were identified. Phenotype 1 consisted of shockable rhythms with high ROSC probability (30.4%), whereas Phenotype 2 showed shockable rhythms with low ROSC probability (4.8%). Phenotypes 3 and 4 included pulseless electrical activities with relatively high (5.2%) and lower (0.5%) ROSC probabilities, respectively. Phenotype 5 primarily consisted of asystole or near-asystole rhythms. Transitions between ECG phenotypes were associated with CPR quality. CONCLUSIONS: The CNN-based model accurately predicted ROSC and shockable rhythm from ECG signals and identified five novel ECG phenotypes in OHCA. These findings can enable accurate ROSC prediction and individualized prehospital resuscitation for patients with OHCA.
4 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection
OBJECTIVE: Accurate detection of adventitious respiratory sounds, such as wheezes and crackles, is essential for diagnosing and managing respiratory conditions. This study introduces a multi-stage, confidence-driven framework for automated pediatric auscultation analysis, performing a three-way classification of normal, wheeze, and crackle sounds to improve diagnostic accuracy. METHODS: We develop a comprehensive pipeline integrating anomaly-specific segment selection, segment-level classification, and confidence-based fusion. Our contrastive variational recurrent neural network (CVRNN) enhances feature extraction, while a confidence-weighted aggregation strategy refines final predictions. The system is validated using a diverse pediatric dataset from 742 subjects (aged 1-59 months) from seven countries. RESULTS: The multi-level framework is evaluated across three stages. The anomaly-specific segment selection achieves 98.47 $\%$ recall, identifying adventitious regions. Next, segment-level classifiers improve sensitivity, achieving balanced accuracies of 72.15 $\%$ (wheeze) and 68.1 $\%$ (crackle). This performance surpasses state of the art systems on the same dataset and demonstrates enhanced balanced performance in detecting both crackle and wheeze sounds, which present different challenges to automated systems given their markedly different acoustic profiles. Finally, the confidence-driven fusion outperforms traditional aggregation methods, yielding a final three-way classification of 62.12 $\%$. CONCLUSION: Our confidence-based multi-stage approach enhances automated respiratory sound classification by prioritizing high-certainty segment predictions, aligning with expert physician annotations. SIGNIFICANCE: This framework advances computer-aided respiratory diagnostics, improving early detection and monitoring of pediatric respiratory conditions. By integrating expert-inspired segmentation with machine learning-driven confidence estimation, it has potential to enhance clinical workflows and screening for pulmonary diseases, particularly in resource-limited settings.
4 May 2026
Read appraisal →Journal of the American College of Surgeons
In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight-of-Evidence Modeling
BACKGROUND: Mortality prognostication in adult patients requiring extracorporeal membrane oxygenation (ECMO) is not accurate or established. We hypothesized that composite lactate-based metrics and machine-learning models would improve in-hospital mortality prognostication compared with lactate alone in this population. STUDY DESIGN: We conducted a retrospective study of adult patients supported with ECMO at a Level I trauma center from 2022 to 2024 (N = 104). Composite metrics were generated by multiplying lactate by acid-base markers and comparing performance to lactate after Benjamini-Hochberg correction to control the false discovery rate. For machine-learning models, 3 multilayer perceptron architectures were developed using laboratory markers from a patient's hospital admission. Feature attribution through local interpretable model-agnostic explanations guided the development of a weight-of-evidence (WoE) model using only initial laboratory values. RESULTS: The composite metric of average arterial lactate × average arterial bicarbonate was the most significantly elevated marker in nonsurvivors and more significant than lactate alone (false discovery rate-adjusted p = 4.49 × 10 -4 vs 2.14 × 10 -3 ). The 100 × 50 multilayer perceptron architecture achieved 85% accuracy (95% CI 80 to 89%), 87% precision (95% CI 82% to 92%), F1 score of 0.81 (95% CI 0.74 to 0.86), and area under the curve of 0.879 (95% CI 0.84 to 0.92). Local interpretable model-agnostic explanations identified kidney replacement therapy, surrogate respiratory quotient, lactate gradient, and arterial bicarbonate as metrics incorporated in the WoE model achieving 80% accuracy (95% CI 76% to 84%), 70% precision (95% CI 63% to 77%), F1 score of 0.72 (95% CI 0.66 to 0.78), and area under the curve of 0.886 (95% CI 0.853 to 0.920). CONCLUSIONS: Composite lactate metrics and the WoE model improved in-hospital mortality prediction in patients requiring ECMO. Prospective studies and external validation are warranted to confirm these findings.
4 May 2026
Read appraisal →Sports health
Evaluation of Exertional Sweat Loss Estimates in Wearable Technology
BACKGROUND: Wearable technology shows promise for monitoring exertional sweat loss, yet its accuracy requires evaluation. This study evaluated the accuracy of 2 commercially available smartwatches for estimating sweat loss compared with gold standard methods. HYPOTHESIS: Sweat loss values measured via wearable technology will be determined accurate compared with gold standard methods. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 2. METHODS: A total of 111 participants (age, 40 ± 14 years; height, 171.0 ± 8.9 cm; body mass, 70.8 ± 14.7 kg; VO2max, 46.8 ± 8.7 ml/kg/min) completed up to 4 running trials under varying conditions (indoor/outdoor, distances from 2.5 km to 20 km, and interval runs). Sweat loss estimates from each smartwatch were compared with criterion measurements based on change in nude body mass pre- and postexercise. Validity statistics included tests for error (mean absolute error [MAE], mean absolute percentage error [MAPE]), linearity (Lin's concordance correlation coefficient [CCC], Pearson's [r], and Deming regression), equivalence (CI for difference in means), and Bland-Altman plots for bias assessment. Stratified analyses by sex, weight, and fitness category were also completed, along with a chi-square test of independence. RESULTS: Both devices demonstrated acceptable overall correlation (CCC range, 0.71 to 0.90) but substantial errors in estimating exertional sweat loss (MAPE range, 25.38% to 33.21%). Stratified analyses yielded similar results, with no additional analyses meeting the combined validity thresholds. CONCLUSION: Wearable devices offer a promising and convenient tool for general sweat loss tracking but lack the precision to replace laboratory methods for hydration management. Traditional methods remain essential for the most accurate measurements. CLINICAL RELEVANCE: Wearable devices provide an accessible option for sweat loss monitoring. This study highlights their potential for general tracking while emphasizing the need for laboratory methods when accuracy is critical, ensuring better guidance for hydration strategies in sports performance and health applications.
4 May 2026
Read appraisal →Scientific reports
Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds
A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misclassifies cases because of overlapping symptoms with other respiratory illnesses. The Asthma Detection Dataset v2, which is openly accessible and comprises actual audio recordings of respiratory noises, is used in this work to demonstrate a hybrid deep learning architecture for automated asthma detection. After applying thorough preprocessing, which includes denoising, segmentation, and augmentation, the suggested pipeline employs a dual-path feature extraction technique that combines pretrained YAMNet embeddings with handmade acoustic descriptors (MFCCs, chromagram, ZCR, and spectral features). Atrous Spatial Pyramid Pooling (ASPP) and Squeeze-and-Excitation (SE) modules are used to further improve these embeddings, and a Multi-Layer Perceptron is used for classification. The proposed model achieved high performance, with an accuracy of 98.6% ± 0.14, an F1-score of 97.5% ± 0.14, and a macro-AUC of 99.1% ± 0.14 using stratified 5-fold cross-validation. SHAP-based interpretability and visualization (waveforms, spectrograms, t-SNE) verified that the model records clinically significant auditory biomarkers such as crackles and wheezes. These results indicate the potential of the proposed model as an explainable and scalable computer-aided asthma diagnostic tool.
3 May 2026
Read appraisal →Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Bacterial quantitation in clinical infection: a systematic review
Pivotal clinical studies of new anti-infective drugs enrol patients with strictly defined clinical syndromes, with clinical and laboratory endpoints used to define treatment success. Assessing bacterial killing in the host by serial quantitation has potential as a more efficient pharmacometric approach to assess antibiotic efficacy. We conducted a systematic review of the use of bacterial quantitation in clinical infection. The main syndromes studied in the 222 included articles were bacteraemia, chronic respiratory disease, diarrhoea, pneumonia and sexually transmitted infections. Most reports (135 articles, 61%) quantified bacterial load in different specimen types using quantitative polymerase chain reaction (qPCR). For bacteraemias, bacterial DNA load measured in whole blood by qPCR at clinical presentation was typically 2-3 log10 copies ml-1, usually substantially higher than the estimates from quantitative culture. Higher bacterial loads were associated with increased mortality in 28 of 35 studies. Faster bacterial clearance was correlated with appropriate antibiotic therapy and improved outcome in the majority of studies. Most studies sampled too infrequently for accurate characterization of bacterial clearance rates. The rate of bacterial clearance from blood or other compartments is an informative pharmacodynamic endpoint in the assessment of antibacterial therapeutic effects, but standardized approaches to assessment are needed based on optimal study design. This article is part of the Theo Murphy meeting issue 'Evaluating anti-infective drugs'.
3 May 2026
Read appraisal →PloS one
Optimizing LSTM networks and feature selection algorithms using GEE data
Feature selection uncertainty and suboptimal model configuration in flood susceptibility mapping (FSM) are critical challenges for disaster risk reduction. This study introduces a novel integrated framework that couples feature selection strategy with metaheuristic-optimized deep learning for high-precision FSM in the flood-prone Khuzestan Province, Iran. An initial set of 19 factors was sourced from Google Earth Engine (GEE). The most influential variables were identified using an ensemble of nine feature selection methods, including Boruta, Boruta-SHAP, Elastic-Net, Mutual Information, Permutation Importance, Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Stability Selection, and Deep Feature Importance. For model development, 1,000 sample points were used, consisting of 500 randomly selected non-flood points (value 0) and 500 flood points (value 1), with the trained model subsequently generalized to the entire study area. In this process, a frequency-based consensus rule was applied, whereby variables were retained only if selected by a majority of methods. This process established the Normalized Difference Vegetation Index (NDVI) and Daily Minimum Temperature (TMMN) as the most critical predictors. A Long Short-Term Memory (LSTM) was developed using this optimal feature set and subsequently enhanced through hyperparameter optimization with five advanced metaheuristic algorithms, including WOA, GWO, OOA, CSA, and HOA. The model validation demonstrated that optimization significantly boosted performance, with the LSTM-WOA model emerging as superior, achieving the highest F1-Score (0.88) and Cohen's Kappa (0.75). The final FSM identified the northwestern and central regions as the highest susceptibility. The study innovation lies in its formalized consensus feature selection and comparative metaheuristic optimization, providing a reliable tool for FSM in arid and semi-arid regions.
3 May 2026
Read appraisal →Journal of the American College of Cardiology
Intravascular Imaging- vs Angiography-Guided Complex PCI: 5-Year Outcomes From a Randomized Trial
BACKGROUND: Data regarding long-term outcomes of intravascular imaging-guided percutaneous coronary intervention (PCI) for complex coronary artery lesions, compared with angiography-guided PCI, remain limited. OBJECTIVES: The authors sought to present long-term clinical outcomes of intravascular imaging-guided PCI compared with angiography-guided PCI in patients with complex coronary artery lesions. METHODS: Eligible patients with complex coronary artery lesions were randomly assigned 2:1 to undergo intravascular imaging-guided PCI or angiography-guided PCI in this prospective multicenter open-label superiority trial performed in South Korea. The primary endpoint was a composite of cardiac death, target vessel-related myocardial infarction, or clinically driven target vessel revascularization. RESULTS: A total of 1,639 patients underwent randomization with 1,092 assigned to imaging-guided PCI and 547 assigned to angiography-guided PCI. At a median follow-up of 5.3 years (Q1-Q3: 4.4-6.2 years), the primary endpoint occurred in 109 of 1,092 patients (10.5%) in the intravascular imaging-guided PCI group and 78 of 547 patients (14.9%) in the angiography-guided PCI group (HR: 0.68; 95% CI: 0.51-0.91; P = 0.009). Cardiac death or target vessel-related myocardial infarction occurred in 78 patients (7.6%) in the intravascular imaging-guided PCI group and in 56 patients (10.7%) in the angiography-guided PCI group, clinically driven target vessel revascularization in 45 (4.4%) and 32 (6.2%), and definite stent thrombosis in 1 (0.1%) and 4 (0.7%), respectively. There were no apparent differences in procedure-related safety events between the groups. CONCLUSIONS: In patients with complex coronary artery lesions undergoing PCI, intravascular imaging guidance reduced the risk of a composite of cardiac death, target vessel-related myocardial infarction, or clinically driven target vessel revascularization at median follow-up of 5.3 years, compared to angiography guidance. (Intravascular Imaging- Versus Angiography-Guided Percutaneous Coronary Intervention for Complex Coronary Artery Disease [RENOVATE-COMPLEX-PCI]; NCT03381872).
3 May 2026
Read appraisal →PloS one
Clinical manifestations of bed bug bites: A systematic review of case reports
IMPORTANCE: Clinical characterization of bed bug (Cimex spp.) bite manifestations remains limited. OBJECTIVE: This systematic review synthesizes case reports to describe dermatological, systemic, and psychological outcomes of bed bug bites. EVIDENCE REVIEW: Following PRISMA guidelines, multiple databases, including Pubmed and Scopus, were searched using terms such as "bed bug," "Cimex lectularius," "Cimex hemipterus," "case report," and "case series." Extracted data included demographics, species, lesion type, anatomical distribution, and clinical manifestations. Quantitative and qualitative analyses were conducted. RESULTS: Eighty-four patients were included: 76 with C. lectularius (90.5%) and, 8 with C. hemipterus (9.5%). Sex distribution was balanced (47.6% females, 52.4% males), and mean ages were similar (females 37.5 ± 22.1, males 42.3 ± 21.27 years; p = 0.332). Extremities were commonly affected, arms were reported in 71.4% of cases, legs in 59.5% and trunk in 34.5%. Psychological symptoms were registered in a handful of case reports (Anxiety 9.5%, Stress 9.5%, Hopelessness 9.5%). Systemic symptoms, generally associated with anemia, were reported in cases related to severe infestations in neglected patients. Some differences in systemic and local signs and symptoms were detected depending on age group. CONCLUSIONS: Bed bug bites predominantly affect exposed extremities, although with variable dermatologic patterns that can be confused with other arthropod bites. Psychological manifestations were reported in a small subset of cases, while systemic complications were rare.
3 May 2026
Read appraisal →Circulation
Prognostic Factors for Long-Term Risk of Stroke After Transient Ischemic Attack or Minor Stroke: A Systematic Review and Meta-Analysis
BACKGROUND: Patients with a transient ischemic attack (TIA) or minor stroke have an increased risk of subsequent stroke that persists for at least 10 years. We aimed to identify prognostic factors associated with long-term risk of stroke in this patient group and estimate their population attributable fraction (PAF). METHODS: A systematic review was performed of MEDLINE, Embase, and Web of Science for cohort studies including patients with TIA or minor stroke that evaluated factors for subsequent stroke over a follow-up period of ≥1 year. We pooled hazard ratios adjusted for relevant confounders using random-effect meta-analysis and determined the PAF of factors based on their pooled prevalence and adjusted hazard ratio. We assessed certainty of evidence using the grading of recommendations, assessment, development, and evaluation approach. The study is registered in PROSPERO (CRD42023476551). RESULTS: From 14 732 identified citations, we included 28 cohort studies comprising 86 810 patients with TIA or minor stroke (median age, 69 years [interquartile range, 65-71]; 52%-60% male patients). Factors that had high certainty evidence of association with increased long-term risk of stroke included older age (adjusted hazard ratio, 1.04 per year increase [95% CI, 1.02-1.05]); male sex (1.25, 1.15-1.36; PAF 13.0% [95% CI, 7.8-18.7]); atrial fibrillation (1.34, 1.18-1.52; 3.8% [95% CI, 0.3-9.9]); diabetes (1.52, 1.32-1.75; 7.7%, 3.1-14.1); hypertension (1.60, 1.31-1.94; 19.3%, 8.4-31.6); ischemic heart disease (1.67, 1.28-2.18; 10.7%, 2.8-22.9); history of stroke or TIA before the index event (1.70, 1.43-2.02; 12.0%, 5.2-21.4); smoking (1.29, 1.05-1.60; 11.2%, 1.0-30.7); ABCD2 (age, blood pressure, clinical features, duration of TIA, and presence of diabetes) score of ≥4 (1.59, 1.31-1.94; 18.0%, 2.9-39.9); presence of acute infarct on neuroimaging (1.97, 1.41-2.74; 19.0%, 5.2-38.9), including diffusion-weighted imaging-positive lesions (1.86, 1.02-3.37; 14.0%, 7.0-25.0); minor stroke as index event (1.75, 1.35-2.27 versus TIA; 28.0%, 10.2-47.6); presentation with aphasia or dysarthria (1.45, 1.24-1.69; 19.2%, 0.2-53.1); presentation with paresis (1.45, 1.15-1.84; 22.0%, 3.8-43.4); and etiologic stroke subtypes, including cardioembolism (2.16, 1.53-3.05; 14.6%, 3.1-33.5), large-artery atherosclerosis (2.19, 1.68-2.86; 13.2%, 5.1-25.5), and small-vessel disease (1.69, 1.14-2.49; 16.8%, 5.0-34.3). CONCLUSIONS: These findings can help identify patients with a particularly enduring risk of stroke who are most likely to benefit from ongoing monitoring and treatment and facilitate the development and implementation of targeted stroke prevention strategies.
3 May 2026
Read appraisal →JMIR public health and surveillance
Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward
Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.
2 May 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Structural equation modeling of findings in pediatric tele-neuroradiology: a 2-year nationwide study of turnaround time predictors and clinical decision support
BACKGROUND: Pediatric neuroradiology faces significant workforce shortages, with teleradiology emerging as a vital solution. However, studies investigating findings and their operational impact from teleradiology centers remain limited. OBJECTIVES: To develop and validate structural equation models for identifying predictors of turnaround time and formulate recommendations for workflow optimization in pediatric tele-neuroradiology services. DESIGN: A retrospective cohort study following STROBE guidelines. SETTINGS: 107 hospitals across 17 states in the United States (US) via a teleradiology platform providing interpretation services through US board-certified radiologists. PATIENTS AND METHODS: We analyzed 9985 pediatric neuroradiology scans from 7958 patients (January 2023-December 2024). We utilized confirmatory factor analysis to validate findings structures, followed by structural equation modeling to predict turnaround times. Binary logistic regression models were developed with area-under-the-curve (AUC) estimation for performance assessment. Bootstrap validation with 5000 samples ensured model stability. MAIN OUTCOME MEASURES: Primary outcome was turnaround time. Secondary outcomes included requirements for multiple imaging studies, follow-up recommendations, and consultations. SAMPLE SIZE: A total of 9985 studies providing over 99% statistical power for detecting significant relationships. RESULTS: Factor analysis demonstrated a two-factor structure (trauma: α = 0.742, structural: α = 0.685). The structural model explained 7.8% of turnaround time variance, with computed tomography (CT) modality (β = -0.164), trauma score (β = 0.125), and structural score (β = 0.142) as significant predictors. Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% achieved turnaround time within the 60-min benchmark for time-sensitive consultations. Prediction models demonstrated excellent discrimination: traumatic findings (AUC = 0.91), structural findings (AUC = 0.92), critical findings (AUC = 0.95), and a dedicated neurosurgical emergency model (AUC = 0.94, NPV = 0.996). A severity classification system showed strong validation against imaging needs (AUC = 0.76) and consultations (AUC = 0.89). CONCLUSIONS: Our study establishes a validated SEM framework for pediatric tele-neuroradiology with excellent predictive performance (AUC = 0.91-0.95). Among immediate neurosurgical emergencies (n = 180, 1.8%), 89.4% met the 60-min benchmark, and a dedicated emergency prediction model achieved AUC = 0.94. However, translation to improved neurosurgical care delivery and patient outcomes remains unvalidated, representing the next investigational priorities. LIMITATIONS: Retrospective design limits causal inference; a single platform may limit generalizability; the CT majority (96.8%) limits magnetic resonance imaging conclusions. Critically, post-diagnostic clinical outcomes, including neurosurgical consultations, interventions performed, and patient outcomes, were not tracked, precluding conclusions about whether documented operational efficiency translated to improved neurosurgical care delivery.
2 May 2026
Read appraisal →Lancet (London, England)
Clinical and bacteriological effectiveness of three different short-course antibiotic regimens and single-dose fosfomycin for uncomplicated lower urinary tract infections in women (SCOUT): a pragmatic, multicentre, open-label, randomised clinical trial
BACKGROUND: Most guidelines recommend nitrofurantoin, fosfomycin, and sometimes pivmecillinam for uncomplicated urinary tract infections (UTIs), but direct comparisons between these antibiotics are needed. This study evaluated the effectiveness and safety of a single dose of fosfomycin compared with two doses of fosfomycin and short-course regimens of nitrofurantoin and pivmecillinam in women with UTI symptoms. METHODS: This phase 4, pragmatic, multicentre, parallel-group, open-label, randomised clinical trial was conducted in Spanish primary care centres from 2022 to 2024. Women aged 18 years or older with at least one UTI-specific symptom (dysuria, urinary urgency, urinary frequency, or suprapubic tenderness) and a positive urine dipstick test for either nitrites or leukocyte esterase were randomly assigned (1:1:1:1) to one of four treatments: a single 3 g dose of fosfomycin, two 3 g doses of fosfomycin, nitrofurantoin (100 mg three times per day for 5 days), or pivmecillinam (400 mg three times per day for 3 days). Doctors and patients were not masked to group assignment. The primary outcome was the proportion of patients with clinical resolution (defined as the disappearance of all infection symptoms) at day 7. This trial is registered with ClinicalTrials.gov (NCT04959331) and EudraCT (2021-001332-26) and is completed. FINDINGS: Of the 804 patients assessed for eligibility between April 4, 2022 and Nov 14, 2024, 768 patients were randomly allocated (191 to the single-dose fosfomycin group, 194 to the two-dose fosfomycin group, 190 to the nitrofurantoin group, and 193 to the pivmecillinam group). Patients had a median age of 48 years (IQR 34-63). Race and ethnicity data were not collected. Among the 720 women with available data included in the primary analysis, single-dose fosfomycin had the lowest proportion of clinical resolution (109 [59%] of 185 patients), while nitrofurantoin had the highest (128 [74%] of 172 patients; difference 15·5 percentage points [95% CI 5·9 to 25·1] vs single-dose fosfomycin; p=0·0168), followed by pivmecillinam (127 [70%] of 182; difference 10·9 percentage points [1·1 to 20·6]; p=0·2352) and the two-dose fosfomycin group (122 [67%] of 181; difference 8·5 percentage points [-1·4 to 18·3]; p=0·6935). Adverse events occurred in 38 (19·9% [95% CI 14·9 to 26·1) of 191 patients who received single-dose fosfomycin, 51 (26·3% [20·6 to 32·9]) of 194 who received two-dose fosfomycin, 51 (26·8% [21·0 to 33·6]) of 190 who received nitrofurantoin, and 41 (21·2% [16·1 to 27·5]) of 193 who received pivmecillinam. Most adverse events were mild and self-limiting, primarily gastrointestinal. Four serious adverse events occurred, of which one was related to the study treatment (one case of pyelonephritis in the pivmecillinam group). INTERPRETATION: Nitrofurantoin was the most effective treatment and single-dose fosfomycin the least effective treatment for UTIs. Adverse events were mild. The role of fosfomycin as a first-line antibiotic for uncomplicated UTI should be re-evaluated. FUNDING: Carlos III Institute of Health, Spanish Ministry of Science and Innovation, and European Regional Development Fund.
2 May 2026
Read appraisal →Heart (British Cardiac Society)
Revascularisation strategies for non-acute myocardial ischaemic syndromes
BACKGROUND: Contemporary guidelines by the European Society for Cardiology and American College of Cardiology/American Heart Association for the treatment of non-acute myocardial ischaemic syndromes dispute the value of revascularisation and differ in their recommendation to perform revascularisation. A Bayesian network meta-analysis was performed, evaluating the strength of evidence for the comparative incremental effectiveness of coronary artery bypass grafting (CABG) versus percutaneous coronary intervention (PCI) over medical therapy on long-term outcomes. METHODS: A hierarchical Bayesian network meta-analysis was designed (PROSPERO CRD42024541215, date 20 May 2024), including randomised controlled trials (RCTs) published between 2005 and 10 June 2025, which consisted of three initial treatment modalities: optimal medical therapy (OMT), PCI+OMT and CABG+OMT. The primary outcome was all-cause mortality at maximum follow-up; secondary outcomes were trates of the rates of myocardial infarction, stroke and re-revascularisation at maximum follow-up, expressed in HRs and 95% credible intervals (CrIs), accompanied by surface under the cumulative ranking curve (SUCRA) scores. RESULTS: 10 RCTs, comprising 10 742 patients, were included. For all-cause mortality, the estimated median HR of CABG+OMT versus OMT was 0.84 (95% CrI 0.68-1.07); the HR of PCI+OMT versus OMT was 0.93 (0.79-1.16); and the HR of CABG+OMT versus PCI+OMT was 0.91 (0.71-1.13). The SUCRAs of a CABG+OMT strategy ranking as the optimal revascularisation treatment regarding mortality, myocardial infarction, stroke and re-revascularisation were 88.1%, 99.7%, 17.5% and 99.5%, respectively. Results were consistent across sensitivity analyses, including in the node-splitting models. CONCLUSIONS: This Bayesian network meta-analysis found that an initial CABG (+OMT) revascularisation strategy was associated with higher probabilities of optimal outcomes, with the exception of stroke, compared with an initial PCI (+OMT) revascularisation strategy, although CrIs overlapped, suggesting that some uncertainty remains. PROSPERO REGISTRATION NUMBER: CRD42024541215.
27 Apr 2026
Read appraisal →Heart (British Cardiac Society)
Variation in mechanical circulatory support use for acute myocardial infarction cardiogenic shock
BACKGROUND: Cardiogenic shock (CS) is a leading cause of mortality following acute myocardial infarction (AMI). Some patients may require intra-aortic balloon pump (IABP) or percutaneous ventricular assist device (PVAD) placement; however, there is a paucity of standardised algorithms to guide the deployment of each device. The present study evaluated interhospital variation in the use of IABP and PVAD for AMI CS and identified institutional factors associated with hospital-level device preference. METHODS: All non-elective adult hospitalisations entailing AMI and CS were identified within the 2019 Nationwide Readmissions Database. Patients were grouped into IABP, PVAD and non-mechanical circulatory support cohorts. The primary aim was to quantify the degree of interhospital variation in the use of IABP and PVAD. Escalation to extracorporeal membrane oxygenation (ECMO), left ventricular assist device (LVAD) implantation, length of stay and hospitalisation costs were secondarily assessed. Hospital factors, such as percutaneous coronary intervention (PCI) volume and safety net status, were also analysed. RESULTS: Among 53 903 patients, 23.4% received IABP, and 12.5% received PVAD. After adjustment for patient factors, approximately 13% (11-14%) of variation in IABP use and 18% (15-20%) of PVAD use were attributable to centre-level differences. High-PVAD hospitals had higher annual PCI volume (257 (185-369) vs 204 (148-276) cases/year, p=0.032) and were more commonly safety net institutions (27.4% vs 11.3%, p=0.023), compared to high-IABP hospitals. Patients treated at high-IABP and high-PVAD hospitals faced similar length of stay (β -0.16, 95% CI -1.82 to 1.49) and costs (β -$3500, 95% CI -16 600 to 9600). Those at high-PVAD hospitals had lower adjusted risk of escalation to ECMO (0.52, 95% CI 0.29 to 0.95) and LVAD implantation (0.28, 95% CI 0.08 to 0.94). CONCLUSIONS: The present study identified wide interhospital variation in the use of IABP and PVAD for AMI CS. Although the likelihood of escalation to ECMO or LVAD differed between hospital types, resource utilisation was similar.
27 Apr 2026
Read appraisal →PloS one
The stress reduction potential of Bhagavad Gita and Yoga for healthcare workers during the COVID-19 pandemic: A randomized controlled trial
BACKGROUND: Healthcare workers (HCWs) experienced elevated psychological distress during the COVID-19 pandemic. Culturally embedded, non-stigmatizing approaches may improve uptake of mental health support in this population. We aimed to evaluate the impact of Yoga, Bhagavad Gita learning, and their combination (Yoga+Gita) on psychological distress among HCWs. METHODS: We conducted a prospective, single-center, parallel-group randomized controlled trial at a secondary hospital in India. Eligible HCWs (>18 years, without pre-existing diagnosed mental health conditions) were randomized in a 1:1:1:1 ratio to: Yoga (daily 60-min sessions for one week), Gita (daily 60-min teaching of Bhagavad Gita Chapter 2 for one week), Yoga+Gita (30 min Yoga + 30 min Gita daily for one week), or Control (usual care). Psychological distress was assessed using the Generalized Anxiety Disorder-7 (GAD7) scale at baseline (B-GAD7), immediately post-intervention (IPI-GAD7), and 45 days post-intervention (DPI-GAD7). Participants were aware of group assignment, but data analysts were blinded. Group comparisons used Friedman and Kruskal-Wallis tests with Bonferroni-adjusted post-hoc analyses. We hypothesized that (1) all three interventions would reduce GAD-7 scores compared with control, (2) Gita would produce more durable effects than Yoga alone, and (3) the combined Yoga+Gita intervention would yield the greatest overall reduction in psychological distress. FINDINGS: Sixty-eight participants were enrolled (17 per group), with no losses to follow-up. B-GAD7 did not differ across groups (p = 0.908). Compared with Control, Yoga+Gita reduced anxiety immediately (p = 0.009), and Gita reduced anxiety at 45 days (p = 0.047). A clinically meaningful (≥4-point) GAD7 reduction at 45 days occurred in 17.6% (Yoga), 47.1% (Gita), 41.2% (Yoga+Gita), and 11.8% (Control) of the participants. Exploratory analyses suggested larger benefits among women and among those who continued practice. INTERPRETATION: Yoga and Gita-learning, particularly in combination, were associated with reductions in psychological distress among HCWs. TRIAL REGISTRATION: Prospectively registered with the CTRI (CTRI/2021/10/037365).
27 Apr 2026
Read appraisal →JMIR medical informatics
Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study
BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhibit degraded performance in patients with comorbidities. OBJECTIVE: This study aimed to introduce and validate a recalibration method using longitudinal patient data to enhance prediction accuracy and simulate its clinical utility for ongoing monitoring. METHODS: We conducted a multicenter, retrospective cohort study using data from 2 hospitals in Korea. A dataset of paired transthoracic echocardiograms (TTEs) and electrocardiograms (ECGs) matched within a 2-week interval was constructed, separating pairs into baseline (first for each patient) and follow-up assessments. In addition to conventional supervised learning, we developed a patient-wise recalibration strategy that incorporated historical left ventricular ejection fraction measurements and prior AI-ECG outputs to adjust for future predictions, thus empirically mitigating confounding effects. Pretraining was also implemented to enhance the model's performance. RESULTS: The recalibrated 12-lead DeepECG LVSD model achieved an area under the receiver operating curve of 0.956 (95% CI 0.946-0.965) for internal validation and 0.940 (95% CI 0.936-0.945) for external validation of follow-up TTE-ECG pairs. The uncalibrated 12-lead DeepECG LVSD model also showed modest performance, with an area under the receiver operating curve of 0.953 (95% CI 0.941-0.965) in the internal validation and 0.947 (95% CI 0.943-0.951) in the external validation when tested on baseline TTE-ECG pairs. Recalibration yielded statistically significant improvements in the 12-lead DeepECG LVSD models (P<.001), with enhanced and more balanced performance across all clinical subgroups. CONCLUSIONS: Patient-wise recalibration improved accuracy and consistency across various comorbidities by mitigating performance degradation and bias. This broadens the application of AI-ECG for LVSD detection from low-risk screening to high-risk longitudinal monitoring.
27 Apr 2026
Read appraisal →PloS one
Resilient road safety modeling through spatially disaggregated explainable AI
Understanding spatial disparities in traffic accident severity is essential not only for improving transport safety but also for advancing sustainable and inclusive transportation systems. Road crashes represent a persistent public health and equity challenge, directly linked to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-Being and SDG 11: Sustainable Cities and Communities). This study proposes an explainable, spatially segmented machine learning framework to examine urban-rural heterogeneity in crash outcomes, using disaggregated accident data from Kent, UK (2022-2024). By treating urban and rural systems as separate analytical units, the study captures risk heterogeneity that conventional pooled approaches often obscure. Among five tested models, Random Forest achieved the best performance and was further interpreted using SHapley Additive exPlanations (SHAP) to uncover how key factors differ in influence across spatial contexts. The results reveal a behavioral risk profile dominating in urban areas and infrastructure-driven risks in rural environments. These findings highlight the need for context-sensitive, evidence-based interventions that ensure transport equity across regions. Additionally, the study contributes to sustainable governance frameworks, enabling spatially adaptive risk mitigation, inclusive policy design and the long-term resilience of transport infrastructures.
27 Apr 2026
Read appraisal →Physics in medicine and biology
MRDT-GAN: generative adversarial network with multi-scale residual dense transformer generator for low-dose CT denoising
Objective.Low-dose computed tomography (LDCT) reduces radiation exposure but introduces noise and artifacts that degrade diagnostic quality. Existing deep learning-based denoising methods still face challenges such as over-smoothing, loss of fine structures, and uneven contrast. This study aims to develop an LDCT denoising framework that enhances noise suppression while preserving anatomical details and structural fidelity.Approach.We propose a multi-scale residual dense transformer generative adversarial network (MRDT-GAN). In the generator, we adopt the MRDT Block as the core unit, which introduces multi-scale strategy into residual dense network to reduce over-smoothing and preserve fine details, and also patching transformer block to capture long-range dependencies, mitigating distortions caused by localized receptive fields in convolutional neural network-based approaches. A hybrid attention module is also introduced in the generator to process spatial, frequency, and contrast information, enabling the network to focus on critical regions for noise suppression, improve contrast uniformity, and maintain texture consistency. In the discriminator, we adversarially explore differences on global, pixel, and also sub-scale between denoised LDCT and normal dose CT to better capture structural variations, reduce local noise and distortions, and ensure more realistic texture reconstruction while minimizing artifacts.Main results.We validate MRDT-GAN on both the NIH-AAPM-Mayo Clinic LDCT dataset and a real-world dataset. Experimental results indicate that MRDT-GAN achieves superior denoising performance compared with existing methods, effectively preserves details, enhances visual quality, and achieves a better balance between noise suppression and structural integrity.Significance.MRDT-GAN provides an effective and generalizable LDCT denoising solution that balances noise reduction with fine-detail preservation. By integrating MRDT modeling, hybrid attention mechanisms, and multi-difference adversarial learning, the framework offers improved clinical applicability and supports high-quality image reconstruction for downstream diagnostic tasks.
27 Apr 2026
Read appraisal →ACS sensors
Wearable Flexible Acoustic Alarm Device Based on MXene/Laser-Induced Graphene@Polyimide Thermoacoustic Film
Flexible acoustic devices have advantages of high flexibility and large sound output area, capable of adhering to the surface of the skin or clothing, which shows great potential in the field of wearable electronics. However, the design of acoustic material structures and their functional applications are still difficult. Here, we fabricated a porous laser-induced graphene (LIG) microarray layer on a polyimide (PI) film by laser direct writing. Subsequently, we uniformly coated the few-layer MXene nanosheets onto the LIG surface to obtain an MXene/LIG@PI film featuring low specific heat capacity per unit area, large interlayer spacing, excellent electrical conductivity, and high thermal conductivity. Meanwhile, the MXene/LIG@PI-based acoustic device composed of flexible copper electrodes exhibits excellent acoustic performance with a high sound pressure level of 77.6 dB and maintained a stable acoustic spectrum as the frequency increased from 20.0 Hz to 20.0 kHz. Finally, the portable acoustic alarm device composed of the piezoelectric module and the MXene/LIG@PI-based acoustic module was designed, which can be worn on various parts of the human body clothing, to provide audible alarm reminders in case of accidental falls or drops during outdoor activities. This study provides a novel approach for designing new flexible acoustic materials and devices.
26 Apr 2026
Read appraisal →ACS sensors
All-in-One Wearable Hydrogel Patch for Multimodal Visualized and Quantitative Heatstroke Monitoring
Heat-related illnesses, particularly heatstroke, pose serious risks to outdoor workers, athletes, and individuals with impaired thermoregulation. Existing monitoring strategies are limited by invasive measurements, reliance on external power sources, and lack of real-time multimodal feedback. Here, this study presents an all-in-one wearable patch that integrates a dual-functional self-powered thermogalvanic hydrogel with a selective electrochemical interface for intuitive visual feedback, dynamic temperature tracking, and durable sweat analysis. The hydrogel enables real-time thermochromic visualization and active thermoelectric sensing of body temperature, while an integrated electrochemical module allows continuous and selective detection of Na+ and K+ ions in sweat. The multiplexed physical-chemical health-monitoring patch provides a multimodal dual-stage evaluation of body temperature and sweat ion level toward improving metabolic status control and alerting individual to heatstroke risk, holding promise for developing personalized healthcare and a scalable route to proactive heatstroke prevention.
26 Apr 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Analysis of mental health outcomes in major versus minor upper extremity amputations: a retrospective national database study
BACKGROUND: Upper extremity amputations are associated with developing psychiatric conditions. The aim of this study is to identify differences in mental health outcomes for patients undergoing major versus minor upper extremity amputations. METHODS: Data were obtained from the PearlDiver database between 2010 and 2022 using Current Procedural Terminology and International Classification of Diseases codes. Patients aged 10 and above without prior mental health diagnoses or antidepressant prescription records who underwent upper extremity amputations were stratified by major (shoulder disarticulation, arm, forearm, wrist, transmetacarpal) versus minor (single metacarpal, digit, phalanx) amputations. Demographic characteristics and rates of mental health diagnoses, antidepressant prescriptions, and psychotherapy care were assessed for 90 days and one year postoperatively, using Welch's T-tests and Pearson's chi-squared tests. RESULTS: Patients with major amputations had increased risk of mental health diagnoses at 90 days (OR: 3.29, p < 0.001) and at one year (OR: 2.01, p < 0.001). Both groups had higher rates of mental health diagnoses than the general population. Patients undergoing major amputations had higher odds of starting antidepressants at 90 days (OR 3.82, p < 0.001) and at one year (OR 2.38, p < 0.001). Psychotherapy care was significantly increased after major amputations at 90 days (OR 5.47, p < 0.001) and at one year (OR 4.18, p < 0.001). CONCLUSIONS: Mental health disorders, antidepressant use, and psychotherapy care are significantly higher for major upper extremity amputations compared to minor amputations. Surgical teams should provide mental health resources to mitigate negative effects from mental health needs after upper extremity amputation. LEVEL OF EVIDENCE: III.
26 Apr 2026
Read appraisal →Journal of ethnopharmacology
Neuroprotective effects of Tongxinluo capsule in acute ischemic stroke: A systematic review and meta-analysis
ETHNOPHARMACOLOGICAL RELEVANCE: Tongxinluo capsule is widely used in patients with acute ischemic stroke (AIS), although the literature does not consistently agree on its efficacy and safety. AIM OF THE STUDY: We aimed to assess the efficacy and safety of Tongxinluo capsule for patients with AIS. MATERIALS AND METHODS: A systematic search of the databases PubMed, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Wanfang data, VIP Chinese Journal Service Platform, and SinoMed was conducted from their inception dates to January 2025. Randomized controlled trials (RCTs) that enrolled patients with AIS who receive Tongxinluo capsule and compared effects with placebo or as an add-on therapy to usual care were included. Literature screening, risk of bias assessment, and data extraction were independently conducted in duplicate. Fixed-effects or random-effects models were used to perform meta-analysis for pooling data from eligible studies. The primary outcome was long-term functional independence (defined as modified Rankin Scale [mRS] grade less than 2). Secondary outcomes included all-cause mortality, improvements in activities of daily living (measured with the Barthel Index [BI]), and neurological impairment (measured with the National Institutes of Health Stroke Scale [NIHSS]). Incidence of adverse events were identified as safety outcomes. RESULTS: We included 4842 patients with AIS from 12 RCTs (2422 [50.02 %] treated with Tongxinluo capsule) in the final analysis. Tongxinluo capsule treatment in AIS significantly improved the proportion of patients with mRS less than 2 (RR, 1.21; 95 % CI, 1.02 to 1.44), improved the change in BI score (MD, 11.47; 95 % CI, 10.34 to 12.60) and NIHSS score (MD, 3.48; 95 % CI, 2.87 to 4.09). No significant differences were detected in all-cause mortality (RR, 0.57; 95 % CI, 0.17 to 1.95), or the incidence of overall adverse events (RR, 1.01; 95 % CI, 0.91 to 1.12). CONCLUSION: Administration of Tongxinluo capsule for AIS demonstrated potential benefits in improving the long-term functional outcomes and activities of daily living, and relieving neurological impairment, without observing major safety concerns.
25 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study
BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced with anticoagulation therapy. Thus, early diagnosis of AF and AFL is essential. However, diagnosis may be challenging due to the paroxysmal and asymptomatic nature of these arrhythmias. OBJECTIVE: Current diagnostic workflows involve time-consuming and resource-intensive manual review of noisy signals and prolonged recordings. We evaluated a mobile system that combines a wireless wearable single-lead chest strap electrocardiogram (ECG) and a novel deep neural network (DNN)-based artificial intelligence (AI) method for detecting AF/AFL episodes, AF/AFL burden, and rhythm change and estimated the delay in the detection of rhythm change from AF/AFL to sinus rhythm. We also assessed the rhythm classification performance. METHODS: A total of 116 patients with recent-onset AF or AFL undergoing cardioversion were monitored using a mobile single-lead chest strap ECG system. Simultaneously, a 3-lead Holter ECG served as the reference. The DNN-based AI analyzed the single-lead chest strap ECG data to detect AF/AFL, non-AF/AFLrhythm, and noninterpretable segments, as well as to estimate AF/AFL burden and detect rhythm change. Performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, and intraclass correlation coefficient for AF and AFL burden estimation. RESULTS: The sensitivity and specificity for detecting AF/AFL were 91.9% (204.9/223.0 h) and 99.6% (242.4/243.5 h), respectively. The sensitivity for detecting AF was 96.2% (191.5/199.0 h), whereas it was 55.8% (13.4/24.0 h) for detecting AFL. The positive predictive value and negative predictive value for AF/AFL detection were 99.5% (204.9/206.0 h) and 93.1% (242.4/260.5 h), respectively. The intraclass correlation coefficient between the AF/AFL burden estimated by the DNN-based AI method and that derived from the physician-interpreted reference ECG was 0.96 (95% CI: 0.94-0.97; P<.001). Rhythm change detection occurred within 1 minute in most cases. CONCLUSIONS: The mobile single-lead chest strap ECG system powered by a DNN-based AI algorithm demonstrated strong performance in detecting AF, estimating AF burden, and recognizing rhythm change to sinus rhythm. This AI-driven approach enables automated and accurate rhythm analysis, supporting clinical decision-making. Further validation in real-world ambulatory settings is warranted.
25 Apr 2026
Read appraisal →The New England journal of medicine
Oral Nirmatrelvir-Ritonavir for Covid-19 in Higher-Risk Outpatients
BACKGROUND: Nirmatrelvir-ritonavir has been shown to reduce progression to severe illness from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in unvaccinated high-risk outpatients. The effectiveness of nirmatrelvir-ritonavir in persons who have been vaccinated, infected naturally, or both is unclear. METHODS: In two open-label platform trials (PANORAMIC in the United Kingdom and CanTreatCOVID in Canada), we enrolled higher-risk adults (≥50 years of age or ≥18 years of age with coexisting conditions) in the community who tested positive for SARS-CoV-2 and had been unwell for 5 days or less. The participants were randomly assigned to receive usual care plus nirmatrelvir (300 mg)-ritonavir (100 mg) twice a day for 5 days or to receive usual care alone. The primary outcome was hospitalization or death from any cause within 28 days after randomization. RESULTS: From December 8, 2021, to September 30, 2024, a total of 3516 participants in the PANORAMIC trial and 716 participants in the CanTreatCOVID trial underwent randomization. In the PANORAMIC trial, 14 of 1698 participants (0.8%) in the nirmatrelvir-ritonavir group and 11 of 1673 participants (0.7%) in the usual-care group were hospitalized or died (adjusted odds ratio, 1.18; 95% Bayesian credible interval, 0.55 to 2.62; probability of superiority, 0.334). In the CanTreatCOVID trial, 2 of 343 participants (0.6%) in the nirmatrelvir-ritonavir group and 4 of 324 participants (1.2%) in the usual-care group were hospitalized or died (adjusted odds ratio, 0.48; 95% Bayesian credible interval, 0.08 to 2.23; probability of superiority, 0.830). In a substudy involving 634 participants, viral load was reduced by the end of treatment with nirmatrelvir-ritonavir. Serious adverse events with nirmatrelvir-ritonavir were reported in 9 participants in the PANORAMIC trial and in 4 participants in the CanTreatCOVID trial. CONCLUSIONS: In two open-label trials, nirmatrelvir-ritonavir did not reduce the incidence of hospitalization or death among vaccinated higher-risk participants with SARS-CoV-2 infection. (Funded by the National Institute for Health and Care Research, and others; PANORAMIC ISRCTN number, 2021-005748-31; CanTreatCOVID ClinicalTrials.gov number, NCT05614349.).
25 Apr 2026
Read appraisal →Journal of plastic surgery and hand surgery
Characteristics and prognosis of skin cancer arising from burn scars: a systematic review
BACKGROUND: Burn scars are recognized risk factors for malignant skin transformation, most notably Marjolin's ulcer (MU). Despite extensive documentation in case reports and series, the epidemiological characteristics and prognosis of burn scar-related skin cancers have lacked large-scale systematic synthesis. METHODS: We conducted a systematic review in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, registered with PROSPERO (CRD42024545404), covering literature from PubMed, Scopus, and Web of Science up to 18 January 2024. Eligible studies included case reports, case series, and observational studies reporting any type of skin cancer in burn scars. Data were extracted on demographics, burn and tumor characteristics, treatment, latency, outcomes, and risk of bias using Joanna Briggs Institute tools. RESULTS: A total of 211 studies reporting 830 cases were included. The mean latency period from burn injury to cancer diagnosis was 21.7 years (SD = 19.6). Males constituted 53% of patients, with third-degree burns predominating (89.37%) and lower limbs being the most affected site (33.59%). Squamous cell carcinoma (SCC) was the most frequent malignancy (67.19%), followed by basal cell carcinoma (BCC, 3.83%) and other cancers (15.33%). Recurrence occurred in 13.2% of cases; mortality was 6.96%. SCC accounted for most deaths (63.8%), while melanoma and sarcoma exhibited high rates of recurrence and mortality. Lymph node metastasis and distant metastasis were found in 7.56 and 4.74% of cases, respectively. CONCLUSION: Skin cancers arising from burn scars, especially SCC, demonstrate aggressive clinical behavior with significant morbidity and mortality. Reduced latency periods and high metastatic potential highlight the importance of vigilant, long-term surveillance and radical initial treatment. This review provides a contemporary benchmark for epidemiological understanding and supports calls for international registries, molecular diagnostics, and standardized management protocols for burn scar malignancies.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Multimodal artificial intelligence for retinal detachment diagnosis using fundus imaging and patient questionnaires
BACKGROUND/AIMS: This study aimed to develop a multimodal artificial intelligence (AI) system that integrates fundus imaging and patient questionnaire data to achieve clinician-level diagnostic accuracy for diagnosing retinal detachment (RD). METHODS: Ultra-widefield fundus images and comprehensive patient questionnaires were collected from patients with RD and healthy controls at Tsukazaki Hospital. A multimodal model was developed using the Contrastive Language-Image Pretraining framework to classify RD cases, alongside separate image-only and questionnaire-only models for comparison. Per-image and per-subject analyses were conducted to assess model performance. RESULTS: The multimodal model outperformed single-modal models in per-image and per-subject assessments. It achieved accuracy, recall and F1 scores of 0.899±0.054, 0.902±0.043 and 0.902±0.048 in the per-image analysis and 0.893±0.071, 0.949±0.044 and 0.873±0.074 in the per-subject analysis, respectively. The AI model's overall diagnostic accuracy was slightly lower than that of human clinicians; however, it exhibited a higher recall rate, indicating improved detection of true RD cases. CONCLUSION: Integrating fundus imaging with patient questionnaire data significantly improves AI-based RD diagnosis. Future research needs to focus on expanding the dataset and refining the questionnaire design to further improve model performance.
25 Apr 2026
Read appraisal →Nutricion hospitalaria
Refeeding syndrome ‒ What we know and what remains to be explored.
Background: refeeding syndrome (RS) is a serious metabolic complication that occurs upon reintroducing nutrition to malnourished individuals. Although formally recognized for more than 70 years, significant gaps in its understanding remain. Objective: to conduct a narrative review on the current state of knowledge regarding RS, its recent advances, existing gaps, and future perspectives. Methods: an integrative review was conducted using articles published between 2000 and 2024 from international databases, following a systematic methodology of literature search, selection, and analysis. Results: the main pathophysiological mechanisms of RS, its multisystemic clinical manifestations, current diagnostic criteria, and risk-based therapeutic strategies were identified. Despite advances in biomarkers and personalized nutrition, challenges remain in early diagnosis, prevention, and treatment, especially in vulnerable populations. Conclusions: RS continues to pose a complex clinical challenge that requires standardized protocols, identification of reliable biomarkers, and increased clinical research, particularly in underrepresented regions such as Latin America. Prevention, based on a multidisciplinary approach, is important to reducing its impact. Introducción: el síndrome de realimentación (SR) es una complicación metabólica grave que ocurre tras reintroducir la nutrición en personas desnutridas. Aunque formalmente reconocido desde hace más de 70 años, aún existen importantes lagunas en su comprensión. Objetivo: realizar una revisión narrativa sobre el estado actual del conocimiento del SR, sus avances recientes, las brechas existentes y las perspectivas futuras. Métodos: se realizó una revisión integrativa de artículos publicados entre 2000 y 2024 en bases de datos internacionales, siguiendo una metodología sistemática de búsqueda, selección y análisis de la literatura. Resultados: se identificaron los principales mecanismos fisiopatológicos del SR, sus manifestaciones clínicas multisistémicas, los criterios diagnósticos actuales y las estrategias terapéuticas basadas en el riesgo. A pesar de los avances en términos de biomarcadores y nutrición personalizada, persisten desafíos en el diagnóstico precoz, la prevención y el tratamiento, especialmente en las poblaciones vulnerables. Conclusiones: el SR sigue siendo un reto clínico complejo que exige protocolos estandarizados, identificación de biomarcadores fiables y una mayor investigación clínica, especialmente en regiones poco estudiadas como América Latina. La prevención basada en un enfoque multidisciplinario es clave para reducir su impacto.
25 Apr 2026
Read appraisal →Neurology
Virtual vs In-Person Neurologic Ambulatory Care: A Case-Control Study of Subsequent Health Care Utilization
BACKGROUND AND OBJECTIVES: Implementation of telemedicine expanded options for outpatient neurology care. It remains uncertain which new neurology patients can be appropriately evaluated virtually. We compared subsequent health care utilization after virtual vs in-person new patient neurology visits across 3 academic medical centers. METHODS: We conducted a retrospective multicenter cohort study of adults with a new outpatient neurology visit from September 2020 through December 2021 using the Vizient Clinical Data Base and Clinical Practice Solutions Center databases. Virtual and in-person patients were matched 1:1 using propensity scores incorporating demographics, clinical characteristics, time period, and previous health care utilization. Outcomes were analyzed overall and stratified by neurologic chief complaint category and institution. We compared rates of subsequent neurologic clinic follow-up, emergency department (ED) visits, and hospitalizations after virtual and in-person encounters. Testing and all-cause ED visits/hospitalizations were also assessed. RESULTS: We identified 10,428 virtual and 36,767 in-person neurology new outpatient visits. After propensity score matching, 8,202 virtual visits were matched to 8,202 in-person visits. Neurology follow-up within 90 days did not differ between virtual and in-person visits (24.6% vs 23.7%, p = 0.18). Thirty-day neurology clinic follow-up was slightly lower after virtual visits, whereas follow-up at 6 months and 1 year was similar between groups. Neurologic ED visits and hospitalizations within 90 days were similar (0.9% vs 0.8%, p = 0.23 and 1.8% vs 1.7%, p = 0.47, respectively). All-cause ED visits and hospitalizations within 90 days were also comparable (1.8% vs 1.7%, p = 0.59 and 2.2% vs 1.8%, p = 0.13, respectively). Analyses by chief complaint found that 90-day follow-up was higher after in-person visits for dementia, whereas 30- and 90-day follow-up was higher after virtual visits for Parkinson disease and multiple sclerosis, and 90-day follow-up was higher after virtual visits for headache. Testing was more frequent after in-person visits for certain chief complaints. DISCUSSION: In this propensity score-matched multicenter cohort, new neurology patients seen virtually had similar downstream utilization as those seen in-person, including comparable 90-day follow-up and similar neurologic and all-cause ED visits and hospitalizations. Although follow-up varied modestly by chief complaint and testing was more frequent after some in-person visits, no major differences emerged overall.
25 Apr 2026
Read appraisal →The New England journal of medicine
Ketamine or Etomidate for Tracheal Intubation of Critically Ill Adults
BACKGROUND: For critically ill adults undergoing tracheal intubation, observational studies suggest that the use of etomidate to induce anesthesia may increase the risk of death. Whether the use of ketamine rather than etomidate decreases the risk of death is uncertain. METHODS: In a randomized trial conducted in 14 emergency departments and intensive care units in the United States, we randomly assigned critically ill adults who were undergoing tracheal intubation to receive ketamine or etomidate for the induction of anesthesia. The primary outcome was in-hospital death from any cause by day 28. The secondary outcome was cardiovascular collapse during intubation, defined by the occurrence of a systolic blood pressure below 65 mm Hg, receipt of a new or increased dose of vasopressors, or cardiac arrest. RESULTS: A total of 2365 patients underwent randomization and were included in the trial population; 1176 were assigned to the ketamine group and 1189 to the etomidate group. In-hospital death by day 28 occurred in 330 of 1173 patients (28.1%) in the ketamine group and in 345 of 1186 patients (29.1%) in the etomidate group (risk difference adjusted for trial site, -0.8 percentage points; 95% confidence interval [CI], -4.5 to 2.9; P = 0.65). Cardiovascular collapse during intubation occurred in 260 of 1176 patients (22.1%) in the ketamine group and in 202 of 1189 patients (17.0%) in the etomidate group (risk difference, 5.1 percentage points; 95% CI, 1.9 to 8.3). Prespecified safety outcomes were similar in the two groups. CONCLUSIONS: Among critically ill adults undergoing tracheal intubation, the use of ketamine to induce anesthesia did not result in a significantly lower incidence of in-hospital death by day 28 than etomidate. (Funded by the Patient-Centered Outcomes Research Institute and others; RSI ClinicalTrials.gov number, NCT05277896.).
24 Apr 2026
Read appraisal →Renal failure
Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study
Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and accurate prediction remains challenging due to the lack of reliable biomarkers. Urinary small-molecule metabolomics offers a promising approach to capture the dynamic metabolic changes during the progression of S-AKI. In this two-center prospective observational study, we enrolled 360 children from both centers. Urine samples were collected within 24h after hospitalized children diagnosed with sepsis, stored at -80 °C, and analyzed using gas chromatography-mass spectrometry (GC-MS). Based on urinary metabolic fingerprints (U-MF), we developed and validated a machine learning model for early prediction of S-AKI. The Shapley Additive Explanations (SHAP) algorithm was applied to visually explain the optimal model. A panel of 10 metabolites was selected as common discriminative features. Among the 4 machine learning models evaluated, the support vector machine (SVM) demonstrated the best performance in both the discovery cohort (AUC 0.94, 95% CI: 0.91-0.98) and the external validation cohort (AUC 0.89, 95% CI: 0.82-0.96), enabling early prediction of S-AKI within 24 h. Furthermore, the U-MF panel was integrated into an open-access online platform to facilitate clinical translation. Our findings suggest that U-MF combined with machine learning holds promise as a robust and noninvasive approach with potential utility for early prediction of S-AKI in pediatric patients.
24 Apr 2026
Read appraisal →European heart journal
Predictors of recurrent venous thromboembolism and bleeding in patients with cancer: a meta-analysis
BACKGROUND AND AIMS: Patients with cancer and venous thromboembolism (VTE) have a high risk of recurrent VTE and anticoagulant-related bleeding. This study aimed to identify prognostic factors for these complications. METHODS: A systematic review was performed for randomized trials and cohort studies evaluating prognostic factors for recurrent VTE or anticoagulant-related bleeding in adult patients with cancer and VTE. Adjusted hazard ratios (aHRs) for factors were pooled using random-effects meta-analysis. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. RESULTS: Thirty-three studies (n = 96 753) were included in the meta-analyses. Factors with high certainty of association with increased risk of recurrent VTE included a previous history of VTE [aHR 1.50 (95% CI 1.08-2.09)], Eastern Cooperative Oncology Group (ECOG) performance status >0 [1.81 (1.34-2.46)] or >1 [2.44 (1.55-3.84)], advanced cancer [1.38 (1.15-1.65)], and specific cancer sites including lung [1.78 (1.29-2.46)], hepatobiliary [2.37 (1.70-3.30)], pancreas [3.20 (2.06-4.96)], and genitourinary [1.38 (1.14-1.67)]. Conversely, recent surgery [aHR 0.56 (95% CI 0.40-0.76)] and breast cancer [0.43 (0.23-0.81)] had a high certainty of association with a decreased risk. Factors with a high certainty of association with an increased risk of anticoagulant-related bleeding included a history of bleeding [aHR 2.41 (95% CI 1.50-3.88)], ECOG performance status ≥2 [2.10 (1.48-2.99)], advanced cancer [1.60 (1.29-1.97)], and cancers of the brain [2.25 (1.64-3.09)], gastrointestinal system [1.74 (1.44-2.11)], genitourinary system [1.90 (1.48-2.45)], and prostate [1.72 (1.26-2.34)]. CONCLUSIONS: The prognostic factors identified in this meta-analysis should be considered as part of risk stratification frameworks for anticoagulation management in patients with cancer and VTE.
24 Apr 2026
Read appraisal →European heart journal
Pre-treatment lysis time of plasma-derived fibrin clots and bleeding in patients on oral anticoagulants for atrial fibrillation in the ARISTOTLE trial
BACKGROUND AND AIMS: Oral anticoagulation reduces stroke risk in patients with atrial fibrillation (AF) but increases bleeding. Longer fibrin clot lysis time has been shown to predict adverse cardiovascular outcomes in acute coronary syndromes. This study explored relationships between fibrin clot lysis time at randomization and clinical outcomes in patients with AF enrolled in the Apixaban for Reduction in Stroke and Other Thromboembolic Events in AF (ARISTOTLE) trial. METHODS: Plasma samples were obtained from anticoagulation-naïve participants, before initiation of study medication (n = 1841). Fibrin clot turbidimetry was performed, and lysis time determined. Associations between lysis time and characteristics, biomarkers, and on-treatment bleeding and cardiovascular events were assessed by lysis time quartile (Q1-4, shortest to longest). RESULTS: A shorter lysis time was associated with being older, male, permanent AF, lower body mass index, estimated glomerular filtration rate and C-reactive protein, and higher N-terminal pro-B-type natriuretic peptide. Major and clinically relevant non-major bleeding was significantly more frequent in lysis time Q1 vs. Q4 [6.3%/yr vs. 2.1%/yr; HR, 2.99 (95% CI, 1.75-5.12); P = .001], including after multifactorial adjustment [HR, 2.61 (1.45-4.69); P = .016]. Those in Q2 and Q3 had intermediate bleeding risk vs. Q4 [HR, 2.21 (1.27-3.87); 2.08 (1.18-3.66) respectively], suggesting a graduated effect. Treatment allocation to apixaban vs. warfarin did not affect the relationship between lysis time and bleeding (interaction-P = .80). There was no significant association between lysis time and a composite of cardiovascular death, stroke, systemic embolism or myocardial infarction. CONCLUSIONS: Shorter pre-treatment fibrin clot lysis time independently predicted higher bleeding risk in patients receiving oral anticoagulation for AF.
24 Apr 2026
Read appraisal →BMJ quality & safety
Sustainability of the de-implementation of low-value care in infants with bronchiolitis: 2-year follow-up of a cluster randomised controlled trial
BACKGROUND: In 2017, the PREDICT (Paediatric Research in Emergency Departments International Collaborative) network conducted a cluster randomised controlled trial (cRCT) at 26 Australian and New Zealand hospitals to improve bronchiolitis care. Findings demonstrated that targeted interventions significantly improved adherence with five evidence-based low-value bronchiolitis practices (no chest radiography, salbutamol, glucocorticoids, antibiotics and epinephrine) in the first 24 hours of hospitalisation (adjusted risk difference, 14.1%; 95% CI: 6.5% to 21.7%; p<0.001). During the intervention year (2017), intervention hospital (n=13) compliance was 85.1% (95% CI: 82.6% to 89.7%). This study aimed to determine if improvements in bronchiolitis management were sustained at intervention hospitals 2 years post-trial completion. METHODS: International, multicentre follow-up study of hospitals in Australia and New Zealand that participated in a cRCT of de-implementation of low-value bronchiolitis practices, 1 year (2018) and 2 years (2019) post-trial completion, obtained retrospectively from medical audits. Sustainability was defined a priori as no more than a <7% decrease to any level of improvement in adherence for all five low-value practices (composite outcome) from the cRCT intervention year. RESULTS: Of the 26 hospitals, 11 intervention and 10 control hospitals agreed to participate in the follow-up study. Data were collected on 3299 infants with bronchiolitis 1 year (intervention and control hospitals) and 1689 infants 2 years post-trial (intervention hospitals). Adherence with no use of the five low-value practices 2 years post-trial completion was 80.9% (adjusted predicted adherence, 80.8%, 95% CI: 77.4% to 84.2%; estimated risk difference from cRCT outcome -3.9%, 95% CI: -8.6% to 0.8%) at intervention hospitals, fulfilling the a priori definition of sustainability. DISCUSSION: Targeted interventions, delivered over one bronchiolitis season, resulted in sustained improvements in bronchiolitis management in infants 2 years later. This follow-up study provides evidence for sustainability in de-implementing low-value care in bronchiolitis management. TRIAL REGISTRATION DETAILS: Australian and New Zealand Clinical Trials Registry No: ACTRN12621001287820.
23 Apr 2026
Read appraisal →ACS applied materials & interfaces
Flexible, Scalable SERS Substrate with High Sensitivity and Stability for On-Site Detection and Wearable Applications
A key challenge in the commercialization of surface-enhanced Raman scattering (SERS) technology is the scalable and controllable preparation of substrates that simultaneously achieve high sensitivity, uniform signal distribution, excellent reproducibility, and long-term stability. In this study, we report a flexible SERS substrate based on a porous, high-surface-energy nanofilm, designed to serve as a spatially confined template for the uniform growth of silver nanoparticles (AgNPs). This substrate exhibits outstanding SERS performance with an enhancement factor (EF) of 2.05 × 1010 and a detection limit (DL) as low as 1.74 × 10-15 M for rhodamine 6G (R6G). The preparation process is mild, highly reproducible (RSD = 8.53% over 60 consecutive batches), and scalable to 25 cm2 areas. Moreover, the substrate maintains its SERS activity for over three months within a vacuum bag at room temperature. Owing to its excellent adhesion and good flexibility, the substrate can adhere effectively to complex surfaces. The substrate was successfully applied to sensitive in situ detection of trace thiram residues on grape surfaces, achieving a detection limit (DL) of 8.51 × 10-10 g cm-2. Additionally, a wearable sensor was developed based on the substrate and used for the noninvasive monitoring of uric acid levels in human sweat, with a DL of 2.95 × 10-7 M. This work provides a practical strategy for constructing high-performance, scalable, and flexible SERS substrates, advancing their potential for real-time, on-site analysis.
23 Apr 2026
Read appraisal →PloS one
Prevalence of antimicrobial resistance in Tanzania: A systematic review and meta-analysis
Antimicrobial resistance (AMR) threatens global health, and understanding resistance patterns aids in effective treatment and promotes responsible antimicrobial use. Despite the urgency of resistant pathogens, systematic reviews focusing specifically on Tanzania are limited, and while several studies report resistance patterns for individual pathogens, a consolidated analysis of overall prevalence is needed to inform policymaking and public health interventions. Therefore, this review and meta-analysis assessed the prevalence of antimicrobial resistance among clinically relevant pathogens in Tanzania, providing a comprehensive overview to support surveillance, infection control, and stewardship efforts. A total of 1865 studies identified from Google Scholar (1600), PubMed (13), and Science Direct (252) underwent screening and full article review. Finally, 28 studies were included. A subgroup analysis was performed to evaluate the resistance patterns within antibiotic classes for specific pathogens. Descriptive statistics were used to describe the characteristics of the studies, while the prevalence of antimicrobial resistance was estimated through Meta-analysis. Inconsistency and heterogeneity between studies were quantified by the I2 index. Among the included studies, most isolates (25.0%) were obtained from urine samples. Of these studies, 75% were cross-sectional studies and 92.9% were conducted in hospital settings. The analysis revealed high resistance to penicillin, particularly amoxicillin-clavulanic and ampicillin, with Klebsiella pneumoniae (0.96 [0.83-0.99]), Acinetobacter baumannii (0.94 [0.67-0.99]) and Escherichia coli (0.90 [0.81-0.95]). Similarly, erythromycin resistance was most prevalent in Campylobacter spp. (0.85 [0.80-0.89]). Ciprofloxacin resistance was highest in Acinetobacter baumannii (0.54 [0.33-0.73]), whereas amikacin resistance was highest in Proteus spp. (0.86 [0.35-0.99]). Ceftriaxone resistance was particularly high in Acinetobacter baumannii (0.91 [0.70-0.98]) and Pseudomonas aeruginosa (0.85 [0.74-0.92]). Meropenem resistance was lowest among Escherichia coli (0.04 [0.01-0.10]) and Klebsiella spp. (0.07 [0.03-0.15]), while the pooled resistance across ESKAPE-E pathogens was (0.11[0.06-0.19]). Imipenem and clindamycin each had an overall pooled resistance of (0.06[0.02-0.14]) against both Escherichia coli and Klebsiella pneumoniae. The findings highlight widespread resistance among bacterial pathogens, ESKAPE-E, particularly in the Access and Watch groups of antibiotics. The variability in resistance patterns underscores the need for the Ministry of Health to re-evaluate empirical treatment protocols (STG/NEMLIT) to ensure effective treatment regimens, strengthen antimicrobial stewardship, enhance surveillance systems, and promote rational antibiotic use.
23 Apr 2026
Read appraisal →The Analyst
Third-generation nanopore sequencing: advancing real-time pathogen detection and antimicrobial resistance surveillance in food safety applications
Foodborne diseases impact 600 million people annually, causing 420 000 deaths and over $110 billion in economic losses. Conventional pathogen detection methods take 3-7 days and lack sensitivity for low-abundance pathogens and antimicrobial resistance (AMR) genes. This review evaluates nanopore sequencing's potential in food microbiology, analyzing real-time pathogen detection, AMR surveillance, and performance versus traditional methods. A systematic review of 80 studies (2015-2025) revealed nanopore sequencing reduces detection time to <24 hours with 95-98% species identification accuracy and >90% AMR gene sensitivity, at $50-200 per sample versus $300-800 for traditional whole-genome sequencing. It enables multiplex pathogen detection, viable but non-culturable organism identification, and real-time AMR profiling. However, challenges include error rates of 8-15% in homopolymeric regions and the need for bioinformatics expertise. Addressing challenges, including error rates of 8-15% in homopolymeric regions and the need for bioinformatics expertise, requires strategic mitigation approaches. Despite limitations, advances in base-calling and workflows make nanopore sequencing a transformative tool for next-generation food safety surveillance.
23 Apr 2026
Read appraisal →Critical care explorations
The Effects of Conservative Fluid Management and Active Deresuscitation on Markers of Tissue Perfusion, Kidney Injury, and Vascular Injury in Critically Ill Adults: A Secondary Analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) Trial
OBJECTIVES: Test the hypothesis that conservative fluid management with active deresuscitation would not adversely affect tissue perfusion or kidney injury and would be associated with reduced vascular injury compared with usual care. DESIGN: Secondary analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) trial. SETTING: ICUs. PATIENTS OR SUBJECTS: Critically ill patients enrolled in the RADAR-2 trial. INTERVENTIONS: Conservative fluid management with active deresuscitation vs. usual care. MEASUREMENTS AND MAIN RESULTS: Measures of tissue hypoperfusion (whole blood lactate), acute kidney injury (AKIRisk score and urinary cystatin-C), and vascular injury (plasma hyaluronan, syndecan-1, and angiopoietin-2) were compared between groups. For each analyte, change from baseline was compared between groups and the median inter-group difference at each timepoint was estimated with bootstrapped CIs. Exploratory logistic regression examined associations between plasma biomarker levels (including N-terminal pro-B-type natriuretic peptide [NT-proBNP]), 28-day mortality, and treatment allocation. Whole blood lactate levels were similar between groups at all timepoints. Using change from baseline comparisons, no statistically detectable between-group differences were observed in AKIRisk scores or urinary cystatin-C levels. Plasma vascular injury biomarkers showed no statistically detectable between-group differences at any timepoint. High baseline hyaluronan (adjusted odds ratio [aOR], 5.75; 95% CI, 1.94-17.02; p = 0.002), syndecan-1 (aOR, 8.82; 95% CI, 2.67-29.15; p < 0.001), and NT-proBNP greater than 2500 pg/mL (aOR, 21.48; 95% CI, 3.57-129.41; p < 0.001) were independently associated with increased 28-day mortality. There was no evidence of differential treatment response based on these biomarker levels. CONCLUSIONS: Conservative fluid management and active deresuscitation were not associated with worsening tissue perfusion or acute kidney injury. A reduction in vascular injury markers was not observed. Given the modest sample size and resultant imprecision, clinically important effects cannot be excluded.
23 Apr 2026
Read appraisal →Infectious diseases of poverty
Progress and challenges in development of minimum essential dataset for disease surveillance through a One Health lens: a scoping review
BACKGROUND: Minimum essential dataset (MED) enables One Health surveillance by facilitating cross-sectoral data sharing. Yet, existing MED research represents insufficient integration of the One Health concept, and its progress in disease surveillance remains unclear. This study aims to evaluate the current progress and gaps in MED research in disease surveillance through a One Health lens. METHODS: This scoping review systematically searched Embase, PubMed, Scopus, and Web of Science from the inception of databases to November 30th, 2024 to identify studies published in English. Studies were independently screened by two reviewers for inclusion based on their relevance to the development of MEDs for disease surveillance. The progress and challenges were synthesized based on data extracted from eligible studies. RESULTS: This review includes 28 eligible studies for analysis, all of which focused on infectious diseases. MED development is predominantly concentrated on the human health interface (89.3%), whereas animal and environmental interfaces are comparatively limited (≤ 10.7%). The One Health concept has been adopted in 16 of the eligible studies; however, there is still insufficient interdisciplinary and cross-sectoral collaboration (57.1%). Meanwhile, numerous methods have been employed in the development of MEDs, with qualitative approaches being the most prevalent (42.9%), while data-driven approaches remain scarce. The absence of a standardized approach is recognized as a primary barrier. Thus, we propose a pathway and operational tool to detail the specific steps, which could facilitate future research on MED. CONCLUSION: This review highlights critical gaps in MED development at the human-animal-environment interface, and proposes a pathway and operational tool to inform future development of MED, including scope identification, establishment, modification and improvement. This study promotes the improvement of disease surveillance system and provides insights to enhance future preparedness for reducing the global public health burden.
23 Apr 2026
Read appraisal →The Analyst
Recent progress in wearable electrochemical sensors based on MXene-conductive hydrogels
MXene-based conductive hydrogels (MCHs) have emerged as highly promising materials for next-generation wearable electrochemical sensors, owing to their exceptional electrical conductivity, mechanical flexibility, and biocompatibility. This review provides a comprehensive and up-to-date overview of recent advances in the development of wearable sensors that incorporate Ti3C2Tx MXene hydrogels for the detection of a wide range of analytes in various biofluids. Diverse hydrogel matrices have been integrated with MXenes to fabricate highly sensitive platforms capable of monitoring key biomarkers such as glucose, dopamine, uric acid, lactate, norepinephrine, sodium, creatinine, and pH. These sensors have been successfully deployed at various locations in the body, including the forearm, chest, wrist, and head, using flexible formats such as skin patches, microfluidic devices, pantyliners, wearable caps, and attachable body accessories. Notably, several configurations demonstrate ultralow detection limits, reaching the nanomolar level, enabling real-time, noninvasive analysis of sweat, urine, and other physiological fluids. The wide range of functional additives and customizable design approaches underscores the modularity and tunability of MCH-based systems for specific applications. This review critically evaluates the design principles, sensing mechanisms, performance metrics, and practical limitations of current MCH-based wearable platforms, providing insights that can guide future innovations in smart wearable healthcare technologies.
23 Apr 2026
Read appraisal →Journal of Korean medical science
Comparison of Clinical and Radiological Outcomes Between Suction Aspiration and Combination Methods of Mechanical Thrombectomy in Patients With Acute Cerebral Infarction: The COMPETE Trial
BACKGROUND: Recent advances in suction catheter technology suggest that suction aspiration may prevent thrombus fragmentation during mechanical thrombectomy. We therefore hypothesize that the radiological outcomes of suction aspiration as a first-line treatment would not be inferior to those of the combination technique. This study aims to evaluate the radiological and clinical outcomes of first-line suction aspiration compared with those of the first-line combination technique for large artery occlusion amenable to both modalities. METHODS: The COMPETE trial is a prospective, randomized, multicenter, noninferiority, open-label trial. A total of 1,136 patients will be randomized at a 1:1 ratio to either the suction aspiration or combination technique groups. RESULTS: The primary endpoints are successful recanalization (modified Thrombolysis in Cerebral Infarction scale score ≥ 2b) and a good clinical outcome (modified Rankin Scale score of 0-2) at 90 days after thrombectomy. The technical outcome is the switching rate from the combination technique to suction aspiration and from suction aspiration to the combination technique. The safety outcomes include procedural morbidity, nonprocedural morbidity, procedural mortality, and nonprocedural mortality. CONCLUSION: The COMPETE trial is expected to determine whether the use of suction aspiration as a first-line approach in patients with acute ischemic stroke is noninferior to the combination technique in achieving successful radiological and good clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06254755; CRIS (KCT0007726).
23 Apr 2026
Read appraisal →European journal of pediatrics
Different doses of vitamin A supplementation as adjuvant treatment for pneumonia in children: a network meta-analysis of randomized controlled trials
UNLABELLED: While previous meta-analyses have established a link between vitamin A supplementation and outcomes in children with pneumonia, the efficacy and safety of different dosing regimens remain unclear. Existing evidence is predominantly based on single-dose trials, creating a critical knowledge gap. We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang and VIP databases from their inception to December 2025. Study searches were independently carried out in December 2025 by two reviewers. Two researchers used the Cochrane risk of bias tool to independently assess the quality of the included studies. Data analysis was conducted through the "netmeta" package in R software (version 4.3.3). Twenty-five trials involving 3116 patients were included. Network meta-analysis revealed that medium-dose vitamin A supplementation was most effective in reducing the duration of fever and cough. Low-to-medium doses were consistently associated with better clinical outcomes than high-dose regimens. However, evidence regarding safety outcomes remained limited and inconclusive. CONCLUSION: Low-to-medium dose vitamin A supplementation appears superior to high-dose regimens for alleviating clinical symptoms in children with pneumonia. However, the evidence is limited by high heterogeneity, indirect comparisons between dose groups, and poor reporting of adverse events. Future trials should prioritize direct dose-comparisons and rigorous safety monitoring to refine dosing guidelines. WHAT IS KNOWN: • Pneumonia poses a significant threat to the health and lives of children. Vitamin A supplementation plays an adjuvant role in the treatment of pneumonia in children. • There are currently divergent conclusions in the research regarding the optimal dosage of vitamin A, particularly with regard to the safety of high-dose supplementation and the effectiveness of low-dose supplementation. WHAT IS NEW: • This study revealed that medium-to-low-dose vitamin A supplementation appears to be more effective than high-dose therapy in alleviating the primary symptoms of childhood pneumonia. • This study also suggests that children in developed regions have higher baseline serum retinol levels, which may diminish the marginal benefits of high-dose supplementation and may even predispose them to its immunosuppressive effects.
22 Apr 2026
Read appraisal →Medicine
Saturation effect of anion gap in predicting long-term mortality in critically ill patients with chronic obstructive pulmonary disease (COPD): A retrospective cohort study based on the MIMIC-IV database
The relationship between anion gap (AG) and long-term mortality in intensive care unit has been widely reported, but whether this association exists in critically ill patients with chronic obstructive pulmonary disease (COPD) is still unknown. The data of this study were collected from the Medical Information Mart for Intensive Care-IV. First of all, we used the Cox regression analysis and Kaplan-Meier curves to measure the relationship between AG and 365-day mortality for critically ill patients with COPD. Next, a restricted cubic spline was used to analyze the relationship between AG and mortality. Finally, age, sex, weight, hypertension, type 2 diabetes mellitus, heart failure, myocardial infarction, and chronic kidney disease were considered for subgroup analysis. A total of 2594 eligible subjects were sampled, of which 36.24% died within 365 days of intensive care unit admission. Cox regression analysis, after adjusting for confounders, demonstrated a significant association between AG and 365-day mortality in patients with COPD (hazard ratio = 1.03, 95% confidence interval: 1.01-1.05. P < .05). Stratifying AG into quartiles revealed higher levels of AG associated with an increased risk of death (Q1: 1.00, Q2: 1.34 [1.09-1.66], Q3: 1.44 [1.17-1.78], and Q4: 1.49 [1.19-1.87]). Additionally, restricted cubic spline analysis indicated a nonlinear relationship, with a critical value of AG at 14 mmol/L. Subgroup analysis highlighted AG as a significant predictor of long-term mortality in COPD patients across different subgroups, with an interaction effect observed in the subgroup with type 2 diabetes mellitus. In critically ill patients with COPD, there was a significant positive association between AG and 365-day mortality. In addition, there is a saturation effect at AG of 14 mmol/L.
20 Apr 2026
Read appraisal →PloS one
Comparison of OneChoice AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru
Bacteremia is a major contributor to global morbidity and mortality, particularly in low- and middle-income countries where diagnostic delays and empirical antimicrobial misuse exacerbate resistance. This study assessed the accuracy of OneChoice®, an artificial intelligence (AI)-based Clinical Decision Support System (CDSS), in guiding antimicrobial therapy for bloodstream infections (BSIs) in Lima, Peru. A cross-sectional, observational design was used, comparing therapeutic recommendations generated by OneChoice®-based on molecular (FilmArray®) and phenotypic (MALDI-TOF MS, VITEK2) data-with the clinical decisions of 94 physicians (35 infectious disease [ID] specialists and 59 non-specialists) across 366 survey-based evaluations of bacteremia cases. Concordance between CDSS and physician decisions was analyzed using Cohen's Kappa and logistic regression. The overall concordance rate was 96.1% when considering any suggested treatment, and 74.6% for the top recommendation, with a substantial agreement (κ = 0.70). ID specialists showed significantly higher concordance (κ = 0.78) than non-ID physicians (κ = 0.61), and specialization was the strongest predictor of agreement (OR = 2.26, p = 0.001). Escherichia coli cases had the highest concordance, while Pseudomonas aeruginosa showed the lowest. The CDSS reduced inappropriate antibiotic use, particularly unnecessary carbapenem prescriptions. These findings support the utility of AI-CDSS tools in enhancing antimicrobial stewardship and standardizing care, especially in resource-limited healthcare settings.
20 Apr 2026
Read appraisal →Gut microbes
Re-establishing bile acid composition after treatment of recurrent Clostridioides difficile infection with fecal microbiota transplantation compared with oral vancomycin or a 12-strain bacterial mixture
Patients with Clostridioides difficile infection have high colonic levels of primary bile acids, which are potent germinators of Clostridioides difficile. Several studies have suggested that re-establishing a normal bile acid composition is a key factor in fecal microbiota transplantation (FMT) for recurrent C. difficile infection, yet former studies supporting this lacked controls. In a subgroup from a randomized controlled trial, we compared the bile acid composition in patients with recurrent C. difficile infection treated with either FMT, a bacterial mixture, or vancomycin. The fecal bile acid content was analyzed several times before and after treatments. Furthermore, we used 16S rDNA gene sequencing to analyze the presence of some bacterial species involved in bile acid metabolism. Stool donors served as healthy controls. We observed a higher proportion of primary bile acids in patients with recurrent C. difficile infection than in donors, yet a donor-like dominance of secondary bile acids was observed after successful treatment in all groups. The shift seemed to occur earliest in the FMT group, followed by the vancomycin group, and the latest in the bacterial mixture group. In approximately half of the participants, the rise in secondary bile acids was timely associated with the detection of bile acid-transforming bacteria that were absent before treatment. Our findings indicate that FMT re-establishes the bile acid composition faster than vancomycin, reducing the time of susceptibility to recurrences of C. difficile infection. Hence, bacterial mixtures developed as an alternative to donor stool for treating recurrent C. difficile infection might benefit from including bile acid-metabolizing bacteria.
19 Apr 2026
Read appraisal →PloS one
Hybrid feature-selection and diversity-guided stacking framework for interpretable ensemble learning: Application to COVID-19 mortality prediction
BACKGROUND: Reliable predictive modeling in high-dimensional biomedical data requires a balance between accuracy, interpretability, and computational efficiency. However, existing ensemble methods often overlook model diversity or rely on ad hoc feature-selection approaches, which limit generalizability. This study introduces a hybrid feature-selection and diversity-guided stacking framework designed to improve robustness and scalability across clinical and other data-intensive domains. METHODS: The proposed framework integrates a hybrid feature-selection pipeline-combining Variance Inflation Factor (VIF), Analysis of Variance (ANOVA), Sequential Backward Elimination (SBE), and Lasso regression-to reduce multicollinearity and overfitting. It also employs a diversity-aware stacking strategy that constructs sub-model sets based on pairwise diversity measures (Disagreement, Yule's Q, and Cohen's Kappa) and non-pairwise metrics (Entropy and Kohavi-Wolpert). Sixteen base classifiers and five meta-learners were trained using repeated 10-fold cross-validation. The framework was evaluated using data from 4,778 hospitalized COVID-19 patients with 116 clinical and laboratory attributes, preprocessed using robust scaling and ROSE-based class balancing. RESULTS: The optimal configuration, which stacked Random Forest and XGBoost models using a Neural Network meta-learner, achieved 91.4% accuracy (95% CI: 89.8-92.8), AUC = 0.955, F1 = 0.801, and MCC = 0.746, outperforming the best individual model (AdaBoost, 90.2%). Training time (~450 s) and per-case inference time (<0.2 s) demonstrated computational feasibility. Feature-importance analysis and SHAP-based interpretation confirmed clinical relevance and interpretability. CONCLUSIONS: The hybrid feature-selection and diversity-guided stacking framework improves predictive accuracy and interpretability while maintaining computational efficiency. Although validated using COVID-19 mortality data, the approach is broadly applicable to biomedical, environmental, and engineering prediction tasks that require interpretable and scalable ensemble learning.
19 Apr 2026
Read appraisal →Medicine
Safety assessment of intravenous immunoglobulins in pediatric population: A systematic review of adverse events
BACKGROUND: Intravenous immunoglobulin (IVIG) is a therapeutic intervention utilized in various disorders due to its antimicrobial, antiinflammatory, and immunomodulatory properties. The market offers a range of IVIG products, leading to variations in efficacy and safety profiles. This study aimed to systematically evaluate the safety profile of IVIG administration in pediatric patients. METHODS: A systematic search was conducted on PubMed, Embase, and Cochrane databases using predetermined MeSH terms, covering the period from January 1, 2000, to October 15, 2022. Two independent reviewers performed relevance screening. Inclusion criteria encompassed English-language studies involving patients below 18 years of age who received IVIG treatment. Included studies were limited to peer-reviewed full-text publications with a minimum of 5 patients. A total of 8 studies met the inclusion criteria, encompassing 1088 pediatric patients. RESULTS: Eight studies met the inclusion criteria (total 1088 pediatric patients). Five studies reported patient-level adverse event data suitable for pooling (688 patients). Among these 688 patients, 267 (38.8%) experienced adverse events (AEs). Among reported AEs, the most frequent were headache 68/267 (25.5%) and fever 47/267 (17.6%). Less common AEs included nausea 12/267 (4.5%) and fatigue 12/267 (4.5%). Four studies recorded a total of 2960 IVIG infusions, with 12.6% of infusions accompanied by AEs. The predominant AEs during infusions were headache (54.3%) and fever (29.4%). Cough (0.8%) and nausea (1.6%) were the least commonly observed AEs. Other AEs such as rash, vomiting, and abdominal pain were also reported. Of the 23 full-text articles reviewed, 8 were included. The remaining 15 were excluded due to the following reasons: 6 for duplicate data, 4 for inappropriate study design, 3 for not meeting the minimum number of patients, and 2 for incomplete adverse event reporting. These reasons are now detailed in the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram. CONCLUSION: IVIG therapy demonstrates good tolerability in pediatric patients, with an overall favorable safety profile, aside from mild AEs associated with its administration. Serious AEs were infrequent and mainly observed in high-risk patients. Headache was the most prevalent AE, which seldom required hospitalization and could be effectively managed with acetaminophen. Other AEs were generally mild systemic reactions. Notably, AEs appear to be more frequent during the initial exposure to IVIG.
19 Apr 2026
Read appraisal →Archives of disease in childhood
How accurate is point-of-care ultrasound for detecting paediatric appendicitis? A systematic review and meta-analysis.
OBJECTIVES: Primary: To evaluate the diagnostic accuracy of point-of-care ultrasound (POCUS) in the diagnosis of appendicitis in paediatric patients presenting with suspected appendicitis.Secondary: To investigate how the use of POCUS affects length of stay in the paediatric emergency department and CT utilisation. METHODS: We searched MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials (CENTRAL) up until February 2025 for studies involving patients presenting to the paediatric emergency department with suspected appendicitis, who underwent POCUS. Studies were assessed for risk of bias using the Quality Assessment Tool for Diagnostic Accuracy Studies framework. The primary outcome of interest was the ability of POCUS to detect appendicitis in terms of sensitivity and specificity. RESULTS: Eight studies were included encompassing 993 patients. Calculated pooled sensitivity was 85.6% (95% CI 68.9% to 94.1%) with a specificity of 90.2% (95% CI 86.5% to 93.0%). There was a wide range of reported sensitivities for POCUS, ranging from 53% to 100%. There was less variation in reported specificities that ranged from 82% to 95.2%. The studies analysed were of variable quality with the reference standard and flow and timing being the main areas subject to bias. CONCLUSIONS: Based on the findings of this systematic review and meta-analysis, it is reasonable to conclude that POCUS can be used as an effective tool to diagnose paediatric appendicitis. With a moderately high sensitivity, it may not be recommended to be used to exclude appendicitis based on POCUS findings alone. Further prospective studies evaluating the reliability of POCUS in excluding appendicitis, either alone or when combined with a clinical prediction rule, are required. Secondary findings from this study demonstrate that POCUS may result in a reduced length of stay in the paediatric emergency department and lead to a reduction in CT utilisation, though insufficient data were available to draw firm conclusions in relation to this.
19 Apr 2026
Read appraisal →Inflammation research : official journal of the European Histamine Research Society ... [et al.]
The calcium-sensing receptor in sepsis and septic shock, mechanistic pathways and translational perspectives: a systematic review.
BACKGROUND: Sepsis and septic shock are major causes of mortality in critically ill patients. They are linked to widespread metabolic and immune dysregulation, including alterations in calcium and phosphate homeostasis. The calcium-sensing receptor (CaSR) not only plays a role in mineral balance, but also modulates key immune pathways and may contribute to the pathogenesis of sepsis. METHODS: A systematic literature review was conducted according to PRISMA 2020 guidelines. Four databases (PubMed, EMBASE, Cochrane Library, and Google Scholar) were searched for studies published since 1990. To be eligible for inclusion, articles had to be original research, reviews, or clinical trials involving adult models (mammalian or human). Full-text availability was required. Risk of bias was assessed for all studies included. RESULTS: Sixty-six articles met the inclusion criteria: 49 original studies and 17 reviews. No randomized controlled trials or meta-analyses were identified. Most studies relied on in vitro or in vivo models. CaSR was consistently reported to be upregulated or activated following exposure to bacterial and inflammatory stimuli in immune cells, including monocytes and lymphocytes. CaSR activation promotes proinflammatory cytokine release, notably IL-1β via the NOD like receptor family 3 (NLRP3) inflammasome although results in intestinal epithelial models remain inconsistent. In non-septic models, CaSR activation was associated with tissue and organ injury, including renal and cardiac damage, as well as vasoplegia related to endothelial dysfunction. Preclinical models of pneumonia and endotoxemia suggest that CaSR antagonists may effectively mitigate inflammation and organ injury. CONCLUSION: This systematic review identifies the CaSR as an amplifier of the host inflammatory response across both septic and non-septic preclinical models The lack of robust clinical data underscores the need for translational studies assessing CaSR expression or activity in patients with sepsis or septic shock, alongside in vivo validation of CaSR inhibition as a therapeutic strategy.
19 Apr 2026
Read appraisal →BMJ open
Patients' experiences of the path to sarcoma diagnosis: a qualitative systematic review and thematic synthesis
OBJECTIVE: To explore existing qualitative research on patients' experiences from the onset of symptoms to the diagnosis of sarcoma, with the purpose of identifying patient-perceived barriers at both patient and healthcare system levels and to highlight opportunities for improvement. DESIGN: Systematic review of qualitative studies using thematic synthesis. METHODS: We systematically searched Medline [Ovid], Embase [Ovid], PsycINFO [EBSCOhost] and CINAHL [EBSCOhost] from database inception to 11 April 2025 for qualitative studies reporting sarcoma patients' experiences during their diagnostic trajectory. The final search was conducted on 11 April 2025. All articles were screened against predefined inclusion and exclusion criteria and methodological quality was appraised using the Critical Appraisal Skills Programme qualitative checklist. Six studies conducted in Australia, the Netherlands and the UK were included. Data were analysed using a thematic synthesis approach guided by Thomas and Harden (2008). Confidence in the synthesised findings was assessed using the Grading of Recommendations Assessment, Development and Evaluation - Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERqual) approach. RESULTS: Four overarching themes emerged: patients' experience and interpretation of symptoms, diagnostic pathways, healthcare system factors, and reflections and recommendations. Key contributors to delayed diagnosis were symptom normalisation, misattribution by both patients and healthcare professionals, limited continuity of care and communication challenges. These findings were consistent across multiple countries, indicating relevance and validity in many settings. Patients emphasised the importance of disease awareness, persistence of patients and coordinated care. CONCLUSIONS: Diagnostic delays in sarcoma are influenced by both patient and healthcare systemic factors. Addressing these factors requires increased disease awareness among healthcare professionals and the public, improved coordination within the healthcare system, and targeted research to guide future interventions. This review provides cross-country insights into barriers to early sarcoma diagnosis, informing future priorities in clinical practice and research. PROSPERO REGISTRATION NUMBER: CRD420251030726.
18 Apr 2026
Read appraisal →PloS one
The association between RDW-to-platelet ratio and in-hospital mortality in critically ill stroke patients: A retrospective cohort study based on the eICU database
OBJECTIVE: The red cell distribution width-to-platelet ratio (RDW-to-platelet ratio, RPR) is a potential biomarker of inflammation and bone marrow function. This study aimed to investigate the association between RPR and in-hospital mortality in critically ill stroke patients in the eICU, and to evaluate mediating role of APACHE-IV score. METHOD: This retrospective cohort study utilized data from the 2014-2015 US multicenter eICU database. Of 200,859 initially included patients, 9,736 critically ill stroke patients were analyzed after excluding non-stroke cases and those with missing key variables. Multivariable logistic regression assessed the relationship between RPR and in-hospital mortality, adjusting for age, sex, comorbidities, and laboratory parameters, with multiple imputation for missing data. Subgroup analyses, mediation analysis, and restricted cubic spline modeling were performed. RESULTS: Patients with higher RPR exhibited significantly elevated in-hospital mortality (17.7% vs. 11.6%, P < 0.001). After adjustment, each unit increase in RPR was associated with 4.6% higher odds of death (adjusted OR=1.046, 95% CI: 1.032-1.061, P < 0.001), with consistent findings across subgroups. Restricted cubic spline analysis indicated a linear relationship. Mediation analysis showed that APACHE-IV score mediated 20.15% of the total effect (P < 0.001). CONCLUSIONS: Elevated RPR is independently associated in-hospital mortality in critically ill stroke patients, partly mediated by disease severity. As a simple and accessible marker, RPR shows promise for clinical prognostic risk stratification.
18 Apr 2026
Read appraisal →BMJ open
Prevalence of incidental findings requiring interventions in CT for trauma patients in the emergency department: a systematic review and meta-analysis
OBJECTIVE: To systematically review and meta-analyse the prevalence of incidental findings (IFs) requiring interventions identified in CT performed for patients with traumatic injuries in the emergency department, including pathologically confirmed cancers and emergent non-traumatic vascular pathologies. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE and Cochrane CENTRAL databases from database inception to 22 November 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Prospective or retrospective studies involving traumatic injury patients presented at the emergency department reporting IFs of clinical significance detected through CT with any interventions proposed were included. Studies that exclusively included paediatric populations were excluded. The systematic review methods included double-screening, dual assessment of eligibility and study validity, dual data extraction, Bayesian multivariate random-effects meta-analysis of prevalence and employing the Grading of Recommendations, Assessment, Development and Evaluations' rating for the certainty of evidence. The primary outcomes were IFs requiring any interventions, and subset IFs requiring urgent interventions. Secondary outcomes were histologically confirmed cancers and emergent non-traumatic vascular pathologies. RESULTS: 22 studies (1 with a prospective and 21 with a retrospective design) mainly from high-income countries using limited-quality data based on clinical practice involving 18 538 patients were included. 9 studies evaluated the whole body, while 13 evaluated selective body regions. The grading criteria for IFs were non-uniform, and the image interpreters involved had diverse experience and expertise. The summary prevalence estimates for IFs requiring any interventions, urgent interventions, pathologically confirmed cancers and emergent non-traumatic vascular pathologies detected in the whole body were 29.8% (95% credible interval (CrI) 20.4% to 42.9%; very low certainty), 7.6% (95% CrI 4.5% to 14.8%; low certainty), 0.6% (95% CrI 0.3% to 1.6%; moderate certainty) and 0.3% (95% CrI 0.1% to 0.9%; moderate certainty), respectively. These findings were largely identified in the chest or abdomen and pelvis, with the overall detection frequency reduced with the scanned body regions narrowed (very low to moderate certainty). Sparse data on the head, neck and spine resulted in limited results. CONCLUSIONS: IFs identified in trauma whole-body CT requiring intervention are prevalent and can lead to substantial medical costs. The widely reported prevalence range suggests variations in radiologist recommendations and reporting in clinical practice and calls for standardisations. IFs requiring urgent intervention are not rare, which leads to a diagnosis of significant diseases including cancers and urgent vascular pathologies. Future studies should report long-term, patient-relevant results based on standardised classification and reporting systems. PROSPERO REGISTRATION NUMBER: CRD42020187852.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Systolic blood pressure within 24 hours after successful reperfusion correlates with outcomes in acute ischemic stroke: a secondary analysis of the EHCHANTED2/MT Trial
BACKGROUND: The optimal threshold or range for systolic blood pressure (SBP) control in patients with successful reperfusion after endovascular thrombectomy for acute ischemic stroke (AIS) remains undefined. This study investigated whether SBP within the first 24 hours after successful reperfusion correlates with functional outcomes in AIS. METHODS: In this secondary analysis of the ENCHANTED2/MT trial, patients were categorized into two groups (120-140 mm Hg and 140-180 mm Hg, respectively) based on achieved SBP within 24 hours after randomization. The primary outcome was the modified Rankin Scale (mRS) score at 90 days. Secondary outcomes included neurological deterioration at 7 days, major disability (mRS score of 3-5 at 90 days), hospitalization duration, and health-related quality of life assessed by the three-level EuroQoL 5-Dimension Self-Report Questionnaire (EQ-5D-3L) at 90 days. Safety outcomes included early neurological decline (END), 90-day mortality, symptomatic intracranial hemorrhage (sICH), and any intracranial hemorrhage (ICH). Treatment effects were expressed as ORs with 95% confidence intervals (CIs). RESULTS: A total of 611 patients (363 in the 120-140 mm Hg group and 248 in the 140-180 mm Hg group) were included. The mean (SD) age was 67 (12) years and 37.8% were female. After adjusting for confounders, the 120-140 mm Hg group was significantly associated with better functional outcomes (mRS: 2 (IQR 1-4) vs 2 (IQR 1-5); adjusted OR 1.54 (95% CI 1.10 to 2.17), P=0.013). Compared with the 140-180 mm Hg group, the 120-140 mm Hg group had lower rates of neurological deterioration at 7 days (adjusted OR 0.68 (95% CI 0.47 to 0.98), P=0.037) and 90-day mortality (47 (13.0%) vs 53 (21.4%); adjusted OR 0.48 (95% CI 0.27 to 0.86), P=0.013). There were no significant differences between groups in END, major disability at 90 days, hospitalization duration, EQ-5D-3L score, sICH, or ICH (all P>0.05). CONCLUSIONS: In patients with successful reperfusion after endovascular thrombectomy, an average SBP within 24 hours of 120-140 mm Hg was associated with a greater likelihood of functional independence compared with 140-180 mm Hg.
18 Apr 2026
Read appraisal →Journal of global health
Associations between haemoglobin-to-red cell distribution width ratio and mortality in intracerebral haemorrhage: a population-based analysis of the MIMIC-IV database
BACKGROUND: Intracerebral haemorrhage (ICH) is a leading cause of mortality and morbidity worldwide. Identifying early prognostic biomarkers is crucial to optimise clinical management of critically ill patients with ICH. The haemoglobin-to-red cell distribution width ratio (HRR) has recently emerged as a potential predictor in various critical illnesses, but its role in ICH remains unclear. This study aimed to evaluate associations between HRR and mortality in patients with ICH. METHODS: This retrospective cohort study included the data of adults (≥18 years) extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (version 1). HRR was calculated from the first available haemoglobin and RDW measurements within one day of each other. Patients were categorised into HRR quartiles. The primary outcomes were 28-day and 1-year all-cause mortality. Associations were assessed using Cox proportional hazards models. RESULTS: The data of 1915 patients (mean age 67.5 years) were analysed retrospectively. After adjusting for possible confounders, compared with the lowest HRR quartile (Q1), patients in Q3 and Q4 had significantly lower 28-day mortality (Q3: adjusted hazard ratio (aHR) = 0.72; 95% CI = 0.54, 0.97; Q4: aHR = 0.67; 95% CI = 0.49, 0.90). Similarly, higher HRR quartiles were associated with reduced 1-year mortality risk (Q3: aHR = 0.64; 95% CI = 0.49, 0.84; Q4: aHR = 0.56; 95% CI = 0.42, 0.75). CONCLUSIONS: Lower HRR at admission is independently associated with higher short-term and long-term mortality in ICU patients with ICH. HRR may serve as a candidate prognostic biomarker for early risk stratification in this high-risk population. Further prospective studies are warranted to confirm these findings.
18 Apr 2026
Read appraisal →Emerging microbes & infections
Optimizing machine learning-based diagnosis of neurosyphilis in HIV-negative patients: a multicenter, real-world comparison of international diagnostic criteria
Neurosyphilis continues to rise globally, yet diagnosis remains challenging, often requiring multidisciplinary expertise and multiple CSF assays that are difficult to access in resource-limited settings. We developed and compared machine-learning (ML) models tailored to six international diagnostic guidelines and built a free, web-based tool for guideline-adapted decision support. We assembled 1,648 suspected neurosyphilis cases from four centers, using Guangzhou as the training cohort and Beijing, Xiamen (China) and Seattle (USA) as external validation cohorts. Five algorithms (Random Forest, AdaBoost, SVC, NuSVC, XGBoost) were trained with randomized search and three-fold cross-validation; performance was assessed by AUC, PRAUC, calibration, decision-curve net benefit, Brier score, and SHAP for model explainability. Across models and guidelines, neurological symptoms, CSF protein, and CSF white blood cell count consistently ranked as the strongest predictors. All models achieved excellent discrimination (AUC and PRAUC >0.90) with good calibration, reliability, and positive clinical utility, though performance varied modestly by guideline. These findings indicate that the optimal ML approach depends on the diagnostic definition applied. Our freely available online tool operationalizes these models to provide clinicians worldwide with context-adapted support aligned to local criteria.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Explainable machine learning model for predicting functional outcomes in posterior circulation stroke after thrombectomy
BACKGROUND: The early prediction of functional outcomes in patients with posterior circulation stroke (PCS) is crucial for timely interventions and optimizing treatment plans. We have developed and validated a machine learning (ML) model for predicting 3-month functional outcomes in patients with PCS undergoing endovascular thrombectomy (EVT). METHODS: The derivation cohort, consisting of 202 patients with PCS who underwent EVT at four medical centers from January 2020 to December 2023, was separated for training and internal validation, and an external dataset of 54 patients admitted from January 2020 to July 2023 was used for external validation. The target outcome was a good functional outcome, defined as a modified Rankin Scale score of 0-3 at 3 months. Seven ML models were trained using preoperative features, with the primary evaluation metric being the area under the receiver operating characteristic curve (AUC). The top performing model was further trained using intraoperative and postoperative features. Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS: The Random Forest model demonstrated the best discriminative ability among the models considered. After feature selection, the final preoperative model used seven features, achieving an AUC of 0.83 in the test set and 0.81 in the external validation cohort. The inclusion of intraoperative and postoperative features further enhanced the model's performance, resulting in an AUC of 0.84 and 0.90 in the test set and 0.83 and 0.90 in the external validation cohort, respectively. These models have been incorporated into a publicly accessible web-based calculator (https://zhelvyao-123-60-basilarz.streamlit.app). CONCLUSION: The interpretable ML models provide dynamic accurate predictions of functional outcomes in patients with PCS after EVT, offering valuable insights for personalized risk stratification and optimizing perioperative management, with potential for integration into clinical workflows.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Stroke thrombectomy with a novel reperfusion system including a 0.088'' aspiration catheter: the Imperative Trial
BACKGROUND: Mechanical thrombectomy is the standard of care for large vessel occlusion (LVO) stroke, yet most device trials focus on individual technologies rather than complete systems. OBJECTIVE: To evaluate the safety and efficacy of the Zoom Reperfusion System compared with performance goals derived from a meta-analysis of trials using established thrombectomy technologies. METHODS: Prospective, multicenter, non-randomized, investigational device exemption trial conducted across 26 United States centers from October 2021 to March 2024. Adults with anterior circulation LVO stroke within 8 hours of onset were treated using the Zoom System, which includes aspiration catheters ranging from 0.035″ to 0.071″, and a novel 0.088″ intracranial guide/aspiration catheter. The primary efficacy endpoint was successful reperfusion (modified Thrombolysis in Cerebral Infarction (mTICI) 2b-3) within three passes using Zoom-only components, adjudicated by an independent core laboratory. The primary safety endpoint was symptomatic intracranial hemorrhage (sICH). Secondary outcomes included final reperfusion, first-pass success, time to reperfusion, 90-day modified Rankin Scale (mRS), and mortality. RESULTS: Among 260 patients (median age 68 years; median National Institutes of Health Stroke Scale (NIHSS) 15), successful reperfusion within three passes was achieved in 83% (216/259; 95% CI: 78% to 89%), exceeding the performance goal (p<0.001). Final reperfusion was 92%, with 5.5% requiring rescue devices. First-pass mTICI 2b-3 success was 57%. Median puncture-to-reperfusion time was 19 min. At 90 days, 55% achieved mRS 0-2. The sICH rate was 1.9%, and 90-day mortality was 12.7%. CONCLUSIONS: The Zoom Reperfusion System demonstrated a safety and efficacy profile exceeding prespecified contemporary performance metrics with potential to significantly shorten reperfusion times and reduce the need for rescue therapy. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov--Identifier:NCT04129125.
17 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Transverse venous sinus stenting versus cerebrospinal fluid shunting in idiopathic intracranial hypertension: a multi-institutional and multinational database study
BACKGROUND: Cerebrospinal fluid (CSF) shunting and transverse venous sinus (TVS) stenting are promising treatment choices for idiopathic intracranial hypertension (IIH), addressing different aspects of IIH pathophysiology. However, large-scale comparative data remain limited. METHODS: We performed a multinational, multi-institutional, retrospective propensity score-matched analysis using the TriNetX platform. Adult patients diagnosed with IIH who underwent either TVS stenting or CSF shunting were included. The primary outcome was treatment failure, defined as the need for subsequent procedures (repeat TVS stenting, CSF shunt, or optic nerve fenestration). Secondary outcomes included residual symptoms (headache, visual disturbances, dizziness, and pulsatile tinnitus), ongoing use of IIH-related medications (furosemide, acetazolamide, and topiramate), and healthcare utilization, assessed through unplanned hospital readmissions and emergency department (ED) visits at 1 year follow-up. RESULTS: Of 134 530 IIH patients identified, 1362 underwent TVS stenting and 5278 underwent CSF shunting. After propensity score matching, patients who received TVS stenting had significantly lower odds of repeat interventions (9.6% vs 38.6%; OR 0.169, 95% CI 0.136 to 0.212, P<0.001). They also had significantly lower odds of residual headache, visual disturbances, and papilledema. However, stenting was associated with higher odds of persistent pulsatile tinnitus (6.7% vs 2.2%; OR 3.183, P<0.001). TVS stenting was associated with lower odds of unplanned inpatient readmissions (24.1% vs 40.5%; OR 0.466, P<0.001) and ED visits (22.0% vs 34.0%; OR 0.548, P<0.001). CONCLUSION: Both TVS stenting and CSF shunting resulted in symptomatic improvement and reduced medication use in patients with IIH. However, stenting was associated with significantly lower treatment failure rates, fewer residual papilledema, headaches and visual symptoms, and reduced healthcare utilization. Persistent pulsatile tinnitus was more common following stenting.
17 Apr 2026
Read appraisal →Sleep
Improving sailor's amount and efficiency of sleep in two-section watchkeeping schedules: A cluster randomized trial study
STUDY OBJECTIVES: The present study aimed at comparing sailors' amount and efficiency of sleep in two two-section watchkeeping schedules, i.e. the traditional 6/6 and the 7-5-5-7. Intention for the latter schedule was to align the major sleep episodes with biologically driven nocturnal sleep patterns. METHODS: Using a parallel crossover design with cluster randomized trials, forty-eight crew members from two vessels of the German Navy participated in a field trial with two 1-week underway periods varying the watchkeeping schedule in randomized order. Sleep minutes and sleep efficiency were assessed on the participant level using wrist-worn actigraphs and analyzed for sleep episodes occurring during the day (i.e. between 10:00 am and 10:00 pm) and during the night (i.e. between 10:00 pm and 10:00 am). RESULTS: Nighttime sleep duration was 14 min longer and with 10% higher sleep efficiency in the 7-5-5-7 schedule compared to the 6/6. In contrast, daytime sleep duration was 40 min longer in the 6/6 with no statistically significant differences in sleep efficiency between the two watchkeeping schedules. CONCLUSIONS: Our results suggest that the amount and efficiency of nighttime sleep can be enhanced with the 7-5-5-7. Hence, even in shift schedules with restricted periods for rest, recuperation can be improved by more closely aligning watchkeeping schedules to the innate human diurnal circadian cycles. Statement of Significance The size of ships and the associated limitations on the size of their crews make two-section watchkeeping schedules common in civil and military maritime settings. Such watchkeeping schedules-in which the 24-h day must be shared between two watches-limit time available for rest and recuperation. The present study shows that even in this context, slight modifications in two-section watchkeeping schedules lead to more and better nighttime sleep because the circadian fluctuations of sleep and performance are considered. Our research findings have high practical significance for all industries with shift workers and demonstrate how the negative effects of watchkeeping schedules can be reduced if they are adapted to better align with innate human circadian rhythms.
17 Apr 2026
Read appraisal →JMIR cardio
Large Language Models in Cardiology: Systematic Review
BACKGROUND: Large language models (LLMs) are increasingly used in health care, but their role in cardiology has not yet been systematically evaluated. OBJECTIVE: This review aimed to assess the applications, performance, and limitations of LLMs across diverse cardiology tasks, including chronic and progressive conditions, acute events, education, and diagnostic testing. METHODS: A systematic search was conducted in PubMed and Scopus for studies published up to April 14, 2024, using keywords related to LLMs and cardiology. Studies evaluating LLM outputs in cardiology-related tasks were included. Data were extracted across 5 predefined domains and the risk of bias was assessed using an adapted QUADAS-2 tool (developed by Whiting et al at the University of Bristol). The review protocol was registered in PROSPERO (CRD42024556397). RESULTS: A total of 33 studies contributed quantitative outcome data to a descriptive synthesis. Across chronic conditions, ChatGPT-3.5 (OpenAI) answered 91% (43/47) heart failure questions accurately, although readability often required college-level comprehension. In acute scenarios, Bing Chat omitted key myocardial infarction first aid steps in 25% (5/20) to 45% (9/20) of cases, while cardiac arrest information was rated highly (mean 4.3/5, SD 0.7) but written above recommended reading levels. In physician education tasks, ChatGPT-4 (OpenAI) demonstrated higher accuracy than ChatGPT-3.5, improving from 38% (33/88) to 66% (58/88). In patient education studies, ChatGPT-4 provided scientifically adequate explanations (5.0-6.0/7) comparable to hospital materials but at higher reading levels (11th vs 7th grade). In diagnostic testing, ChatGPT-4 interpreted 91% (36/40) electrocardiogram vignettes correctly, significantly better than emergency physicians (31/40, 77%; P< .001), but showed lower performance in echocardiography. CONCLUSIONS: LLMs show meaningful potential in cardiology, especially for education and electrocardiogram interpretation, but performance varies across clinical tasks. Limitations in emergency guidance and readability, as well as small in silico study designs, highlight the need for multimodal models and prospective validation.
17 Apr 2026
Read appraisal →Journal of the American College of Surgeons
Effect of Adhesion Barrier Use During Ladd Procedure for Intestinal Malrotation on Postoperative Midgut Volvulus and Postoperative Small-Bowel Obstruction: A Retrospective Cohort Study Using a National Inpatient Database
BACKGROUND: Postoperative midgut volvulus is a life-threatening complication after the Ladd procedure. An adhesion barrier to prevent postoperative small-bowel obstruction could increase postoperative midgut volvulus incidence due to reduced adhesions. We aimed to examine the effect of adhesion barrier use during Ladd procedures on postoperative midgut volvulus and postoperative small-bowel obstruction. STUDY DESIGN: We retrospectively identified patients younger than 18 years who underwent the Ladd procedure between July 2010 and March 2021, using a Japanese nationwide inpatient database. We used propensity score overlap-weighting analyses to compare reoperation for midgut volvulus, rehospitalization, and operation for small-bowel obstruction, mortality, total rehospitalization length, and total rehospitalization cost within 1 year after the Ladd procedure between patients in whom adhesion barrier was used (barrier group) and patients in whom it was not used (nonbarrier group). RESULTS: Of the 1,159 eligible patients, adhesion barrier was used in 314 and not used in 845 patients. The proportion of reoperations for midgut volvulus did not differ between the barrier and nonbarrier groups (3.5% vs 2.7%; risk difference 0.8%, 95% CI -1.9 to 3.5). The proportion of rehospitalization due to small-bowel obstruction was lower in the barrier group than in the nonbarrier group (2.6% vs 6.3%; risk difference -3.6%, 95% CI -6.6 to -0.8). No significant differences were observed in operation for small-bowel obstruction, mortality, and total length and cost of rehospitalization. CONCLUSIONS: The use of adhesion barrier during the Ladd procedure can prevent rehospitalization due to postoperative small-bowel obstruction without increasing the incidence of postoperative midgut volvulus.
17 Apr 2026
Read appraisal →Journal of global health
The impact of disasters on maternal mental health: a systematic review
BACKGROUND: The vulnerability of maternal mental health is increasing with the frequent increase in natural and man-made disasters, yet evidence remains fragmented. We aimed to synthesise quantitative evidence on the impact of disaster exposure on maternal mental health and related pregnancy outcomes. METHODS: We systematically searched 12 electronic databases, nine sub-databases, and additional web sources for the studies published between 1995 and 2025. We included studies that assessed the impact of disaster exposure during pregnancy and provided quantitative outcomes for depression, anxiety, stress, posttraumatic stress disorder (PTSD), or adverse pregnancy outcomes. RESULTS: Of 18 604 studies, 24 studies met the inclusion criteria. Natural disasters were the highest group (n = 16), including earthquakes (n = 8), floods/cyclone/hurricane (n = 7), and wildfire (n = 1), followed by man-made disaster (n = 7), including conflict/war/terrorism (n = 5), nuclear disaster (n = 1), plane crash (n = 1), and war (n = 1), and one hybrid disaster. Disaster showed a consistent increase in the risk of depression, anxiety, stress, and PTSD among pregnant women, with effects persisting up to six years post-disaster event. Across studies, disaster exposure increased maternal mental health problems by approximately 30% to over 200%, and PTSD odds increased seven to 10-fold among highly exposed pregnant women. Three studies reported that both depression and PTSD during pregnancy were associated with adverse pregnancy or early child outcomes, suggesting potential intergenerational effects. CONCLUSIONS: Disasters exert enduring and profound impacts on maternal mental health, with consequences cascading across both mother and child. Urgent, cost-effective and disaster-informed policies are needed to mitigate such long-term impacts. REGISTRATION: PROSPERO: CRD42023445559.
17 Apr 2026
Read appraisal →Acta orthopaedica
Intraoperative single-dose methadone significantly affects postoperative morphine consumption in older patients with a hip fracture: the MetaHip randomized controlled trial
BACKGROUND AND PURPOSE: Effective pain management in older patients with a hip fracture is critical for postoperative recovery. Our primary objective was to compare intraoperative methadone with placebo on postoperative morphine consumption over 72 hours. METHODS: Patients aged ≥ 60 years with hip fractures were randomized to receive methadone (0.10 mg/kg) or placebo intraoperatively. The primary outcome was postoperative morphine consumption measured in 24-hour intervals over 72 hours. Secondary outcomes included pain scores, time to mobilization, and discharge. Harms were assessed as adverse and serious adverse events. RESULTS: 129 patients were included. The primary endpoint analysis demonstrated that postoperative morphine consumption over 72 hours differed significantly between groups (likelihood-ratio test, P = 0.02). Model-based estimates suggested lower morphine consumption in the methadone group at 0-24 hours (least square mean [LSM] 7.1 [SE 1.2] vs placebo 10.1 [SE 1.7] mg) and at 24-48 hours (4.1 [SE 0.8] vs placebo 5.3 [SE 0.9] mg). At 48-72 hours, the model suggested lower morphine consumption in the placebo group (3.2 [SE 0.6] vs methadone 4.6 [SE 0.9] mg). Secondary outcomes were similar between groups, except that time to hospital discharge was longer in the methadone group (LSM 5.6 vs 4.5 days; mean difference -1.3 days, 95% confidence interval -2.3 to -o.4; P < 0.01). Harms appeared comparable, although the low event rate precluded formal analysis. CONCLUSION: A single intraoperative dose of methadone significantly alters postoperative morphine consumption over 72 hours after hip fracture surgery, without major safety concerns.
17 Apr 2026
Read appraisal →BMJ open respiratory research
Personalised management of community-acquired pneumonia and the role of Clinical Decision Support Software
Community-acquired pneumonia (CAP) is one of the most common causes of hospital admission and is associated with significant morbidity and mortality. National and international guidelines are available to guide the management of CAP, including antibiotic prescribing. However, these guidelines are often not adhered to and there is significant overprescribing of broad-spectrum antibiotics, contributing to the growing pandemic of antimicrobial resistance. Clinical Decision Support Software (CDSS) are electronic tools that use individual patient data to generate patient specific assessments or recommendations, that can then be acted on by the patient or clinical decision maker. This article reviews the evidence surrounding the initial management of CAP in hospital and considers the potential of CDSS to support CAP management.
17 Apr 2026
Read appraisal →Stem cell reports
Cellular immunotherapy for COVID-19-induced acute respiratory distress syndrome: Results of the CIRCA-19 phase 1 safety and phase 2 randomized controlled trials
The ability of immunomodulatory mesenchymal stromal cells (MSCs) to improve COVID-19-associated acute respiratory distress syndrome (ARDS) in clinical trials is uncertain. We assessed whether freshly cultured umbilical cord (UC)-derived MSCs improved outcomes in patients with severe COVID-19 ARDS. We enrolled 37 patients with severe COVID-19 ARDS: 15 in the phase 1 dose escalation and open label extension studies (NCT04400032), and 22 patients in the phase 2b randomized clinical trial (NCT04865107). Delivery of up to 270 × 106 MSCs in three divided daily doses was well tolerated and resulted in qualitative improvement in all clinical outcomes. Furthermore, MSCs resulted in resolution of lymphopenia, consistent with an important immunomodulatory effect, with significant improvement in patient reported quality-of-life measures (SF-36) at 6 months pointing to possible durable clinical effects. These findings suggest a potential benefit of freshly cultured, UC-MSCs in severe COVID-19 ARDS, associated with biological evidence of favorable immunomodulatory activity.
17 Apr 2026
Read appraisal →BMJ open
Association between ambient air pollution exposure levels and the risk of developing pulmonary embolism in the Chinese population: findings from the Chinese Cardiovascular Association (CCA) Database-Chest Pain Centre
OBJECTIVE: To analyse the associations between exposure to different air pollutants and the morbidity of pulmonary embolism (PE). DESIGN: Time series study with a distribution lag non-linear model. SETTING: 275 cities in China, 1 January 2015 to 31 March 2022. PARTICIPANTS: 27 369 cases of PE. MAIN OUTCOME MEASURES: The associations between exposure to different air pollutants and the morbidity of PE. RESULTS: During the study period, 27 369 cases of PE were recorded. PM2.5(Particulate Matter 2.5), PM10(Particulate Matter 10) and NO2(Nitrogen Dioxide) exposure levels were associated with an increased risk of developing PE in the single-pollutant model. PM2.5 exposure caused the greatest risk of developing PE at lag 1 in the single-day lag model, with a relative risk (RR) value of 1.033 (95% CI 1.007 to 1.058). PM10 and NO2 exposure had the greatest effects at lag 0, with RR values of 1.038 (95% CI 1.016 to 1.059) and 1.039 (95% CI 1.009 to 1.068), respectively. No associations were found between CO(Carbon Monoxide), O3(Ozone) or SO2(Sulfur Dioxide) exposure levels and the risk of developing PE. PM2.5, PM10 and NO2 exposure levels were most strongly associated with the development of PE at lag 01 in the cumulative lag model, with RR values of 1.052 (95% CI 1.009 to 1.095), 1.053 (95% CI 1.020 to 1.086) and 1.058 (95% CI 1.005 to 1.111), respectively. CONCLUSIONS: Short-term exposure to PM2.5, PM10 and NO2 can increase the risk of developing PE, especially in females. The effects of PM2.5 and PM10 exposure were more significant in cold seasons and in North China.
17 Apr 2026
Read appraisal →Scientific reports
Predicting post-stroke functional outcome using explainable machine learning and integrated data
Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated a diverse set of clinical characteristics and blood biomarkers with multiple machine learning models to predict 3-month functional outcome after AIS, and used explainable artificial intelligence to identify key drivers of performance. Models were trained on 506 patients aged 18-69 years with AIS enrolled at four stroke units in western Sweden. We compared extreme gradient boosting, multilayer perceptron (MLP), and L1- and L2-regularized logistic regression. Feature importance was assessed with Shapley additive global explanations. Of the 506 patients, 105 had an unfavorable outcome (modified Rankin Scale score > 2). All models showed high area under the curve (AUROC, 0.900-0.906). The MLP achieved the highest precision-recall performance (AUPRC, 0.773 ± 0.080) and sensitivity (0.655 ± 0.096), though with lower specificity (0.920 ± 0.035). Stroke severity (NIH Stroke Scale score) was the dominant predictor across models. Among biomarkers, brain-derived tau (BD-tau) was most informative, followed by inflammation-related plasma proteins. In conclusion, machine learning accurately predicted functional outcome after AIS. BD-tau and inflammation-related proteins contributed predictive information above stroke severity, suggesting a potential for blood biomarkers to enhance individualized prognostication after AIS.
17 Apr 2026
Read appraisal →Emerging microbes & infections
Cross-protection against highly pathogenic avian influenza H5N1 virus from seasonal influenza vaccines: a systematic review and meta-analysis of ferret studies
The recent surge in spillover events of highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b to humans and mammals in North America has raised urgent pandemic concerns. Human H5N1 vaccines are unavailable in most countries. We synthesized data from ferret challenge trials to evaluate whether widely available seasonal influenza vaccines confer cross-protection against lethal H5N1 infection. We systematically searched PubMed, Embase, and Web of Science for ferret studies of lethal H5N1 challenge published up to 5 July 2025 (PROSPERO #CRD42024520346). Random-effects meta-analyses were conducted to compare vaccine efficacy (VE) of seasonal influenza vaccines and H5N1 vaccines against H5N1-related mortality. Seroprotection was defined as a neutralizing antibody titre of ≥1:40. We identified 35 studies (157 trials). Seasonal influenza vaccines without N1 did not confer significant cross-protection (five trials; VE 14.8%, 95% CI -3.6 to 30.0). In contrast, VE was 73% for N1-containing seasonal influenza vaccines (19 trials; 95% CI 54-84) and 77% for H5N1 vaccines overall (133 trials; 95% CI 72-82) (p = 0.52). The VE of N1-containing seasonal influenza vaccines was modestly lower than that of H5N1 vaccines with seroprotection (88%; 66 trials; 95% CI 84-91; p = 0.009), but comparable to H5N1 vaccines that did not achieve seroprotection (63%; 67 trials; 95% CI 52-71; p = 0.29). The VE of seasonal influenza vaccines against H5N1 was robust across sensitivity analyses, with no evidence of publication bias (p = 0.99). Seasonal influenza vaccines significantly reduce H5N1-associated mortality in ferret trials, suggesting the cross-protection potential of currently available vaccines. Human studies are warranted.
16 Apr 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
Comparison of CATCH, PECARN and CHALICE rules in children with minor head trauma
PURPOSE: The aim of this study is to prospectively compare the diagnostic performance of the PECARN, CATCH, and CHALICE clinical decision rules in pediatric patients presenting with minor head trauma, within a rural secondary-level emergency department characterized by limited resources, high patient volume, and restricted access to specialist physicians. METHODS: The study was designed as a prospective observational study. The PECARN, CATCH, and CHALICE clinical decision rules were applied to the patients included in the study, and the data were recorded. The data included in the study were statistically analyzed using the SPSS (Statistical Package for Social Sciences; SPSS Inc., Chicago, IL) 27 program. A value of p < 0.05 was considered statistically significant. RESULTS: A total of 1,032 patients were included in the study. The PECARN did not miss any of the 15 patients with pathological brain CT and had 100% sensitivity and 52.1% specificity. CATCH failed to detect pathological brain CT in 1 patient and had 93.3% sensitivity and 53.3% specificity. CHALICE failed to detect pathological brain CT in 2 patients, with 86.7% sensitivity and 87.8% specificity. Overall, PECARN was more successful in detecting pathological brain CT, but it also had a high false-positive brain CT rate. CONCLUSION: PECARN criteria are more sensitive than other criteria in predicting brain CT pathology, but they lead to a large number of brain CT scans. While the CHALICE criteria are beneficial in reducing unnecessary brain CT scans, they may miss positive cases.
16 Apr 2026
Read appraisal →Neuroscience
Neural responses to acute hypoxia and hyperoxia
The brain's high metabolic demands make it highly sensitive to oxygen level changes, with both hypoxia and hyperoxia affecting cognitive and neural function. However, the underlying neural mechanisms remain unclear. This study aimed to characterise the effects of acute normobaric hypoxia and hyperoxia on neural function and perceptual thresholds using EEG, visual evoked potentials (VEPs), and critical flicker fusion (CFF) thresholds. In a single-blind, randomised, crossover design, thirty participants inhaled 10.5%, 21%, or 100% oxygen while resting-state EEG, VEPs, and CFF thresholds were recorded. Hypoxia reduced CFF thresholds in an exposure-dependent manner, while hyperoxia produced a smaller, transient reduction. Resting-state EEG revealed significantly increased power across delta, theta, and beta bands in response to hypoxia. Whereas hyperoxia induced smaller increases in theta and alpha and decreases in gamma power and a reduction in beta power over the sensorimotor area. Hypoxia increased spectral entropy, indicating impaired network synchronisation, while hyperoxia increased fuzzy entropy, particularly in eyes-closed states, potentially reflecting enhanced neural complexity. In VEPs, hypoxia reduced P1 amplitude and increased N2 deflections, reflecting disrupted early and late sensory processing, with sustained effects across blocks. Hyperoxia transiently increased P1 amplitude and showed minor effects on other components. These findings demonstrate the impact of altered cerebral oxygen on fundamental neural processing and the associated state- and time-dependent effects on brain function. This provides insight into the mechanisms underlying oxygen-induced neuromodulation and highlights the value of EEG and CFF as biomarkers for tracking neural responses to oxygen variability.
16 Apr 2026
Read appraisal →The New England journal of medicine
Asundexian for Secondary Stroke Prevention
BACKGROUND: Patients with noncardioembolic ischemic stroke or transient ischemic attack (TIA) are at risk for recurrent stroke. Low factor XI levels are associated with a reduced risk of ischemic stroke. Asundexian inhibits activated factor XI. Whether the addition of asundexian to antiplatelet therapy would be superior to antiplatelet therapy alone for the secondary prevention of ischemic stroke is unclear. METHODS: In this phase 3, double-blind trial, we randomly assigned patients within 72 hours after the onset of a noncardioembolic ischemic stroke or high-risk TIA to receive asundexian (50 mg once daily) or placebo, in addition to planned dual or single antiplatelet therapy. Patients had at least one of the following: a nonlacunar infarct on imaging, a history of atherosclerosis, or evidence of atherosclerotic plaque at any location on cerebrovascular imaging. The primary efficacy outcome was ischemic stroke. The composite of death from cardiovascular causes, myocardial infarction, or stroke was a key secondary outcome. The primary safety outcome was major bleeding. RESULTS: Among 12,327 patients who underwent randomization (6162 to the asundexian group and 6165 to the placebo group), the incidence of ischemic stroke was lower in the asundexian group than in the placebo group (6.2% vs. 8.4%; cause-specific hazard ratio, 0.74; 95% confidence interval [CI], 0.65 to 0.84; P<0.001). The incidence of the composite of death from cardiovascular causes, myocardial infarction, or stroke was lower in the asundexian group than in the placebo group. The incidence of major bleeding was similar in the asundexian group and the placebo group (1.9% and 1.7%, respectively; cause-specific hazard ratio, 1.10; 95% CI, 0.85 to 1.44). The incidence of adverse events was 69.3% in the asundexian group and 70.1% in the placebo group; the incidence of serious adverse events was 19.2% and 19.5%, respectively. CONCLUSIONS: Among patients with noncardioembolic ischemic stroke or high-risk TIA treated with antiplatelet therapy, asundexian at a daily dose of 50 mg resulted in lower risks of ischemic stroke and major cardiovascular events than placebo, without a higher risk of major bleeding. (Funded by Bayer; OCEANIC-STROKE ClinicalTrials.gov number, NCT05686070.).
16 Apr 2026
Read appraisal →Global health action
Impact of climate change on pediatric health outcomes
Climate change has become one of the most critical health issues globally in the twenty-first century with children bearing the disproportionate burden of the burden since they are more vulnerable than adults because of their physiological, behavioral, and developmental capacities. It is a systematic review that rates the evidence of the relationship between climatic exposures such as heat, air-pollution, and extreme weather events and pediatric health outcomes. The number of peer-reviewed studies involved was 23 published in 2000-2025, which represented different geographic areas and study designs and assessed acute and chronic health outcomes. The Newcastle-Ottawa Scale and the ROBINS-I tool were used to evaluate the methodological quality, and the majority of the studies had low to moderate risks of bias. The narrative synthesis shows that there are always links between air pollutants especially PM2.5, NO2 and O3 and respiratory morbidity, prevalence of asthma and hospitalization of children. Amplified temperatures as well as heat waves were associated with increased cases of heat illness, dehydration, and febrile state in infants and young children. There were elevated cases of diarrheal and vector-related infections, especially in low-resource settings, which were linked to extreme weather events especially floods. Although the overall results were similar, significant differences in the regions and methods were found, and low-income countries show little evidence. In addition, exposures as analyzed in most studies were usually considered individually, which may have underestimated the cumulative or compound climate risks. Main findings: This review shows that rising temperatures, air pollutants, and extreme weather events are consistently linked with higher rates of respiratory illness, heat-related illness, and infectious diseases among children.Added knowledge: The synthesis brings together two decades of evidence showing that children across multiple continents experience similar health vulnerabilities when exposed to climate-related environmental conditions.Global health impact for policy and action: The findings highlight an urgent need for stronger climate-responsive child health policies and early warning systems to protect children from worsening environmental threats worldwide.
16 Apr 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Effects of race distance and probiotics intervention on kidney, muscle, and gut injury and inflammation biomarker responses during running.
INTRODUCTION: Prolonged intense physical activity, such as long-distance running, may lead to systemic inflammation and cause organ injury, particularly to the kidneys. This study aimed to assess the impact of trail race running on kidney and muscle injury and gut inflammation biomarkers, and the potential mitigating effects of Lactiplantibacillus plantarum 299v (Lp299v) supplementation. METHODS: This randomized, double-blind, placebo-controlled study included 34 participants who completed 42 races, ranging from 20 to 164 km. Participants were divided into two groups, receiving either 40 bn CFU Lp299v or a placebo for 4 weeks before the race. Urine and fecal samples were collected pre- and post-race (immediately after, morning after and 24 h after the race) to measure biomarkers of muscle and kidney injury, and gut inflammation. Principal component analysis was used to create a single tubular kidney injury biomarker component variable (TKIBC1) positively associated with five separate tubular kidney injury biomarkers (MCP-1, KIM-1, GST-π, clusterin, and calbindin) at the three post-race time points. RESULTS: Running led to increased tubular and glomerular kidney injury markers, increased levels of fecal calprotectin, and, in some cases, elevated urine myoglobin levels. These effects were more pronounced in races ≥107 km (ultradistance). While Lp299v supplementation did not significantly influence TKIBC1, it was associated with a protective effect against gut inflammation. CONCLUSION: These findings suggest that prolonged intense exercise induces kidney and muscle injury as well as gut inflammation, with more severe effects observed in ultra-distance running. Lp299v may have some protective effects, particularly against gut inflammation, but further studies are needed to confirm these findings and explore the underlying mechanisms linking gut health and kidney injury during extreme physical exertion.
16 Apr 2026
Read appraisal →Journal of investigational allergology & clinical immunology
Future Perspectives for Artificial Intelligence in Allergy: Advances in Diagnosis, Monitoring, and Personalized Care
Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.
16 Apr 2026
Read appraisal →The American journal of cardiology
FFR-Guided Complete versus Culprit-Only Revascularization in Patients With Myocardial Infarction: A Systematic Review and Meta-Analysis
In patients with myocardial infarctions (MI) and multivessel coronary artery disease (CAD), the effect of fractional flow-reserve (FFR)-guided complete revascularization during index hospitalization versus culprit-only revascularization remains unclear. Our objective is to determine whether FFR-guided complete revascularization during index hospitalization reduces major adverse cardiac events (MACE) among patients with MI and multivessel CAD. We systematically searched MEDLINE, EMBASE, and the Cochrane Library for randomized controlled trials (RCTs) comparing FFR-guided complete versus culprit-only revascularization in patients with MI and multivessel CAD. The primary outcome was MACE, as defined by a composite endpoint of all-cause death, MI or unplanned revascularization, at a minimum 1-year follow-up. Count data were pooled across trials using random-effects models to estimate risk ratios (RRs) and 95% confidence intervals (CIs). A total of 3 RCTs (n = 3,054) were included. The majority (77.5%) of participants were male. The pooled RR of MACE for FFR-guide complete versus culprit-only revascularization was 0.63 (95% CI: 0.37-1.05; I2 = 90%). FFR-guided complete revascularization was associated with reduced unplanned revascularization events, with a pooled RR 0.43 (95% CI: 0.21-0.87; I2 = 88%). There was no significant difference in the risk of recurrent MI (RR: 0.9; 95% CI: 0.61-1.33; I2 =30%). The risk of major bleeding was low in both FFR-guided complete (23/1373) and culprit-only (29/1681) revascularization groups across the 3 trials. In conclusion, in patients with MI and multivessel CAD, FFR-guided complete revascularization during the index hospitalization reduces the risk of unplanned revascularizations and may reduce the risk of MACE compared to culprit-only revascularization.
16 Apr 2026
Read appraisal →Journal of affective disorders
Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients
BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains challenging, particularly in developing regions. Leveraging routinely collected clinical biomarkers may offer a scalable solution for timely risk detection. METHOD: This multicenter study utilized two independent prospective trauma cohorts to develop and externally validate machine learning models for predicting PTSD at 1, 3, and 6 months post-trauma. The development cohort consecutively enrolled 382 hospitalized trauma patients (Nov 2023-Dec 2024), while the external validation cohort included 204 patients (Oct 2021-Nov 2022). Predictors comprised psychosocial variables and routine clinical biochemical markers, including inflammatory indicators, liver and renal function markers, and hematological parameters. Five models were constructed (Logistic Regression, Random Forest, SVM, XGBoost, and Neural Network), with performance evaluated by AUC and feature importance interpreted via SHAP. RESULTS: Support Vector Machine achieved the best performance at 1 month (AUC = 0.84), while Random Forest models performed best at 3 and 6 months (AUC = 0.78 and 0.87). SHAP analysis indicated that ASDS were the most important predictors at all time points, with feature importance shifting over time. Acute stress and psychological state were dominant at 1 month, fearful experiences with physiological responses at 3 months, and chronic psychological burden and emotional state at 6 months. CONCLUSIONS: PTSD can be reliably predicted using psychosocial data and routine clinical biochemical markers. These findings support the integration of clinically accessible biological data into early PTSD risk screening, particularly in resource-limited settings.
15 Apr 2026
Read appraisal →International journal of medical informatics
Clinician preferences for explainable AI in critical care: a comparative study of interpretable models and visualizations for intubation decision support
BACKGROUND: The complexity of many AI models hinders their clinical adoption because the clinicians using them do not regard them as transparent. This study addresses the lack of clinician-centered explainable AI (XAI) interfaces by designing and evaluating intuitive visual explanations for intubation prediction, testing the hypothesis that workflow-compatible designs enhance acceptance. OBJECTIVE: This study compares three, time-aware, visual explanations for XAI-based intubation prediction and evaluate their acceptance, comprehension, and perceived utility among clinicians. METHODS: We developed machine learning models to estimate the near-term risk of deterioration in the patient's condition which may lead to mechanical intubation using ICU time-series data. We generated global and local explanations using SHAP and designed three customized visual formats-a temporal force plot, a temporal bar chart, and a dual-encoded SHAP heatmap. Clinicians (n = 206) evaluated comprehension and usability using objective questions and a Likert-based survey. RESULTS: Based on 4608 critically ill patients with 10 medical variables over 7 hours of data for each patient, the Random Forest (RF) model achieved the highest area under the curve (AUC): 0.94. Furthermore, the local explanations were customized and evaluated by 206 clinicians through a survey conducted on the Prolific platform. A customized heatmap representation was selected as the visualization with the highest perceived clinical utility and alignment with clinical workflows. DISCUSSION: The reported findings support the need for explanation formats to be tailored to clinical reasoning and task context, supporting the concept of cognitive fit. The heatmap's close alignment with clinicians' mental models and its graphical integrity enhances interpretability and trust. This study demonstrates that explanation effectiveness depends on contextual relevance, rather than a universal standard, and that the presentation format itself significantly shapes clinicians' trust in XAI systems. CONCLUSION: This study advances clinical XAI by introducing a time-aware explanation framework for ICU intubation decisions. By integrating temporal trends with model reasoning, our visualizations closely align with clinicians' cognitive workflows. Rigorous clinician-centered evaluation identified the dual-encoded SHAP heatmap as the most useful and workflow-compatible visualization, highlighting the importance of explanation design alongside predictive accuracy for clinical adoption.
15 Apr 2026
Read appraisal →Neurology
Outcomes After Minor Ischemic Stroke in Older Patients Treated With IV Thrombolysis vs Standard of Care in the TEMPO-2 Trial
BACKGROUND AND OBJECTIVES: There are few high-quality data on thrombolysis outcomes in the oldest patients, especially for minor stroke. We examined safety and efficacy outcomes of thrombolysis in patients older than 80 years, compared with those ≤80 years, in the Tenecteplase Versus Standard of care for Minor Ischaemic Stroke With Proven Occlusion (TEMPO-2) trial. METHODS: In this post hoc analysis of the TEMPO-2 randomized controlled trial, we compared outcomes and adverse events in patients with minor stroke and symptomatic intracranial occlusion or focal perfusion lesion within 12 hours of symptom onset, assigned to tenecteplase vs nonthrombolytic standard of care, in those >80 years and ≤80 years. The primary outcome was responder analysis of the 90-day modified Rankin Scale (responder was mRS = 0-1 if premorbid mRS = 0-1, 0-2 if premorbid mRS = 2). Secondary outcomes included neurologic recovery according to the NIH Stroke Scale (NIHSS) at 5 days/discharge, vessel recanalization, Lawton Instrumental Activities of Daily Living Scale, EuroQol-5 Dimension (EQ-5D), and adverse events. We used mixed-effects Poisson, ordinal logistic, and quantile regression as appropriate, adjusted for sex, baseline NIHSS, and treatment assignment as fixed-effects and enrolling site as random effects. RESULTS: Among 884 patients in the intention-to-treat analysis (208 [23.5%] >80 years, 365 [41.6%] female). Significant interactions between age and treatment were observed for mRS responder, mRS 0-1, return to baseline function, EQ-5D index, and vessel recanalization. Patients >80 years generally fared worse with tenecteplase for mRS outcomes (mRS 0-1 in 49 [46.2%] vs 61 [59.8%] with control, adjusted risk ratio [aRR] 0.83, 95% CI 0.72-0.97), while among patients ≤80 years, there was no significant difference (249 [76.4%] vs 260 [74.7%], aRR 1.01, 95% CI 0.98-1.03). However, in both age groups, patients were more likely to achieve recanalization of visible occlusions (aRR ≤80 years 2.06, 95% CI 1.60-2.65, >80 years 2.77, 95% CI 2.21-3.47) and NIHSS = 0 at 5 days/discharge when assigned to tenecteplase (aRR ≤80 years 1.14, 95% CI 1.04-1.26, >80 years 1.21, 95% CI 1.10-1.33). Serious adverse events (SAEs) were more frequent with tenecteplase among patients >80 years (risk ratio 2.29, 95% CI 1.27-4.13), particularly hemorrhages and stroke progression/recurrence (any extra/intracranial hemorrhage in 5 [4.7%] vs 0 with standard of care, p = 0.026). DISCUSSION: Older patients (>80 years) with minor stroke and visible occlusion or perfusion lesion assigned to tenecteplase were more likely to achieve recanalization of occlusions and short-term neurologic recovery, as were younger patients, but they experienced worse 90-day outcomes with more frequent hemorrhagic and stroke progression/recurrence-related SAEs. Overall, these results, while post hoc, do not support thrombolysis for minor strokes in older patients. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT02398656. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients >80 years with minor stroke and visible occlusion or perfusion lesion, IV thrombolysis with tenecteplase is associated with worse functional outcomes at 90 days compared with nonthrombolytic standard of care therapies.
15 Apr 2026
Read appraisal →Journal of medical Internet research
Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study
BACKGROUND: Emergency radiology requires highly accurate reporting under time constraints; yet, increasing workloads raise the risk of errors. While large language models (LLMs) show potential for proofreading in general radiology, their performance in emergency settings and non-English contexts remains unclear. OBJECTIVE: We aim to evaluate the performance of a domain-optimized LLM, DeepSeek-R1, for identifying errors in Chinese emergency radiology reports, with comparison against assessments by board-certified radiologists. METHODS: We compiled 7435 emergency reports (dataset 1; radiography, computed tomography, and magnetic resonance imaging) collected from November 2024 to April 2025. In stage 1, a total of 5 LLMs were evaluated using 200 reports. The best model, DeepSeek-R1, proceeded to stages 2 and 3, where 0-shot and few-shot learning were tested on a separate set (n=100). Model performance was compared against 12 radiologists. Stage 4 validated real-world utility on 800 verified reports. RESULTS: In subdataset 1, under stress-testing conditions, DeepSeek-R1 achieved a higher error detection rate in the few-shot setting than in the 0-shot setting (84.4% vs 60.9%, P=.003). Its performance exceeded that of radiology residents (84.4% vs 51.6% and 53.1%, respectively; both P<.05) and showed no statistically significant difference compared with senior radiologists and attending radiologists (84.4% vs 68.8%-93.8%, P=.26 to ≥.99). Compared with residents, DeepSeek-R1 detected more critical omissions (100% vs 25% and 50%; both P<.05) and other errors (92% vs 33% and 33%; both P=.02). In dataset 2, collected from independent institutions, DeepSeek-R1 achieved a detection rate of 95% under the few-shot setting. Reading time was shorter than that of human readers (92 vs 109 s). In real-world validation, DeepSeek-R1 identified 117 true reporting errors, yielding a positive predictive value of 56.5%. CONCLUSIONS: DeepSeek-R1 holds promise for improving quality control in emergency radiology reports. Its performance and efficiency support its use as an assistive proofreading tool in real-world radiology workflows.
15 Apr 2026
Read appraisal →ChemMedChem
Structure- and Ligand-Based Discovery of Novel 3-Chymotrypsin-Like Protease Nonpeptidomimetic Hits
The SARS-CoV-2 3-chymotrypsin-like (3CLpro) protease is a key target for the development of COVID-19 therapeutics. While ensitrelvir and nirmatrelvir are approved drugs for treatment, the continuous research and development for new antiviral drugs is necessary to combat the emergence of variants and other related viruses. This study employed structure- and ligand-based computer-assisted approaches to identify new 3CLpro nonpeptidomimetic inhibitors. Using data from COVID Moonshot, NCATS, and the literature, computational methods such as shape-based, ensemble docking, and machine learning (ML) techniques were developed, achieving robust validation metrics: AUC = 87%, EF = 7, BEDROC = 60% for shape-based; AUC = 87%, EF = 7.03, BEDROC = 62% for ensemble docking, and ACC = 81%, MCC = 62% for ML models, combing Random forest + ECFP4 fingerprint. These models were utilized in virtual screening (VS) campaigns using the H3D and ChemBridge libraries, from which six promising hits with IC50 values ≤80 µM were identified, including LabMol-499 with an IC50 of 13.71 µM and a Ki of 21.74 µM. Moreover, we found that LabMol-499 acts as a noncompetitive, reversible inhibitor of 3CLpro. These findings provide a foundation for hit-to-lead optimization of new nonpeptidomimetic 3CLpro inhibitors.
15 Apr 2026
Read appraisal →Neurology
Systolic Blood Pressure Trajectory and Outcomes in Acute Intracerebral Hemorrhage: Pooled Analysis of the 4 INTERACT and ATACH-II Clinical Trials
BACKGROUND AND OBJECTIVES: While moderate and rapid systolic blood pressure (SBP) lowering was associated with better functional outcomes after intracerebral hemorrhage (ICH), large reductions in SBP within 1 hour, for example, reductions from >200 to <140 mm Hg, diminished or even reversed these beneficial effects. We aimed to define the optimal trajectory of SBP control in relation to favorable functional outcomes after acute ICH. METHODS: We conducted a pooled analysis of individual patient-level data from all 4 Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trial (INTERACT) and second Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-II) trials, which were international, randomized, open-label, blinded, end point-assessed, controlled trials that determined the effectiveness of early intensive SBP control in acute ICH. Latent class analysis grouped SBP trajectories over the first 24 hours (9 measurements) into defined clusters. The primary outcome was functional recovery at 90 days after randomization, defined as modified Rankin Scale (mRS) scores of 3-6. Logistic regression models with adjustment for baseline covariates and trial were used to determine associations between SBP cluster trajectories and outcomes in INTERACT, with validation in ATACH-II. RESULTS: A total of 11,269 patients (INTERACT n = 10,269; ATACH-II n = 1,000; mean age 62.4 years; female 36.4%) with at least 1 postrandomization SBP reading were included. Six SBP trajectories were identified: low, moderate-to-low, moderate, high, high-to-moderate, and high-to-low. Compared with the low SBP group, associations with poor functional outcome (mRS scores 3-6) increased progressively across other groups in INTERACT (p = 0.04 for trend). Adjusted odds ratios (95% CI) for groups 2 to 6 were 1.16 (0.98-1.37), 1.44 (1.18-1.75), 1.46 (1.15-1.87), 1.90 (1.32-2.73), and 1.28 (1.02-1.60), respectively. A similar albeit nonsignificant trend was observed in ATACH-II due to limited power. DISCUSSION: Distinct SBP trajectories over 24 hours defined prognosis after ICH, with a severe hypertensive group having the highest odds of death or disability, regardless of the BP-lowering strategy used. These findings highlight the importance of well-controlled but tailored SBP management strategies after ICH. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that distinct SBP trajectories over 24 hours are associated with prognosis after acute ICH, with a severe hypertensive group having the highest odds of poor functional outcome, regardless of the BP-lowering strategy used. TRIAL REGISTRATION INFORMATION: (INTERACT1 NCT00226096; INTERACT2 NCT00716079; INTERACT3 NCT03209258; INTERACT4 NCT03790800; ATACH-2 NCT01176565).
15 Apr 2026
Read appraisal →JAMA
Restrictive vs Liberal Physical Restraint Strategies in Critically Ill Patients: The R2D2-ICU Randomized Clinical Trial
IMPORTANCE: The effect of wrist-strap physical restraints on outcomes in patients receiving mechanical ventilation in the intensive care unit (ICU) remains uncertain. OBJECTIVE: To investigate the effect of a low-use wrist-strap physical restraint strategy in critically ill patients receiving invasive mechanical ventilation. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial conducted across 10 ICUs in France. Between January 5, 2021, and January 2, 2024, 405 adult patients who had initiated invasive mechanical ventilation within the previous 6 hours and were expected to require ventilation for at least 48 hours were enrolled. Follow-up was completed on May 17, 2024. Statistical analysis was conducted from June 1, 2025, to December 15, 2025. INTERVENTIONS: Patients were randomized to undergo either a restrictive, low-use physical restraint strategy (wrist straps avoided unless necessary because of severe agitation, defined as a Richmond Agitation-Sedation Scale score of ≥3 [on a scale from -5 (unresponsive) to 4 (combative)]; n = 201) or a liberal, high-use strategy (wrist straps applied systematically and reassessed daily; n = 204). Discontinuation of restraints was allowed in patients who were awake or extubated without delirium (measured via the Confusion Assessment Method for the ICU). MAIN OUTCOMES AND MEASURES: The primary outcome was the number of days alive without coma or delirium during the first 14 days after randomization. Secondary outcomes included incidence of self-extubation and day-90 mortality. RESULTS: Among 396 patients with available primary outcome data, the median (IQR) age was 65 (56-73) years, 245 (62%) were male, and the median (IQR) Sequential Organ Failure Assessment score was 7 (4-10). The mean days alive without coma or delirium were 6.67 days (95% CI, 5.69-7.65) in the low-use strategy group and 6.30 days (95% CI, 5.35-7.24) in the high-use strategy group (adjusted mean difference, 0.37 days [95% CI, -0.71 to 1.46]; P = .51). Self-extubation occurred in 18 patients (9.2%) in the low-use strategy group and 17 (8.5%) in the high-use strategy group, and day-90 mortality was 37.2% and 41.0%, respectively. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, among adult patients receiving mechanical ventilation in the ICU, a low-use wrist-strap physical restraint strategy compared with a high-use strategy did not reduce days free of delirium or coma at 14 days. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04273360.
15 Apr 2026
Read appraisal →Scientific reports
Region guided mask R-CNN with Haralick ResNet fusion for accurate coronary artery disease detection in computed tomography angiography images
Coronary Computed Tomography Angiography (CCTA) is a non-invasive imaging technique used to visualize the coronary arteries and diagnose coronary artery disease. It provides detailed 3D images which helps in identifying blockages, stenosis and plaques. However, CCTA images often suffer from noise, low contrast, and motion artifacts, which complicate accurate segmentation and analysis. The proposed work addresses the challenges in detecting and classifying coronary artery disease (CAD) from CCTA images. For accurate coronary artery segmentation, Region-Guided Mask R-CNN (RG-Mask R-CNN) combines region growing for initial region identification with Mask R-CNN for precise instance segmentation. This hybrid approach reduces the complexity of the structures in CCTA images and provides better segmentation accuracy. For feature extraction, Haralick-ResNet Fusion (HRF) captures the texture information using Haralick features and ResNet for high-level feature extraction to handle the difficulty of subtle variation differentiation in CCTA images. Finally, a Deep Convolutional Network (DeepConvNet) combines deep learning and convolutional layers to classify CAD efficiently based on the features extracted. This integrated framework offers an accurate solution (98.3%) for CAD detection from CCTA images by overcoming common issues such as noise and complex arterial structures, while improving diagnostic performance.
15 Apr 2026
Read appraisal →BMJ health & care informatics
Supporting street medicine through medical informatics: a manifesto on research needs
Homeless individuals face major barriers in accessing regular healthcare, leading to the development of street medicine as a distinct humanitarian field. To enhance continuity and quality of care, consistent documentation is crucial. However, electronic health records are rarely used in European street medicine. An interdisciplinary European workshop identified four key research fields for medical informatics in street medicine: (1) Research ethics: The creation of ethical guidelines that promote co-creation and active communication with patients. (2) System development: Designing a secure, anonymous and European Health Data Space-compliant personal Electronic Health and Social Record (pEHSR), establishing standardised health and social care datasets, and implementing dynamic consent management methods. (3) Education and training: Developing targeted programmes to improve digital and health literacy among street medicine clients and training professionals in the effective use of the pEHSR system. (4) Management and evaluation: Creating management structures and evaluation frameworks suited to the unique challenges of street medicine.This manifesto calls for internationally coordinated scientific efforts to build effective, integrated digital health solutions for vulnerable populations. It encourages researchers and practitioners from medical informatics and related fields to engage with these priorities and support innovation that advances equitable healthcare access.
15 Apr 2026
Read appraisal →BMJ open
Challenging history taking encounters: a systematic review, meta-analysis and phenomenological framework
OBJECTIVES: All physicians will experience challenging history taking encounters, where communication is impaired and negatively impacts the diagnostic process. The aims of this systematic review were to (1) undertake a meta-analysis of the frequency of challenging encounters; (2) collate adverse outcomes of challenging encounters; (3) identify underlying causes of challenging encounters; (4) identify strategies to deal with different challenges; and (5) align these strategies with our published phenomenological framework of history taking challenges. DESIGN: This was a systematic review and meta-analysis of prevalence data adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses and the Meta-analyses of Observational Studies in Epidemiology guidelines. DATA SOURCES: A literature search in MEDLINE, Embase and Cochrane databases was performed on 12 July 2020, and updated on 4 August 2025, focusing on challenging history taking encounters in any clinical setting. ELIGIBILITY CRITERIA: Articles reporting on the frequency, adverse outcomes, causative factors or strategies used to address challenges in the history taking process in any clinical area of medicine. DATA EXTRACTION AND SYNTHESIS: Factors associated with challenging history encounters (causative or consequential) were categorised using inductive coding and referenced to a phenomenological framework. Meta-analysis was used to estimate the prevalence of history taking encounters using a restricted maximum likelihood model with τ 2 and I 2 as tests for heterogeneity and funnel plot with Egger's test for publication bias. RESULTS: 73 articles were included in the analysis. The overall prevalence of challenging history taking encounters was 19.5% (95% CI 14.2% to 24.7%). Adverse outcomes of patient dissatisfaction (level 1 evidence) and diagnostic uncertainty (level 3 evidence) were identified. Factors associated with (n=22) and strategies to mitigate challenging encounters (n=13) were categorised. Correlation of factors and strategies with a phenomenological approach created a framework to assist novice history takers in approaching such circumstances. CONCLUSIONS: Challenging history taking encounters are common. Little is known of the relative importance of factors associated with challenging history taking encounters or the impact of suggested strategies. Many of the suggested strategies to facilitate meaningful communication in these situations involve a departure from standard history taking. More research is required to better define the nature of challenges encountered in history taking with a view to develop better educational models for trainee physicians.
15 Apr 2026
Read appraisal →Disaster medicine and public health preparedness
The Beirut Pager Explosions of 2024: A Systematic Review of an Unprecedented Mass Casualty Event
OBJECTIVE: In September 2024, Lebanon experienced an unprecedented mass casualty incident involving the simultaneous detonation of thousands of weaponized pager devices. This systematic review characterizes the resulting injury patterns, surgical burden, and disaster-response lessons to help inform future preparedness and response strategies. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies on casualties from the pager explosions. A random-effects meta-analysis estimated pooled prevalence of injuries by body region. RESULTS: Sixteen articles met inclusion criteria overall, comprising seven studies in the clinical synthesis and nine articles in the qualitative disaster-response synthesis. A consistent injury pattern predominantly affected the hands, face, and eyes. Specialty-specific cohorts demonstrated severe ocular destruction, frequent hand amputation, and a substantial operative burden. In the two comparable hospital-based cohorts eligible for meta-analysis, upper extremity injuries were most common (pooled prevalence 84%; 95% CI: 73-91%; I2 = 0%). The incident exposed critical weaknesses in triage, communication, and specialty surge capacity. CONCLUSION: The 2024 pager explosions produced a novel injury signature distinct from conventional blast trauma. These findings, while based on limited case series, underscore the urgent need for health care systems to develop flexible, adaptive disaster plans that can respond to the unique threats of unconventional warfare.
14 Apr 2026
Read appraisal →Blood advances
Neutrophil CD14 is a driver and a therapeutic target for deep vein thrombosis.
Neutrophil-mediated persistent inflammation and neutrophil extracellular trap formation (NETosis) are critical in the pathogenesis of deep vein thrombosis (DVT). Identifying the mechanisms controlling these proinflammatory and prothrombotic functions is critical for designing and developing new therapeutics for DVT. However, how neutrophils acquire these phenotypes during the early stages of DVT remains poorly understood. Here, multiomic analyses (RNA sequencing, proteomics, and flow cytometry) of bone-marrow neutrophils 3 hours after DVT, revealed consistent overexpression of CD14. Concurrently, mice with DVT exhibited significantly elevated plasma granulocyte colony-stimulating factor (G-CSF) and neutrophil hyperactivation. Mechanistic studies using a geometric deep learning model (DeepPBS) and chromatin immunoprecipitation revealed that elevated G-CSF drives CD14 overexpression by upregulating the transcription factor C/EBPα (leucine zipper CCAAT-enhancer binding protein α). Importantly, neutrophil-specific CD14 knockdown using a novel lentiviral short hairpin RNA approach significantly improved DVT outcomes by lowering thrombus burden, thrombosis incidence, and intrathrombus neutrophil and citrullinated histone H3 accumulation. Studies in G-CSF stimulated primary human neutrophils revealed that CD14 inhibition reduces markers of inflammation and NETosis (activated gasdermin D, citrullinated histone H3, and S100A8/A9), while increasing apoptosis, thus demonstrating translational relevance. Collectively, our study uncovers an essential role of neutrophil CD14 in DVT pathogenesis and establishes it as a promising therapeutic target for DVT.
14 Apr 2026
Read appraisal →Blood advances
How to determine the optimal duration of anticoagulation for VTE: an evidence-based decision-analytical approach
When advising patients on the optimal duration of anticoagulation for venous thromboembolism (VTE), current management often relies on the intuition of treating physicians and guideline panels, an approach that remains opaque to outside observers. We present a decision-analytical framework adopted in our practice. This approach formalizes the intuition of physicians and guideline panels, providing an explicit and transparent integration of the best available evidence on benefits and harms. It also incorporates a systematic method for eliciting patient values and preferences to determine the optimal duration of anticoagulation. To demonstrate the practical utility of the decision-analytical approach, we apply it to 3 clinical scenarios: unprovoked VTE, VTE related to long-haul travel, and portal vein thrombosis in cirrhosis. By transparently synthesizing evidence and patient values, this method supports individualized, evidence-based, patient-centered recommendations for the duration of anticoagulation, and it is applicable to solving similar clinical problems in hematology and medicine.
14 Apr 2026
Read appraisal →Lancet (London, England)
Aspirin versus clopidogrel for chronic maintenance monotherapy after percutaneous coronary intervention: 10-year follow-up of the HOST-EXAM trial
BACKGROUND: The long-term clinical outcomes of clopidogrel monotherapy versus aspirin monotherapy after percutaneous coronary intervention (PCI) remain uncertain. We conducted a 10-year follow-up of the HOST-EXAM trial to assess the very long-term effects of clopidogrel versus aspirin monotherapy in this setting. METHODS: In HOST-EXAM, patients who had completed dual antiplatelet therapy without clinical events for 6-18 months after PCI were randomly assigned to receive clopidogrel 75 mg once daily or aspirin 100 mg once daily. This study is an investigator-initiated 10-year extended follow-up of the HOST-EXAM trial. The primary endpoint was a composite of all-cause death, non-fatal myocardial infarction, stroke, readmission due to acute coronary syndrome, and Bleeding Academic Research Consortium type ≥3 bleeding. The primary analysis was done in the intention-to-treat population. The study is registered with ClinicalTrials.gov (NCT02044250) and is complete. FINDINGS: From March 26, 2014, to May 29, 2018, 5530 patients were enrolled and 5438 were randomly assigned to the aspirin group (n=2728) or the clopidogrel group (n=2710). Clinical follow-up status was ascertained on May 1, 2025, resulting in a median follow-up duration of 10·5 years (IQR 9·4-11·4) after PCI and a completion rate of 92·8%. Clopidogrel was associated with a lower rate of the primary composite endpoint than aspirin (Kaplan-Meier estimate 25·4% for the clopidogrel group vs 28·5% for the aspirin group; hazard ratio 0·86 [95% CI 0·77-0·96]; log-rank p=0·0050). Clopidogrel was also associated with a lower rate of the thrombotic endpoint (17·3% vs 20·0%; log-rank p=0·0024) and bleeding endpoint (9·1% vs 10·8%; log-rank p=0·020). All-cause mortality was similar between groups. INTERPRETATION: During 10 years of follow-up, clopidogrel monotherapy, compared with aspirin monotherapy, was associated with lower rates of the primary composite, ischaemic, and bleeding outcomes, but not all-cause mortality after PCI. These findings support consideration of clopidogrel as an alternative to aspirin for long-term antiplatelet monotherapy during the chronic maintenance phase after PCI. FUNDING: Ministry of Health & Welfare, South Korea.
14 Apr 2026
Read appraisal →Medicine
Association of blood urea nitrogen to serum albumin ratio with mortality in ICU patients with sepsis-induced coagulopathy: A Retrospective Cohort Study from MIMIC-IV database
The blood urea nitrogen to serum albumin ratio (BAR) has shown promise as a prognostic marker in sepsis, yet its specific role in sepsis-induced coagulopathy (SIC) remains unclear. We aimed to clarify whether BAR informs mortality risk in critically ill intensive care unit (ICU) patients with SIC. Using the Medical Information Mart for Intensive Care IV database, we identified consecutive ICU patients admitted between 2008 and 2022 who fulfilled Sepsis-3 criteria and had an SIC score ≥4. BAR was calculated from the 1st available laboratory values. Multivariable Cox regression and restricted cubic splines were used to estimate mortality associations after adjustment for demographics, comorbidities, interventions, severity scores (Sequential Organ Failure Assessment, Simplified Acute Physiology Score II), and laboratory parameters. The primary end-point was 30-day mortality; 90- and 365-day mortality were secondary. Among 4244 patients, higher admission BAR independently predicted increased mortality at 30, 90, and 365 days. Each 10-unit rise in BAR conferred a 30% higher hazard of death within 30 days (unadjusted hazard ratio (HR) 1.30, 95% confidence interval (CI) 1.25-1.35; P < .001). After full adjustment, the association persisted (HR 1.18, 95% CI 1.12-1.24; P < .001). Similar patterns were seen for longer-term outcomes. A clear dose-response relationship was observed across BAR tertiles; patients in the highest tertile (BAR ≥ 10 mg/g) had markedly higher mortality risk (adjusted HR 1.69, 95% CI 1.43-2.00; P < .001) than those in the lowest. Nonlinear analysis revealed a curvilinear BAR-mortality relationship, and subgroup analyses showed consistent findings, with significant effect modification by age. In critically ill patients with SIC, an elevated admission BAR independently predicts short- and long-term mortality and may aid risk stratification and early intervention planning.
12 Apr 2026
Read appraisal →Medicine
Prognostic factors for mortality in age-stratified elderly sepsis patients: A retrospective cohort study based on the MIMIC-IV database
Sepsis represents a major cause of death among elderly patients; however, prognostic factors specific to different age strata within this population remain insufficiently characterized. This retrospective cohort study aimed to identify early predictors of mortality in elderly sepsis patients using the MIMIC-IV database. We included 13,461 sepsis patients aged ≥60 years, stratified into younger-old (60-74 years; n = 7618) and older-old (75-90 years; n = 5843) groups. Propensity score matching was applied to balance baseline characteristics, yielding 4657 matched patients per group. Multivariable logistic regression, receiver operating characteristic analysis, and Kaplan-Meier survival curves were employed to assess clinical and biochemical markers for 28-day mortality and to identify age-specific prognostic indicators. These findings underscore the importance of age-stratified prognostic models to guide targeted clinical interventions and potentially improve outcomes in elderly patients with sepsis. Lactate dehydrogenase (LDH) and the PaO2/FiO2 ratio emerged as key mortality predictors in the younger-old group, whereas urea nitrogen and hypoalbuminemia were predominant in the older-old group. Urine output served as a common predictor across both strata, with oliguria associated with a 2.5-fold increase in mortality risk among older patients. While a combination of predictors enhanced prognostic accuracy in younger-old patients, this was not observed in the older-old group, suggesting a greater influence of frailty and comorbidities in advanced age.
12 Apr 2026
Read appraisal →Heart (British Cardiac Society)
Subphenogroups of acute heart failure with preserved ejection fraction: comprehensive proteomics and pathway analysis
BACKGROUND: Heterogeneity of heart failure with preserved ejection fraction (HFpEF) results in significant challenges for treatment development. Identifying and characterising distinct HFpEF phenogroups may aid in tailoring therapeutic strategies for these patients. The objective of this study was to assess proteomic patterns of HFpEF phenogroups identified through a machine-learning-based clustering model, with the aim of uncovering specific biological pathways associated with each phenogroup. METHODS: This study represents a post-hoc analysis of the ongoing Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a multicentre prospective observational study of hospitalised patients with acute decompensated HFpEF. Of the overall cohort (N=1238), this study analysed 198 patients with HFpEF with available proteomics data. These patients were classified into four phenogroups using the machine-learning-based clustering model. The SomaScan assay V.4.1 was used to measure levels of >7000 plasma proteins, and subsequent pathway analysis was conducted to determine the biological differences among the phenogroups. RESULTS: We identified four distinct phenogroups: Phenogroup 1 ('rhythm trouble'), Phenogroup 2 ('ventricular-arterial uncoupling'), Phenogroup 3 ('low output and systemic congestion') and Phenogroup 4 ('systemic failure'). The proteomics revealed distinct protein expression profiles among the phenogroups, with ribonuclease 4, tax1-binding protein 1, regenerating islet-derived protein 3-gamma and alpha-1-antichymotrypsin being the most significant markers to specific identified phenogroups. Pathway analysis suggested differences in immune response, autonomic activation, cellular homeostasis and tissue repair mechanisms across the phenogroups. CONCLUSIONS: Using a comprehensive plasma proteomics approach, our study identified distinct proteomic profiles of HFpEF phenogroups, which in turn suggest specific underlying biological processes. These profiles suggest the involvement of inflammatory activation, tissue injury and regenerative responses, immune modulation and systemic stress signalling as key components of HFpEF pathophysiology. TRIAL REGISTRATION NUMBER: UMIN-CTR ID: UMIN000021831.
12 Apr 2026
Read appraisal →BMJ open respiratory research
What is the evidence for virtual wards or hospital-at-home care pathways for exacerbations of chronic obstructive pulmonary disease? A systematic review and meta-analysis.
OBJECTIVES: Given increasing interest in admission avoidance, we evaluated the evidence to support virtual wards (VW) and hospital at home (HaH) models of care during exacerbations of chronic obstructive pulmonary disease (ECOPD). DESIGN: A systematic review and meta-analysis. A comprehensive search of MEDLINE (1946 to March 2024), Embase (1974 to March 2024) and CENTRAL (searched 22 March 2024) was conducted. Risk of bias and a random effects meta-analysis were performed. POPULATION: Adults with an ECOPD presenting to the hospital or who require hospital-led care. INTERVENTIONS: VW: defined as assessments and interventions delivered remotely or HaH (defined as assessments and interventions delivered by healthcare professionals in patient's homes) care pathways, compared with hospital admission. PRIMARY AND SECONDARY OBJECTIVES: Safety (mortality rate of all causes, in-patient, 7 days and 30 days) and readmission rate in 7 and 30 days. Length of stay in hospital and changes in pulmonary function tests. RESULTS: One study assessed VWs (reported in two publications) and 10 assessed HaH. There were no changes in survival or short-term readmission rates attributable to the interventions and no evidence that VW or HaH care pathways reduced the total time a patient spent under hospital-led care, whether at home or in the hospital. CONCLUSIONS: More evidence is needed to support the widespread roll-out of HaH and especially VW pathways for ECOPD. PROSPERO REGISTRATION NUMBER: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024517565.
12 Apr 2026
Read appraisal →BMJ paediatrics open
Systematic review of the evidence of cardiac dysfunction and intravenous fluid use among children with severe acute malnutrition and severe dehydration
BACKGROUND: A sizeable proportion of cholera attributed mortality occurs among children with severe acute malnutrition (SAM). International guidelines severely limit the use of intravenous fluids among children with SAM and cholera due to their perceived vulnerability to fluid overload. We reviewed the cardiac function evidence and studies of intravenous fluid use among children with SAM. METHODS: Two systematic reviews of Chinese, English, French and Spanish language papers published after the last Global Task Force for Cholera Control recommendations on the treatment of children with cholera and SAM (2017) were conducted. The first included studies of cardiac function among children presenting to hospital with SAM. The second included studies of intravenous fluid use among children with dehydrating diarrhoea and SAM. RESULTS: Among eight echocardiographic studies of cardiac function in children with SAM at presentation to hospital, all found left ventricular mass to be decreased but none found systolic function to be impaired. Seven of the eight studies assessing diastolic function found some degree of mild-to-moderate impairment. Two echocardiographic studies monitored responsiveness to intravenous fluid resuscitation and found no evidence of fluid overload. Finally, four studies gave intravenous fluids to children with SAM and either shock or dehydration. Collectively, these studies included 251 children with SAM given intravenous fluid. No cases of fluid overload were observed. CONCLUSION: No evidence to support decreased systolic function among children with SAM and no documented cases of fluid overload during intravenous rehydration of these children were identified. These data call into question guidelines that severely restrict intravenous fluids among children with SAM and highly dehydrating conditions such as cholera.
12 Apr 2026
Read appraisal →PloS one
Applying machine learning and natural language processing to patient safety event reports: Identifying patterns of cardiovascular diagnostic errors
OBJECTIVE: To explore the utility of natural language processing (NLP) and machine learning (ML) techniques to identify unsafe conditions leading to cardiovascular diagnostic errors using patient safety event (PSE) reports data. METHODS: PSE reports from January 2016 to August 2021 from a multi-hospital healthcare system in the mid-Atlantic region of the United States were included in this study. To have the true cardiovascular diagnostic errors labels for PSE reports, each individual PSE report was manually reviewed to find clinician-reported narratives describing current definitions of cardiovascular diagnostic errors. The PSE reports which contained cardiovascular diagnostic errors-related narratives were labeled as one and zero otherwise. Four binary ML models were employed to identify cardiovascular diagnostic errors narratives and common features from annotated PSE reports data: (1) simple logistic regression, (2) elastic net, (3) XGBoost, and (4) deep neural networks. RESULTS: XGBoost outperformed the rest of the models in identifying cardiovascular diagnostic errors -related reports and achieved high performance metrics on the testing data (AUROC = 0.914, specificity = 0.982, PPV = 0.866, accuracy = 0.929, F-1 score = 0.738, and AUPRC = 0.783). Pacemaker emerged as a significant signal for cardiovascular diagnostic errors in our PSE reports. Our analysis demonstrated that ordering MRI for patients with pacemaker was a frequent theme among cardiovascular diagnostic errors-related PSE reports containing the word pacemaker. Order, EKG, cardiac, and chest were the five most important features in identifying cardiovascular diagnostic errors events. These words were utilized in explaining heart conditions, associated care process, and related safety incidents. CONCLUSIONS: Findings from our study demonstrates the feasibility of ML and NLP techniques in identifying cardiovascular diagnostic errors-related reports among the existing PSE data sources. However, validation in external healthcare systems is needed before broader application.
11 Apr 2026
Read appraisal →PloS one
Prevalence and risk factors for AMS: A systematic review and meta-analysis
BACKGROUND: Acute Mountain Sickness (AMS) is a high-altitude-specific condition with variable prevalence and poorly defined risk factors. Despite growing global exposure to high-altitude environments, no systematic review has comprehensively synthesized AMS epidemiology. OBJECTIVES: To assess the prevalence of AMS and critically evaluate evidence on risk factors associated with increased susceptibility. DESIGN: A systematic review and meta-analysis following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. DATA SOURCES: PubMed, Cochrane Library, Web of Science, CINAHL Plus, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and Weipu Database (VIP) were comprehensively searched for observational studies investigating the prevalence and risk factors of AMS from January 1, 2004 to December 31, 2024. REVIEW METHODS: Original journal articles were included which met the inclusion criteria. The quality of the included studies was evaluated independently by two investigators. Meta-analysis was conducted using R software (v4.2.2), with estimates of AMS from pooled using a random-effects model. RESULTS: Fifty-eight studies (n = 2,705) were included. The pooled AMS prevalence was 48.25% (95% CI: 42.58-53.96%), with substantial heterogeneity (I² = 82.3%). Significant risk factors included extreme altitude (>5500 m; RR = 1.89), rapid ascent (<6 h; RR = 1.60), marked heart rate increase (>20 bpm; RR = 2.35), and oxygen saturation decline (>10%; RR = 2.02). Prior AMS history (RR = 1.36) and male sex (RR = 1.15) were also associated with higher risk, while older age (>50 years) was protective (RR = 0.78). CONCLUSION: AMS affects nearly half of high-altitude visitors. Altitude, ascent rate, cardiopulmonary response, and prior AMS history are key risk determinants. These findings support pre-exposure risk stratification, staged ascent protocols, and real-time physiological monitoring. PROSPERO CRD42024595365.
11 Apr 2026
Read appraisal →JMIR aging
Global, Regional, and National Burden of Falls Among Older Adults Aged 65 Years and Above: Secondary Data Analysis of the Global Burden of Disease Study 2021
BACKGROUND: Falls are a leading cause of injury, disability, and death among older adults, posing significant public health challenges. However, comprehensive global analyses of fall-related burdens in older populations remain scarce. OBJECTIVE: This study aimed to explore the patterns and distribution of the global, regional, and national burden of falls among adults aged 65 years and older. METHODS: Data from the Global Burden of Disease study 2021 were used to assess the overall, disability, and mortality burden of falls among adults aged 65 years and older. Age-standardized rates of disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost were calculated to compare burdens across countries. Health inequalities were evaluated via the slope index of inequality and the concentration index. Frontier analysis identified optimal burden levels by sociodemographic index (SDI). Bayesian age-period-cohort models projected trends up to 2050. RESULTS: DALY age-standardized rates showed a U-shaped distribution across SDI regions: lower-SDI countries faced higher mortality burdens, while higher-SDI countries had elevated disability burdens. Despite an increase in absolute overall burden inequality from 1990 to 2021, absolute inequalities in YLDs and years of life lost declined, with DALYs and YLDs exhibiting relatively more balanced distributions. Frontier analysis pinpointed countries with the greatest burden reduction potential. Projections suggest decreasing overall and mortality burdens by 2050 but rising disability burdens. CONCLUSIONS: Higher- and lower-SDI countries face distinct fall-related challenges. Reducing cross-national health inequalities and closing gaps between the observed burden and the optimal burden level achievable at a similar SDI level are critical. Despite projected declines in the overall burden (DALYs), the rising disability burden (YLDs) could present evolving challenges, potentially underscoring the importance of proactive preparedness.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Discourse, Secondary Victimization, and Psychological Harm: Mixed-Methods Analysis of System Justification in the #MeToo Movement
BACKGROUND: The #MeToo movement, initiated in 2006 and amplified on social media in 2017, mobilized women worldwide to share experiences of sexual harassment and assault online. While the movement increased awareness, it also revealed deep social divisions in digital spaces. Supportive discussions promoted solidarity and healing, whereas antagonistic responses reinforced backlash and secondary victimization. In India, the Indian Entertainment Industry (IEI) became a focal point where survivors' disclosures highlighted structural gender inequalities. These polarized reactions function as digital-health signals, reflecting stigma, psychosocial distress, and conditions that shape women's safety and mental well-being. Examining these narratives as indicators of public health risk helps identify patterns of structural inequity and secondary mental health burdens among survivors. OBJECTIVE: This study examined online discourse surrounding #MeToo to identify forms of system-justifying narratives on social media and to assess how #MeTooIndia exposed institutional inequities within the IEI. METHODS: This mixed-methods study comprised 2 components. In study 1, natural language processing was applied to analyze global #MeToo Twitter (subsequently rebranded X) discourse. From an initial corpus of 350,000 tweets, 205,082 were preprocessed, and sentiment and stance detection analysis identified 18,416 tweets expressing negative attitudes toward the movement. Latent Dirichlet allocation topic modeling extracted 22 topics, 12 of which aligned with system-justification categories, revealing distinct lexical and semantic patterns related to gender, institutional, and power dynamics. Two trained coders manually annotated a subsample to ensure conceptual clarity and interrater reliability. Study 2 involved qualitative, semistructured interviews with 20 academic experts in film, gender, and media studies to gather opinions on how #MeTooIndia influenced institutional discourse in the IEI and how these dynamics translate into digital and mental health risks. RESULTS: Analysis of #MeToo Twitter discourse in study 1 identified 4 primary forms of system justification: by gender, by the institutional system, by backlash, and by victim-blaming. Gender and institutional system justifications were the most prevalent. Study 2 reinforced these findings, revealing how experts perceived #MeTooIndia as both empowering and constrained by entrenched institutional and cultural barriers. Together, our findings highlight the dual function of social media in promoting collective advocacy while reproducing conditions linked to gender-based violence, psychological stress, and reduced help-seeking-key digital and mental health concerns. CONCLUSIONS: This mixed-methods study reveals that digital discourse surrounding #MeToo often sustains existing gender and institutional hierarchies rather than dismantling them. Across Twitter data and expert interviews, gender and institutional system justifications emerged as dominant narratives, highlighting how online spaces can reinforce structural inequities while appearing progressive. Although #MeToo amplified visibility and awareness, its potential for lasting institutional change remains limited. These findings underscore the need for trauma-informed digital governance, public health recognition of online hostility as a psychosocial risk, and frameworks that situate digital activism to institutional reforms that support safety and mental well-being.
11 Apr 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
A machine-learning-based tool for stroke risk prediction in blunt cerebrovascular injury: development and preliminary evaluation
PURPOSE: Stroke risk correlates with the Biffl grading system in blunt cerebrovascular injury (BCVI). Although anti-thrombotic therapy is the mainstay of stroke prevention, no point-of-care clinical decision-support tool exists to guide timing for therapy. We sought to develop an interactive online calculator that incorporates patient-specific demographic and injury characteristics to estimate stroke risk and risk reduction with anti-thrombotic (AT) administration. METHODS: Data from BCVI patients (n = 1,197) at a Level I Trauma Center were retrospectively collected. Six machine learning methods were employed to predict stroke risk with and without AT therapy. Class imbalance was addressed using downsampling and/or class weighting. Model performance was assessed using 10-fold cross-validation. The model was implemented as an R-based Shiny online application. RESULTS: Stroke rate among the population was 4%, and the strongest predictors for stroke were the greatest Biffl grade of carotid (aOR [95%CI] = 2.02 [1.62-2.53]) and vertebral injuries (1.44 [1.18-1.77]). The least absolute shrinkage and selection operator (LASSO) model outperformed all others, achieving 66% [33%-100%] sensitivity and 74% [62%-82%] specificity for stroke prediction, with an area under the receiver operating characteristic curve of 0.79 [0.57-0.95]. This model was integrated into an interactive online tool ( https://grady-bcvi-calc.shinyapps.io/calculator/ ), where patient demographic and injury characteristics can be used to compute baseline stroke risk and estimate stroke risk with AT. CONCLUSION: We developed and evaluated a preliminary predictive model for personalized stroke risk assessment in patients with BCVI using key risk factors. The integration of patient-specific risk-benefit assessments into clinical decision-making could optimize and reduce variability in AT therapy. External validation is warranted to prepare this tool for broad clinical applicability.
11 Apr 2026
Read appraisal →Games for health journal
The Effect of Emergency Escape Room Simulation on Knowledge, Anxiety, and Satisfaction Levels of Students: A Randomized Controlled Trial
BACKGROUND: The aim of this study is to determine the effect of escape room simulation intervention on the knowledge, anxiety, and satisfaction levels of students in emergency nursing education. METHODS: This randomized controlled study was conducted with 40 students studying at the nursing faculty of a university in Istanbul. The students in the intervention group (n = 20) were given an escape room simulation intervention related to emergency nursing education. The students in the control group (n = 20) were not given any intervention other than the traditional education method. Data were collected using the Knowledge Test, State Anxiety Scale, and Visual Analog Scale for satisfaction. RESULTS: The knowledge level of the students in the intervention group measured immediately after the education (P = 0.003), 1 week after the education (P < 0.001), and 1 month after the education (P < 0.001) was found to be significantly higher than that of the control group. While no significant difference was found in anxiety levels before and after the education between the groups, a significant difference was found in anxiety levels before and after the training in the intervention group (P < 0.001). The satisfaction level measured immediately after the education was found to be significantly higher in the intervention group than in the control group (P < 0.001). CONCLUSION: This study show that escape room simulation increased emergency nursing knowledge and satisfaction and reduced anxiety levels of the students. It is recommended that escape room simulation intervention be used in more studies in nursing education. The study was registered on ClinicalTrials.gov.tr with the number NCT06619119.
11 Apr 2026
Read appraisal →A&A practice
Efficacy and Safety of Ketamine or Esketamine versus Fentanyl-Class Opioids as Adjuncts to Propofol for Gastrointestinal Endoscopy: A Systematic Review and Meta-Analysis
OBJECTIVE: Propofol is widely used for gastrointestinal (GI) endoscopies but may cause respiratory depression and hemodynamic compromise when used alone. Adjuncts such as ketamine or esketamine and fentanyl-class opioids (fentanyl, alfentanil, and sufentanil) are used to optimize sedation, yet their comparative efficacy and safety remain uncertain. This meta-analysis evaluated ketamine or esketamine plus propofol (KP regimens) versus fentanyl/alfentanil/sufentanil plus propofol (FP regimens). METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, Scopus, Cochrane Library, and Google Scholar from inception to February 2025 for randomized controlled trials (RCTs). Eligible studies included patients undergoing GI endoscopy (Population), receiving ketamine or esketamine with propofol (Intervention), compared with fentanyl-class opioids with propofol (Comparison), and reporting outcomes including propofol dose, comfort, adverse events, recovery time, or procedure time (Outcomes). Risk of bias was assessed with the Cochrane tool. Heterogeneity was evaluated using Cochran's Q test and the I2 statistic, and clinical heterogeneity was considered based on study design and population differences. Sensitivity analyses were conducted to test robustness of findings. RESULTS: Fifteen RCTs (1492 participants) were included. KP regimens significantly reduced total propofol use compared with FP (mean difference [MD] -0.42; 95% confidence interval [CI] -0.66 to -0.19; P = .0005), with greater benefit observed for esketamine (p for subgroup = 0.001). No significant differences were found in sedation (MD 0.01; 95% CI -0.40 to 0.43; P = .95) or pain scores (MD 0.24; 95% CI -0.18 to 0.65; P = .26). Safety outcomes were comparable across groups, with no significant differences in desaturation, nausea/vomiting, bradycardia, or tachycardia, except for hypotension, which was lower with KP (risk ratio [RR] 0.56; 95% CI 0.39 to 0.82; P = .003). Recovery and procedure times were similar between groups. CONCLUSIONS: Both KP and FP regimens are effective and safe for GI endoscopy. KP regimens reduce total propofol requirements and significantly lower the risk of hypotension, while showing comparable sedation quality, analgesia, and recovery profiles to FP. These findings suggest KP may offer safety and dosing advantages, though larger standardized trials are needed to confirm its clinical superiority.
10 Apr 2026
Read appraisal →Journal of medical Internet research
Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems
BACKGROUND: Deaf individuals often face communication challenges when interacting with those who can hear. Within health care settings, these challenges may pose risks to their safety, potentially resulting in misdiagnoses, treatment errors, and decreased quality of care. OBJECTIVE: This study aims to systematically review the evidence on communication systems reported in the literature that use human-computer interaction techniques to support communication between deaf individuals who use sign language and hearing health professionals in health care settings. The review focuses on systems that are either currently in use or proposed for use in health care and that have been tested using human participants or videos of human users. METHODS: A comprehensive search was performed via MEDLINE, Web of Science, ACM, IEEE Xplore, Scopus, and Google Scholar in March 2025. The inclusion criteria comprised studies developing a sign language recognition system within a health care context and testing with human users. Eligible studies underwent screening by 2 independent investigators (LRV and LMMSR or LFRdO and GTdSS), with any disagreements resolved by a senior researcher (MSM). RESULTS: The search retrieved 21,778 publications, and screening of reference lists identified 2 additional studies, resulting in a total of 23 studies meeting the eligibility criteria. Most systems (15/23, 65.2%) were image-based, while 34.8% (8/23) relied on sensors (glove-based or depth-sensing). Applications varied across health care settings, including general hospital care (10/23, 43.5%), emergencies (8/23, 34.8%), and primary care (4/23, 17.4%). All systems were in the development and testing stage, with no data on security and psychological impacts. Accuracy ranged from 25% to 100% for image-based and 72% to 99.7% for sensor-based systems. Bidirectionality and facial expression recognition, crucial for effective communication, were largely overlooked. CONCLUSIONS: Image-based systems were more common than sensor-based ones, though both showed wide variability in accuracy in recognizing and interpreting signs. Most systems failed to address critical aspects such as bidirectional communication and the recognition of facial expressions, essential for effective communication. None fully addresses the requirements for integration into health care settings. These findings highlight the need for further research on implementation, usability, and impact on the quality of care for deaf patients.
10 Apr 2026
Read appraisal →PloS one
A decade of triage with the Manchester Triage System-The MTS big data study
BACKGROUND: Over the past 25 years, numerous studies have evaluated triage system performance, but longitudinal data are limited. This study is the first to assess the Manchester Triage System (MTS) over a decade to determine the accuracy of initial patient assessments and system stability under external influences such as staff turnover, health policy changes, demographic shifts, and the COVID-19 pandemic. METHODS: We conducted a large-scale, monocentric, retrospective observational study at a tertiary care hospital. RESULTS: Between 2014 and 2023 a total of 355,135 emergency patients were included. Average age increased from 48.1 to 52.1 years. The distribution of the five MTS urgency levels remained stable. Prediction of hospital admission (AUC 0.719-0.756) and ICU admission (AUC 0.831-0.862) was consistent throughout the period. Twelve-hour survival probabilities were above 0.99 for "blue" to "orange" patients and above 0.89 for "red" patients. Short-term mortality prediction (AUC) ranged from 0.845 to 0.894. Thirty-day survival remained >0.8 for "blue" to "orange" stages, while the "red" stage declined from 0.63 (2014) to 0.44 (2023). The AUC for 30-day mortality increased from 0.672 to 0.750 over the decade. CONCLUSION: Construct validity assessment demonstrates that the MTS reliably assigns appropriate urgency levels to incoming emergency patients. Longitudinal evaluation shows that its accuracy remained stable over ten years despite high staff turnover, health policy and demographic changes, and the COVID-19 pandemic. These findings confirm the robustness and reliability of the MTS in a high-volume tertiary care setting and support its continued use for emergency patient prioritization.
10 Apr 2026
Read appraisal →JMIR mental health
Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study
BACKGROUND: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones-rather than just usage patterns-may provide more proximal signals of risk. OBJECTIVE: This study aimed to test whether vision-language models (VLMs) applied to passively captured smartphone screenshots can predict momentary suicidal ideation (SI). METHODS: Seventy-nine adults with past month suicidal thoughts or behaviors completed ecological momentary assessments (EMA) over 28 days while screenshots were captured every 5 seconds during active phone use. We fine-tuned open-source VLMs (Qwen2.5-VL [Alibaba Cloud], LFM2-VL [Liquid AI]), and text-only models (Qwen3 [Alibaba Cloud]) to predict SI from screenshots captured in the 2 hours preceding each EMA. We evaluated performance with temporal and subject holdouts. RESULTS: The analytic sample comprised 2.5 million screenshots from 70 participants. Temporal holdout models achieved strong discrimination at the EMA level (AUC=0.83; AUPRC=0.77), with image-based models outperforming text-only models (AUC=0.83 vs 0.79; 95% CI 0.003-0.07). Subject holdout generalization was near chance (AUC≈0.50), though a simple lexical screening method retained modest discrimination (AUC=0.62). Smaller models performed comparably to larger models, supporting feasible on-device deployment. CONCLUSIONS: Screen content predicts short-term SI with clinically meaningful accuracy when models are personalized but does not generalize across individuals. These findings support a 2-stage clinical architecture, coarse lexical screening for new patients, with personalized VLM-based monitoring after a calibration period. On-device inference may enable privacy-preserving deployment.
10 Apr 2026
Read appraisal →Scientific reports
Association between estimated plasma volume status and all-cause mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: analysis of the MIMIC-IV database
BACKGROUND: The estimated plasma volume status (ePVS) is an independent risk factor for poor prognosis in certain cardiovascular disease populations. However, the relationship between ePVS and prognosis in critically ill patients with non-traumatic subarachnoid hemorrhage (NSAH) remains unclear. This study aims to explore the association between ePVS and all-cause mortality (ACM) in NSAH patients. METHODS: A total of 750 NSAH patients were included in this retrospective study. The primary outcome was 1-month ACM. A Cox proportional hazards model was constructed to examine the relationship between ePVS and ACM in critically ill NSAH patients. A restricted cubic spline model was used to assess the relationship between ePVS and the outcome. RESULTS: In this patient cohort, 1-month, 3-month, and 1-year ACM rates were 21%, 25%, and 29%, respectively. After adjusting for potential confounders, we found a U-shaped relationship between ePVS and 1-month ACM, with an inflection point at 3.94. Specifically, when ePVS is below the inflection point, each unit increase in ePVS reduces the mortality risk by 56% [HR, 0.44 (95% CI 0.25, 0.76)]. However, once the inflection point is surpassed, each unit increase in ePVS increases the mortality risk by 1.8 times [HR, 2.80 (95% CI 1.58, 4.97)]. CONCLUSION: We identified a U-shaped relationship between ePVS and ACM in critically ill patients with NSAH, suggesting both low and high ePVS levels may portend adverse outcomes. Prospective multicenter studies are warranted to validate these findings and elucidate underlying mechanisms.
10 Apr 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prevalence of respiratory viruses in stable and acute asthma: a systematic review and meta-analysis
BACKGROUND: Respiratory viruses, frequently detected in asthma, are associated with worse outcomes. This meta-analysis systematically quantifies the prevalence of respiratory viruses in stable and acute asthma, across children and adults, and explores factors associated with increased viral burden through meta-regression. METHODS: This prospectively registered meta-analysis (PROSPERO-CRD42023375108) included studies employing molecular techniques to assess respiratory virus prevalence in asthma. Three databases were searched in August 2024. Risk of bias and certainty of evidence were assessed. We performed random-effects meta-analysis of proportions. RESULTS: We included 111 eligible studies. Moderate-certainty evidence indicated a pooled prevalence of any respiratory virus of 33.9% (95% confidence interval 24.8-43.7%) in children and 23.0% (12.9-35.0%) in adults with stable asthma. In acute asthma, prevalence increased to 58.8% (52.5-65.0%) in children and 49.9% (41.2-58.5%) in adults (moderate certainty). Rhinovirus was the most frequently identified virus, especially in acute asthma (45.0% in children versus 21.2% in adults). Respiratory syncytial virus and bocavirus were more common in younger children, while coronavirus and influenza were more frequently detected in adults; respiratory syncytial virus peaked in older adults too. A higher prevalence of influenza virus B and adenovirus in children, and of influenza virus A and parainfluenza 2 in adults with severe versus non-severe acute asthma suggests a potential association with more severe acute attacks. CONCLUSION: Respiratory viruses are common in both stable and acute asthma. This suggests that the diagnostic value of a positive viral test during acute episodes may be limited and could benefit from complementary biomarkers to improve interpretation.
10 Apr 2026
Read appraisal →BMJ paediatrics open
Does osteogenesis imperfecta predispose infants to metaphyseal fractures? A systematic review.
OBJECTIVE: The notion that osteogenesis imperfecta (OI) predisposes children to metaphyseal fractures is controversial. This review aimed to ascertain the prevalence of metaphyseal fractures in children with OI less than 2 years of age. MATERIALS AND METHODS: Medline, CINAHL and Web of Science were searched for studies reporting metaphyseal fractures in children with OI less than 2 years of age. Articles not retrievable, not published in English, or where 'metaphyseal' fracture was not defined/illustrated, were excluded. Article quality was independently assessed using the Critical Appraisal Skills Programme and Case Reports tools. RESULTS: Of 298 retrieved abstracts, 82 were duplicates: 216 articles were screened by title and abstract, excluding 186. Hand search of the remaining 30 articles identified two further articles. Only four articles were included: (1) 4/41 children with OI aged between 1 day and 3 years with 'metaphyseal' fractures; (2) a case series including a 16-month-old boy with three 'metaphyseal' fractures. Abuse concerns remained despite a type IV OI diagnosis (not genetically confirmed); (3) a 7-month-old girl with two 'corner' fractures suspected to have OI and to have been abused; and (4) a pair of siblings with 'metaphyseal' fractures and type XI OI (FKBP10 mutation). CONCLUSIONS: There is limited evidence that OI predisposes infants and young children to metaphyseal fractures. The two diagnoses are not mutually exclusive and until further large studies are conducted, physical abuse should always be considered in infants and young children with metaphyseal fractures, even in those with confirmed OI.
9 Apr 2026
Read appraisal →Sexual health
Resilience of STI service delivery in community-based organizations during the 2024 armed conflict: insights from Lebanon.
This study explores the potential impact of war on STI services in Lebanon. Programmatic STI service data from Lebanon were analyzed, alongside a targeted review of the literature on conflict-affected settings, using public health databases covering the period 2014-2024. Evidence-based STI service packages for conflict settings remain limited. Drawing insights from the COVID-19 crisis, periods of emergencies are associated with an elevated risk of STI surges among key populations. Programmatic data from the conflict further demonstrated this increased risk, reflected in reductions in prevention and early detection activities compared with pre- and post-conflict periods. Self-care and outreach service packages could be critical in these contexts. Community-based organizations (CBOs) should adapt STI services to the wartime environment and evaluate the effectiveness of these interventions post-conflict. CBOs are encouraged to prioritize the implementation of self-care modalities for STI prevention and early detection, expand the availability of telehealth services, and deploy mobile clinics to maximize service continuity. This editorial analyzes STI service delivery within the specific context of the Lebanese war.
9 Apr 2026
Read appraisal →Journal of medical Internet research
Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis
BACKGROUND: Liver cirrhosis (LC) can lead to several complications. Esophageal variceal bleeding (EVB) and esophagogastric variceal bleeding (EGVB) are particularly severe, leading to a high risk of mortality. Early identification of esophageal varices and esophagogastric varices is essential. Several studies have constructed prediction models for EVB and EGVB in patients with LC. However, robust systematic evidence to prove their performance is lacking. OBJECTIVE: We included original studies that developed prediction models for esophageal or gastric variceal bleeding in patients with LC under different modeling variables. This study aimed to review the predictive performance of various models for EVB or EGVB in patients with LC, providing insights into the development or updating of simplified scoring tools in the future. METHODS: PubMed, Web of Science, Embase, and the Cochrane Library were searched up to August 21, 2024, to collect original full-text studies on machine learning (ML) in the prediction of EVB and EGVB in patients with LC. The models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses were carried out based on the modeling variables. RESULTS: In total, 21 studies were included, with 7011 patients with LC, among whom 1412 (20.14%) developed EVB and 733 (10.45%) developed EGVB. The meta-analysis results suggested that the pooled c-index, sensitivity, and specificity of the prediction model for predicting EVB in the validation set were 0.85 (95% CI 0.77-0.92), 0.93 (95% CI 0.87-0.96), and 0.66 (95% CI 0.46-0.82), respectively. The pooled c-index, sensitivity, and specificity of the prediction model for predicting EGVB in the validation set were 0.89 (95% CI 0.85-0.94), 0.77 (95% CI 0.66-0.85), and 0.81 (95% CI 0.67-0.90), respectively. The subgroup analysis based on modeling variables revealed that, for predicting EVB, the c-index in the validation set was 0.84 (95% CI 0.80-0.88) for models based on clinical features, 0.82 (95% CI 0.69-0.96) for radiomics-based models, 0.78 (95% CI 0.67-0.89) for models based on radiomics and clinical features, and 0.97 (95% CI 0.95-1.00) for models based on endoscopic features. Subgroup analyses based on modeling variables revealed that, for predicting EGVB, the c-index in the validation set was 0.91 (95% CI 0.86-0.96) for models based on clinical features and 0.85 (95% CI 0.75-0.96) for models based on radiomics and clinical features. CONCLUSIONS: ML methods are feasible for predicting EVB and EGVB in patients with LC. Nevertheless, the number of included original studies is limited. In the future, more studies with larger sample sizes are needed to promote the application of ML in the early assessment of EVB and EGVB in patients with LC in clinical practice.
9 Apr 2026
Read appraisal →PloS one
Dizziness in the emergency department and risk of stroke: A systematic review and meta-analysis
BACKGROUND: Dizziness is a frequent emergency department (ED) presentation, and a subset of patients, especially those with isolated dizziness without focal neurological deficits, have stroke but are prone to misdiagnosis and adverse outcomes. Reported stroke prevalence in ED dizziness cohorts is highly heterogeneous, and comprehensive assessments of isolated dizziness remain limited. METHODS: Following PRISMA guidelines, we systematically searched PubMed, Web of Science, Embase, and the Cochrane Library for relevant studies. We included cross-sectional studies reporting stroke prevalence among all ED patients with dizziness or isolated dizziness. A random-effects model was used for meta-analysis to calculate pooled prevalence. Subgroup analyses and Egger's test were employed to explore heterogeneity and publication bias. The diagnostic accuracy of bedside diagnostic tools was also systematically reviewed. RESULTS: Twenty-nine studies involving 161,013 ED patients presenting with dizziness were included. The pooled stroke prevalence among all ED dizziness patients (n = 158,583) was 5.5% (95% CI: 4.1-7.1). Among patients with isolated dizziness (n = 2,559), the pooled prevalence was 13.9% (95% CI: 8.2-20.9), substantially higher than in the overall dizziness cohort. Subgroup analyses indicated diagnostic methods and hospital level as major contributors to heterogeneity. Summary analysis of bedside diagnostic tools showed that HINTS and STANDING examinations have high diagnostic accuracy overall, while the TriAGe+ score can be applied flexibly for screening or confirmation based on different cut-off points. CONCLUSIONS: ED patients with dizziness carry a meaningful, setting-dependent stroke risk. Standardized bedside exams (HINTS, STANDING) improve early triage, and selectively deploying neuroimaging helps prevent missed strokes when risk is high.
9 Apr 2026
Read appraisal →Journal of clinical microbiology
Etiologies of community-acquired febrile illness identified by TaqMan Array Card qPCR on blood samples: a systematic review and meta-analysis
UNLABELLED: Community-acquired febrile illness presents a significant diagnostic challenge. The TaqMan Array Card (TAC) enables simultaneous detection of many bacterial, viral, parasitic, and fungal pathogens. This systematic review aimed to characterize the etiologies of community-acquired fever diagnosed by TAC directly on blood specimens. A systematic search of PubMed, Scopus, Embase, and Web of Science (from inception to January 2026 in English) identified studies using TAC to detect pathogens directly in human blood to identify the etiology of community-acquired febrile illness. Eligible studies were appraised using a modified Joanna Briggs Institute (JBI) checklist. Pooled proportions and meta-analyses were conducted using a random-effects model. A total of 16 studies comprising 8,937 participants from 12 African and Asian countries met inclusion criteria. TAC detected at least 1 pathogen in 0.43 (95% CI [0.32; 0.54]), with high between-study heterogeneity (I² = 99%). Parasitic infections were most common (56%), nearly all due to Plasmodium spp., followed by bacterial (26%), viral (22%), and fungal (0.5%) etiologies. Plasmodium spp., Rickettsia spp., dengue virus, Lassa fever virus, Streptococcus pneumoniae, Orientia tsutsugamushi, Mycobacterium tuberculosis complex, and Salmonella spp. were the most frequently identified pathogens. Culture-negative bacteria accounted for 56% of bacterial detections. Positivity with more than 1 organism occurred in 6% of cases. TAC showed strong concordance with blood culture for culturable organisms (positive agreement: 73%, 95%CI [0.53; 0.89]; negative: 98%, 95%CI [0.95; 1.00]). TAC provides culture-independent pathogen detection and reveals overlooked causes of febrile illness, such as culture-negative bacteria. Its application may improve clinical diagnosis and surveillance programs. IMPORTANCE: Accurate identification of the causes of community-acquired febrile illness is essential for guiding clinical decision-making, surveillance programs, and public health response. This systematic review and meta-analysis synthesizes data from more than 8,937 patients across Africa and Asia tested with blood-based TaqMan Array Card (TAC) assays. TAC enabled broad, culture-independent detection of bacterial, viral, parasitic, and fungal pathogens-revealing both expected causes, such as Plasmodium spp. and dengue virus, and neglected culture-negative bacteria including Orientia tsutsugamushi, Coxiella burnetii, and Rickettsia spp. By uncovering these underrecognized etiologies, TAC provides valuable pathogen-level data to strengthen fever surveillance, inform outbreak detection, and refine regional disease prioritization frameworks. These findings highlight the potential of high-throughput molecular diagnostics to enhance infectious disease control, particularly in resource-limited settings.
9 Apr 2026
Read appraisal →Biomedical physics & engineering express
Bluetooth low energy thermohygrometric sensor for continuous respiratory rate monitoring: 48 h breathing simulator and human proof-of-concept
Continuous respiratory rate monitoring is essential for early detection of clinical deterioration, but conventional methods like capnography are often impractical outside high-acuity settings. We developed a low-cost, Bluetooth Low Energy (BLE) respiratory rate monitor incorporating a MEMS temperature-humidity sensor (AHT21) and Nordic nRF52840 microcontroller, designed to attach externally to a standard oxygen mask. The device detects breaths using rapid, cyclic changes in exhaled temperature and humidity via a slope-based time-domain algorithm. Bench validation over 48 h using a breathing simulator (5-60 breaths min-1, tidal volume 300 ml, 34 °C, 95%-100% RH) demonstrated 96.4% overall breath detection accuracy and strong correlation with programmed rates (r = 0.996, P < 0.0001). Sensor saturation occurred at high respiratory rates (60 breaths min-1), and high ambient humidity levels (90% RH) both of which reduced measurement accuracy. Human testing in three healthy adults showed strong agreement with capnography (mean bias 0.44 breaths min-1; 95% limits of agreement -1.86 to 2.75), with minor under-detection at the highest rate (50 breaths min-1). The device operated reliably across oxygen flow rates of 4-15 l min-1, with reduced performance at 0-2 l min-1due to saturation of the sensor. These results demonstrate that BLE-enabled MEMS thermohygrometric sensors can accurately monitor respiratory rate under controlled conditions while highlighting operational limits due to sensor saturation. This study extends prior work on humidity-based respiratory monitoring and illustrates the potential for low-cost, portable respiratory rate devices suitable for low-acuity or resource-limited clinical settings.
9 Apr 2026
Read appraisal →European journal of pediatrics
Predicting coronary artery abnormalities in Kawasaki disease: Model development and validation
Routine echocardiography at one month after diagnosis is the current standard for screening coronary artery abnormalities (CAA), a major complication of Kawasaki disease. The study aimed to develop and validate models to predict CAA and assess whether routine echocardiography could be safely reduced in low-risk patients. Two prospective multicenter Japanese registries were utilized: PEACOCK (development/internal validation) and Post-RAISE (external validation). Variables obtained within one week of diagnosis were used to predict CAA at one month after diagnosis, defined as a maximum coronary artery Z score (Zmax) ≥ 2. The models included simple models using the previous maximum Z score only, logistic regression models, and machine learning models (LightGBM and XGBoost). Discrimination, calibration, and clinical utility were assessed. Among 4,973 PEACOCK and 2,438 Post-RAISE patients, the CAA incidence was 5.5% and 6.8%, respectively. Twenty-two models were developed using 29 variables. For external validation, a simple model using the maximum Z score at week 1 produced an area under the curve (AUC) of 0.79; adding other variables or using more complex models did not increase the AUC by more than 0.02. The models failed to efficiently reduce the number of echocardiographic examinations while minimizing missed cased of CAA.Conclusion: Until superior predictors are identified, routine echocardiography at one month after diagnosis should remain the standard practice.
9 Apr 2026
Read appraisal →Journal of the American Heart Association
Effects of Computed Tomography Hyperdense Middle Cerebral Artery Sign on the Efficacy of Endovascular Therapy in Acute Ischemic Stroke With Large Infarcts: A Subgroup Analysis of the ANGEL-ASPECT Trial
BACKGROUND: The hyperdense middle cerebral artery sign (HMCAS) observed on noncontrast computed tomography is associated with thrombus composition and thrombectomy outcomes. The impact of HMCAS on the efficacy of endovascular therapy (EVT) in patients with large core infarcts remains unclear. METHODS: This analysis uses data from the ANGEL-ASPECT (Endovascular Therapy in Acute Anterior Circulation Large Vessel Occlusive Patients With a Large Infarct Core) trial, a multicenter randomized controlled trial conducted in China. Patients with acute ischemic stroke and anterior-circulation large-vessel occlusion were categorized according to whether HMCAS was present on baseline noncontrast computed tomography. The primary outcome was the 90-day modified Rankin Scale score. RESULTS: Of the 432 patients included in this analysis, 33% were HMCAS positive on baseline noncontrast computed tomography. In the EVT-treated patients, patients with HMCAS had worse functional outcomes than those without HMCAS (adjusted relative risk, 0.44 [95% CI, 0.26-0.73]; P=0.002). Patients with HMCAS required a greater number of thrombectomy passes (P<0.001). Among patients without HMCAS, EVT was associated with better functional outcomes than medical management (generalized odds ratio [OR], 2.78 [95% CI, 1.80-4.27]; P<0.001), whereas this benefit was not statistically significant among patients with HMCAS (adjusted OR, 1.69 [95% CI, 0.93-3.08]; P=0.09). There was no significant interaction between HMCAS status and treatment assignment (P=0.19). CONCLUSIONS: In this subgroup analysis comparing EVT with medical management, we found no statistically significant treatment-by-HMCAS status interaction, indicating that the benefits of EVT extend to patients with large core infarcts irrespective of HMCAS status. However, the magnitude of benefit appears greater in patients without HMCAS, whereas those with HMCAS tend to have a poorer overall prognosis. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04551664.
9 Apr 2026
Read appraisal →BMJ open
Process evaluation of a basic life support educational intervention (FirstCPR cluster randomised study) delivered at community organisations in New South Wales, Australia.
BACKGROUND: The FirstCPR cluster randomised trial delivered multimodal basic life support (BLS) learning opportunities to community organisations. An a priori process evaluation examined intervention implementation, including participation, reach, uptake and member engagement. METHODS: The study used a multimethod process evaluation. Data were collected via semistructured interviews, focus group discussions, participant surveys, study records, web analytics and in-field observations. These sources captured participation patterns and implementation measures (delivery, reach, uptake and engagement: opt-in to digital messages and attendance at training sessions), as well as reasons for refusals and withdrawals. Qualitative data were analysed thematically and organised using the UK Medical Research Council process-evaluation framework. Qualitative and quantitative data were analysed separately and subsequently interpreted collectively to contextualise implementation patterns and identify barriers and enablers that influenced trial successes and failures. RESULTS: Intervention uptake and engagement varied significantly across organisations, with greater success observed in social and faith-based groups. Of the 82 intervention clusters, 78 (95%) received intervention materials; 74 (90%) engaged in at least one activity and 15 (18%) engaged in all activities. Participation was primarily driven by the organisation's leadership interest and support in providing BLS training to members, and by the time available to facilitate intervention activities. The presence of a dedicated liaison/champion emerged as the most critical enabler of member engagement and implementation. Feedback recommended concise, simple and culturally tailored modules, with practical components delivered in shorter, convenient sessions. Intervention delivery was affected by contextual challenges, including COVID-19 disruptions that limited in-field recruitment and group activities. CONCLUSIONS: Process evaluation can strengthen community-based interventions by identifying mechanisms and contextual factors that shape implementation and engagement. Partnering with social and faith-based organisations may be an effective approach to disseminating educational programmes such as life-saving skills to lay communities. Minimising research burden and ensuring organisational leadership support may improve participation while brief, practical and culturally tailored training may enhance engagement. TRIAL REGISTRATION NUMBER: ACTRN12621000367842.
9 Apr 2026
Read appraisal →Journal of the American Heart Association
Patient Care Pathways and Outcomes Following Newly Diagnosed Atrial Fibrillation
BACKGROUND: Care pathways for patients with atrial fibrillation (AF) are poorly understood and may influence the likelihood of guideline concordant oral anticoagulation or antiarrhythmic drug therapy and associated outcomes. METHODS: Adult patients in the Optum Clinformatics Database (2015-2023) with incident AF were identified. Care pathways, by treating clinician specialty (primary care, cardiology, electrophysiology) during each AF-related visit in the 1 year post diagnosis were examined. Cox regression models were used to assess associations between an electrophysiologist visit and AF-related treatments including antiarrhythmic drug therapy and oral anticoagulation and outcomes including AF-related hospitalizations, heart failure hospitalizations, and stroke. RESULTS: Of the 37 370 patients included, 7700 (20.6%) had a care pathway including an electrophysiologist visit. Older patients (70-79 and 80+ versus 18-49 years: hazard ratio [HR], 0.71 [95% CI, 0.64-0.78] and HR, 0.57 [95% CI, 0.51-0.63], respectively) and those with a higher CHA2DS2-VASc score (2-3 and ≥4 versus 0-1: HR, 0.79 [95% CI, 0.74-0.85] and HR, 0.76 [95% CI, 0.68-0.84], respectively) were less likely to have an electrophysiologist visit. Electrophysiologist visits were associated with a significant increase in rates of treatment with antiarrhythmic drug and oral anticoagulation. Patients aged ≥80 years with an electrophysiologist visit had significantly lower risks of stroke compared with those without an electrophysiologist visit (HR, 0.63 [95% CI, 0.42-0.95]). CONCLUSIONS: Certain demographic groups including older patients had lesser likelihood of electrophysiologist consultation, but guideline concordant care was more likely associated with electrophysiology care pathways. Electrophysiology care pathways were associated with improved outcomes especially among the groups with greatest stroke risk.
8 Apr 2026
Read appraisal →Gut
High versus gradually increasing energy nutrition in the early phase of acute pancreatitis (GOULASH): a multicentre double-blind randomised clinical trial
BACKGROUND: Acute pancreatitis (AP) is among the most common gastrointestinal diseases requiring hospitalisation, often with severe outcomes and no disease-specific therapy. Nutritional support has been proven to improve outcome, but little is known regarding optimal timing and composition. OBJECTIVE: This clinical trial aimed to compare high (30 kcal/kg/day, high energy (HE)) versus gradually increasing energy (0 increased to 30 kcal/kg/day over 4 days, low energy (LE)) strategies for enteral nutritional support in AP. DESIGN: This was a multicentric, double-blind, randomised clinical trial, enrolling patients with AP regardless of predicted severity (January 2017 to April 2023). The primary outcome was a combination of mortality and severe acute pancreatitis (Revised Atlanta Criteria); secondary outcomes included severity, rate of infection, organ failure and pain relapse. Interim analysis was planned after 50% enrolment. The Benjamini-Hochberg false discovery rate (FDR) method was used to correct p value for multiple testing. RESULTS: The trial was stopped early after enrolling 636 patients. Interim analysis showed that the primary outcome showed no difference between groups in the modified intention-to-treat (mITT) population (HE: 28/312, 9.0% vs LE: 18/307, 5.7%, p(uncorrected/corrected)=0.19/0.42). Secondary outcomes showed no difference in the mITT analysis. Without correction for multiplicity testing, results favoured a low gradual energy strategy in terms of organ failure (HE: 52/312, 16.7% vs LE: 28/307, 9.1%, p(uncorrected)=0.007) and pain relapse (80/312, 27.1% vs 54/307, 19.0% p(uncorrected)=0.03) but showed no differences between groups after correction for multiple testing (p=0.13 and p=0.23, respectively). It was determined that the superiority of the intervention would not be shown even with an increased sample size, and thus the trial was terminated based on a post hoc decision on ethics and futility. CONCLUSION: Based on this early terminated trial, a high-energy strategy for early nutrition in pancreatitis does not decrease mortality/severity, but potentially increases organ failure and pain relapse rate. TRIAL REGISTRATION NUMBER: ISRCTN63827758.
8 Apr 2026
Read appraisal →JBJS reviews
Treatment Outcomes for Distal Radius Fractures: A Meta-Analysis
BACKGROUND: Distal radius fractures (DRFs) are among the most prevalent in upper-extremity injuries, particularly in older adults and individuals with osteoporosis. The common treatment options include surgical interventions such as volar locking plate (VLP) fixation and percutaneous pinning, as well as conservative strategies like splinting and bracing. However, the comparative effectiveness of these modalities remains unclear. This study aims to compare the functional outcomes and complication rates associated with VLP, percutaneous pinning, bracing, and splinting in the treatment of DRFs. METHODS: A systematic search of PubMed, Google Scholar, and the Cochrane Library (September 2001-February 2025) identified randomized controlled trials, prospective cohort studies, and retrospective cohort studies comparing DRF treatments. Functional outcomes and complication rates (superficial infection, malunion, nonunion, delayed union) were analyzed. Data were analyzed using Review Manager Web with significance set at p ≤ 0.05. RESULTS: Twenty-seven studies involving 3,200 patients met inclusion criteria: VLP fixation (n = 896), percutaneous pinning (n = 666), splinting (n = 718), and bracing (n = 903). No significant differences were observed between VLP and splinting or between bracing and splinting for Disabilities of the Arm, Shoulder, and Hand (DASH); Patient-Rated Wrist Evaluation (PRWE); or grip strength. Compared with VLP, percutaneous pinning demonstrated better PRWE scores but a higher infection rate. Compared with bracing, percutaneous pinning showed significantly better DASH, PRWE, and grip strength outcomes, although with substantial heterogeneity. Compared with bracing, VLP fixation yielded significantly better DASH, PRWE, and grip strength outcomes and a lower malunion rate. CONCLUSION: All 4 treatments achieved acceptable outcomes, but with key trade-offs: Percutaneous Pinning had the best functional results but a higher infection risk. VLP provided better function and lower malunion rates than bracing. Splinting and bracing were effective conservative options, often yielding results similar to VLP, but were inferior in some specific measures. The findings suggest that a perfect anatomic repair is not always necessary for a good recovery. Ultimately, the choice of treatment must be individualized, weighing the functional benefits against the risks of complications, while considering patient health factors and preferences. LEVEL OF EVIDENCE: Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.
8 Apr 2026
Read appraisal →JAMA
Tenecteplase for Acute Non-Large Vessel Occlusion 4.5 to 24 Hours After Ischemic Stroke: The OPTION Randomized Clinical Trial
IMPORTANCE: The efficacy and safety of intravenous tenecteplase in non-large vessel occlusion acute ischemic stroke beyond 4.5 hours after symptom onset remain uncertain. OBJECTIVE: To assess the efficacy and safety of intravenous tenecteplase administered 4.5 to 24 hours after stroke onset in patients with non-large vessel occlusion and salvageable brain tissue. DESIGN, SETTING, AND PARTICIPANTS: This randomized, open-label, blinded end-point clinical trial was conducted at 48 centers in China. A total of 566 patients with non-large vessel occlusion stroke and evidence of potentially salvageable tissue determined on perfusion imaging presenting within 4.5 to 24 hours of the time last seen well were recruited between June 2, 2023, and August 4, 2025 (final follow-up, October 28, 2025). INTERVENTIONS: Patients were randomly assigned 1:1 using a minimization algorithm to receive intravenous tenecteplase (0.25 mg/kg; maximum dose, 25 mg; n = 282) or standard medical treatment (n = 284). MAIN OUTCOMES AND MEASURES: The primary efficacy outcome was an excellent functional outcome, defined as a score of 0 or 1 on the modified Rankin Scale at 90 days. Safety outcomes included symptomatic intracranial hemorrhage within 36 hours and mortality within 90 days. RESULTS: Among the 570 patients randomized, 566 were included in the primary analysis (median age, 68 [IQR, 59-75] years; 196 female [34.6%]). An excellent functional outcome was observed in 123 of 282 patients (43.6%) in the tenecteplase group and 97 of 284 (34.2%) in the control group (risk ratio, 1.28 [95% CI, 1.04-1.57]; P = .02). The incidence of symptomatic intracranial hemorrhage at 2.8% was higher with tenecteplase than with standard medical treatment at 0% (risk difference, 2.85% [95% CI, 1.16%-5.54%]; P = .004), and the mortality at 90 days was 5.0% and 3.2%, respectively (risk ratio, 1.57 [95% CI, 0.69-3.57]; P = .28). CONCLUSIONS AND RELEVANCE: Among patients with non-large vessel occlusion acute ischemic stroke and salvageable brain tissue, intravenous tenecteplase administered 4.5 to 24 hours after onset resulted in a greater likelihood of an excellent functional outcome at 90 days than standard care but had an increased risk of symptomatic intracranial hemorrhage. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05752916.
8 Apr 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Ultrasound-Guided Modified Clavipectoral Fascial Plane Block With Superficial Cervical Plexus Block for Midshaft Clavicular Surgery: A Prospective Randomized Controlled Trial
BACKGROUND This study evaluated the efficacy and safety of modified clavipectoral fascial plane block (mCPB) plus superficial cervical plexus block (SCPB) compared with interscalene brachial plexus block (ISBP) in midshaft clavicular fracture surgery. MATERIAL AND METHODS Fifty-six patients scheduled for midshaft clavicular surgery under regional anesthesia were randomly allocated to a control (ultrasound-guided ISBP plus SCPB) or experimental (mCPB plus SCPB) group at a 1: 1 ratio. The primary outcome was the Numerical Rating Scale (NRS) score at 12 hours postoperatively. Secondary outcomes were NRS scores at 4, 8, and 24 hours; rescue analgesic administration frequency; block success rate; procedural and onset times; and hemidiaphragmatic paralysis incidence. RESULTS Block success was 100% in both groups, without differences in performance time or onset time. A significant group × time interaction was observed (P<0.001). Compared with the control group, the experimental group had significantly lower NRS scores at 12 hours (estimated mean difference -1.29; P<0.001) and 24 hours (estimated mean difference -2.36; P<0.001). No significant differences occurred at 4 or 8 hours. Rescue analgesic requirements within 24 hours were significantly reduced in the experimental group (P=0.002). Hemidiaphragmatic paralysis incidences were 71.4% in the control group and 0% in the experimental group (P<0.001). CONCLUSIONS In midshaft clavicular fracture surgery, mCPB plus SCPB provided effective anesthesia with procedural characteristics comparable to ISBP, while avoiding hemidiaphragmatic paralysis and demonstrating superior analgesia at 12 and 24 hours. These findings require validation in larger multicenter trials.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning
BACKGROUND: Stressor-associated atrial fibrillation (AF) refers to new-onset AF that occurs with a reversible, acute stressor. Identifying individuals at highest risk for AF recurrence is essential to guide management. Although clinical factors have shown limited value, the utility of contemporary artificial intelligence (AI)-enabled models using the 12-lead ECG to estimate recurrence risk remains unknown. METHODS: We retrospectively analyzed consecutive primary care and cardiology patients with stressor-associated AF occurring during hospitalization. We quantified the cumulative incidence of recurrence accounting for death as a competing risk. We investigated the relationship between time-varying recurrence and a composite end point of AF-related adverse events (stroke, heart failure, all-cause death) using Cox models. We then developed and validated a penalized regression model to predict recurrence using clinical factors, stressor type, and AF risk estimates from a previously validated ECG-based AI model. RESULTS: We analyzed 3371 patients with stressor-associated AF (mean age, 69±12 years; 40% women). Over a median of 3.7 years (interquartile range, 1.8-7.2), the 10-year cumulative incidence of AF recurrence was 41% (95% CI, 39-44). AF recurrence was strongly associated with AF-related adverse events (hazard ratio, 2.24 [95% CI, 1.81-2.76]). A model incorporating clinical factors, stressor type, and ECG-based AI model AF risk estimates (clinical-AI) discriminated AF recurrence (area under the receiver operating characteristic curve, 0.768 [95% CI, 0.707-0.830]) favorably compared with clinical features (area under the receiver operating characteristic curve, 0.707 [95% CI, 0.642-0.772]; P<0.05). CONCLUSIONS: AF recurrence rates following stressor-associated AF are considerable and are associated with substantially higher risk of adverse cardiovascular events. Models incorporating ECG-based AI risk estimates may prioritize individuals for intensive monitoring and preventive interventions.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on Behalf of the Stakeholder Taskforce for Artificial Intelligence-Assisted Robotic Thrombectomy (START)
Although we are making progress in overcoming infectious diseases and cancer, one of the major medical challenges of the mid-21st century will be the increasing prevalence of stroke. Occlusions in large vessels are especially debilitating, yet effective treatment-needed within hours to achieve best outcomes-remains limited because of geographic accessibility. One solution for improving timely access to mechanical thrombectomy in geographically diverse populations is the widespread deployment of robotic surgical systems. Artificial intelligence assistance may enable the safe and effective upskilling of operators in this emerging therapeutic delivery approach. Our aim was to establish consensus frameworks for developing and validating artificial intelligence-assisted robots for thrombectomy. Objectives included standardizing effectiveness metrics and defining reference testbeds across in silico, in vitro, ex vivo, and in vivo environments. To achieve this, we convened experts in neurointervention, robotics, data science, health economics, policy, statistics, and patient advocacy. Consensus was built through an incubator day, a Delphi process, and a final position statement. We identified that the 4 essential testbed environments each had distinct validation roles. Realism requirements vary: simpler testbeds should include realistic vessel anatomy compatible with guidewire and catheter use, whereas standard testbeds should incorporate deformable vessels. More advanced testbeds should include blood flow, pulsatility, and disease features, such as atheromatous plaques. There are 2 macroclasses of effectiveness metrics: one for in silico, in vitro, and ex vivo stages focusing on technical navigation (eg, path-following error), and another for in vivo stages, focused on clinical outcomes (eg, modified treatment in cerebral infarction scores). Patient safety is central, and not a barrier, to this technology's development. One requisite patient safety task needed now is to correlate in vitro measurements to in vivo complications.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Impact of Atrial Fibrillation Pattern on Edoxaban Antithrombotic Therapy in Patients With Atrial Fibrillation and Stable Coronary Artery Disease: A Secondary Analysis of the EPIC-CAD Randomized Clinical Trial
BACKGROUND: Paroxysmal atrial fibrillation (AF) is associated with a risk of stroke or systemic embolism similar to nonparoxysmal (persistent or permanent) AF. However, whether the optimal antithrombotic strategy in patients with AF and stable coronary artery disease differs by AF pattern remains uncertain. METHODS: This prespecified substudy of the EPIC-CAD (Edoxaban Antithrombotic Therapy for Atrial Fibrillation and Stable Coronary Artery Disease) trial compared edoxaban monotherapy with dual antithrombotic therapy (edoxaban plus a single antiplatelet agent) in patients with AF and stable coronary artery disease. Stratified analyses were performed by AF pattern. The primary outcome was net adverse clinical events, defined as a composite of all-cause death, myocardial infarction, stroke, systemic embolism, unplanned urgent revascularization, major bleeding, or clinically relevant nonmajor bleeding within 12 months. RESULTS: Among 1040 patients, 575 (55.3%) had paroxysmal and 465 (44.7%) had nonparoxysmal AF. The 12-month primary outcome rate was similar between paroxysmal and nonparoxysmal AF (10.3% versus 12.2%; adjusted hazard ratio [HR], 1.15 [95% CI, 0.79-1.68]). Edoxaban monotherapy was associated with a significantly lower risk of the primary outcome than dual antithrombotic therapy in both paroxysmal AF (4.6% versus 16.2%; adjusted HR, 0.23 [95% CI, 0.12-0.42]) and nonparoxysmal AF (9.3% versus 15.0%; adjusted HR, 0.56 [95% CI, 0.32-0.87]). No significant interaction was observed between antithrombotic strategy and AF pattern (P for interaction=0.10). CONCLUSIONS: In patients with AF and stable coronary artery disease, edoxaban monotherapy significantly reduced net adverse clinical events at 12 months compared with dual antithrombotic therapy, irrespective of AF pattern. REGISTRATION: URL: https://www.ClinicalTrials.gov; Unique identifier: NCT03718559.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time
BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable medication history. We investigated whether routinely available coagulation tests, prothrombin time-international normalized ratio and activated partial thromboplastin time, paired with machine learning can identify anticoagulant exposure at presentation. METHODS: We performed a multicenter diagnostic modeling study using the Japan Stroke Data Bank. Adults with ischemic or hemorrhagic stroke admitted in 2016 to 2021 comprised the derivation cohort; those in 2022 formed a temporally distinct validation cohort. Three models (random forest, extreme gradient boosting, and neural network) used prothrombin time-international normalized ratio and activated partial thromboplastin time as predictors to classify anticoagulant status: vitamin K antagonist, direct oral anticoagulant, or none. Discrimination (area under the receiver operating characteristic curve and area under the precision-recall curve), calibration, and decision curve analysis were assessed. Model outputs were implemented as bedside probability heat maps. RESULTS: Among 30 767 patients (derivation n=24 857; validation n=5910), vitamin K antagonist/direct oral anticoagulant use was 4.5%/8.2% in derivation and 2.8%/9.5% in validation. In the validation cohort, the neural network yielded the best performance: area under the receiver operating characteristic curve, 0.933 (95% CI, 0.908-0.955) and area under the precision-recall curve, 0.52 (95% CI, 0.44-0.61) for vitamin K antagonist; area under the receiver operating characteristic curve, 0.841 (95% CI, 0.822-0.858) and area under the precision-recall curve, 0.39 (95% CI, 0.35-0.43) for direct oral anticoagulant, with preserved calibration and highest net benefit across clinically relevant thresholds. Heat maps summarized class probabilities over the prothrombin time-international normalized ratio×activated partial thromboplastin time space to support rapid inference. CONCLUSIONS: A simple machine learning model using only prothrombin time-international normalized ratio and activated partial thromboplastin time classified vitamin K antagonist and direct oral anticoagulant exposure with high accuracy and practical clinical utility. This tool assists emergency decision-making when medication history is unknown, supporting the safety and timeliness of acute stroke care.
8 Apr 2026
Read appraisal →Journal of Korean medical science
Effectiveness and Cost-Utility of Anti-Adhesion Agents in Intra-Abdominal Surgery Patients
BACKGROUND: Adhesive intestinal obstruction (AIO) is a common and critical complication of intra-abdominal surgery, often requiring preventative measures such as using anti-adhesion agents (AAA). However, despite their prevalence and significance, a notable lack of long-term and large-scale clinical evidence exists supporting the effectiveness and cost-utility of these agents. This study aimed to evaluate the effectiveness and cost-utility of applying AAA in patients undergoing intra-abdominal surgery. METHODS: This is a retrospective observational study utilizing the Korean National Health Insurance Database in combination with the data from the Clinical Data Warehouse collected from six university-affiliated hospitals in Korea. The study included patients who underwent intra-abdominal surgeries between April and December 2016, with 2-year follow-up. The effectiveness of AAA was assessed by the incidence rate of AIO, and cost-utility was evaluated through calculating quality-adjusted life years (QALYs) and direct costs. Subgroup and sensitivity analyses were conducted to confirm the consistency and robustness of the study findings. RESULTS: A total of 2,831 patients were included in the study. Among them, 1,365 (47.2%) received AAA while 1,466 (52.8%) did not. The group that received AAA exhibited a significantly lower incidence of AIO, with a rate of 12.4% compared to 18.5% in the non-user group. Utility scores were higher in the user group, averaging 1.992 QALYs versus 1.986 in the non-user group. Direct costs were significantly lower in the user group, totaling 3,794 USD, which is 1,330 USD less than those incurred by the non-user group. Subgroup and sensitivity analyses reinforced these findings, demonstrating consistent reductions in AIO incidence, increased QALYs, and decreased costs observed across various subgroups, particularly among older patients and those without chronic conditions. CONCLUSION: These findings highlight the clinical effectiveness and cost-utility of AAA in patients undergoing intra-abdominal surgery. By significantly reducing the incidence of AIO and lowering healthcare costs, these agents provide empirical evidence supporting their broader adoption in clinical practice. This study addresses critical gaps in the understanding of postoperative management and offers valuable insights that could inform future healthcare policies.
8 Apr 2026
Read appraisal →BMJ open quality
Feasibility and early outcomes of a multi-site TeleHospitalist admissions programme: staff and patient perspectives from a large health system
BACKGROUND: Staffing shortages contribute to admission delays, fragmented care and hospitalist burnout. Although telemedicine has been widely adopted in critical care and subspecialties, its use in general inpatient admissions remains understudied. This study evaluated the feasibility, operational performance and early perceptions of a TeleHospitalist admissions programme designed to address nocturnal staffing shortages in a large academic health system. METHODS: In October 2024, Houston Methodist launched a nocturnal TeleHospitalist programme. The programme provided centralised coverage via audio-video technology, operating from 18:00 to 02:00 initially and expanding to 18:00-06:00. Adult patients admitted from the emergency department (ED) between October 2024 and August 2025 and clinical staff involved in admission workflows, were surveyed. Operational data on admission timeliness were extracted from the Epic electronic medical record, and patient and staff experiences were evaluated through structured surveys and qualitative feedback. RESULTS: Over 10 months, 1575 TeleHospitalist encounters were completed (median age 65 years (IQR 49 to 75); 56% were ≥65 years). Median hospital length of stay was 38.5 hours (IQR 17.3 to 66.3), and median ED arrival-to-admission time was 7.3 hours (IQR 4.7 to 12.8). Among 311 patient respondents (19.7%; median age 66.5 years (IQR 52.3 to 75.6); 5% were ≥65 years), satisfaction scores were high (median 4 (IQR 3 to 5). Staff responses (n=29) demonstrated strong endorsement: 86% favoured continuation, 79% reported comfort with the technology and 75% rated admission processes as better than traditional workflows. CONCLUSIONS: A system-wide TeleHospitalist admissions programme was feasible and well accepted by patients and staff. Older adults reported positive experiences, suggesting age is not a barrier to virtual inpatient care. Early findings indicate improved timeliness, communication and workflow efficiency while mitigating overnight staffing gaps. Future studies will evaluate long-term outcomes, cost-effectiveness and strategies to enhance privacy and patient engagement.
8 Apr 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings
BACKGROUND Artificial intelligence (AI) is increasingly explored as a clinical decision-support tool in nephrology; however, its real-world applicability for dialysis decision-making in emergency and intensive care unit (ICU) settings remains insufficiently studied. Hemodialysis initiation and prescription are complex, time-sensitive, and dynamic processes that require expert clinical judgment. MATERIAL AND METHODS This retrospective observational study evaluated agreement between AI-generated (ChatGPT) and nephrologist-made dialysis decisions in emergency and ICU settings. Adult patients undergoing first-time dialysis were included. Agreement was assessed for dialysis initiation, modality selection, and key prescription parameters. To ensure clinical relevance, continuous prescription variables were categorized into predefined ranges. Agreement was quantified using Gwet's AC1 coefficient and Cramer's V statistic. RESULTS Eighty-four patients were included. AI demonstrated 100% agreement with nephrologists regarding dialysis initiation. Overall agreement for dialysis modality selection was 92.9% (Cramer's V=0.87, P<0.001). Agreement for core dialysis prescription parameters - including blood flow rate, dialysate sodium, potassium, and calcium concentrations - was high across modalities (all P<0.001). Lower agreement was observed for ultrafiltration-related parameters, particularly ultrafiltration duration, reflecting the individualized and dynamic nature of volume management during dialysis. CONCLUSIONS AI-assisted decision support demonstrated high agreement with nephrologist decisions for initial dialysis initiation, modality selection, and core prescription parameters in emergency and ICU settings. Discrepancies were primarily confined to ultrafiltration-related decisions, underscoring the necessity of ongoing bedside clinical judgment. These findings support the role of AI as a decision-support tool rather than a replacement for clinician-led dialysis management.
8 Apr 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Behavioural nudges to reduce low-value care for low back pain in the emergency department (NUDG-ED): a 2 × 2 factorial, pragmatic cluster randomized trial
BACKGROUND: Low-value care exposes patients to unnecessary risk and wastes scarce health resources. We aimed to determine if patient- or clinician-directed nudges could reduce low-value care for low back pain in the emergency department. METHODS: We conducted a 2 × 2 factorial, cluster randomized controlled trial involving patients with low back pain presenting to emergency departments. Eight emergency departments were randomized to receive patient nudges (6 electronic information posters discouraging unnecessary imaging and opioids, displayed on 55-inch screens in waiting rooms), clinician nudges (3 electronic health record alerts that provided indications for lumbar spine imaging and suggested alternatives to opioids), both patient and clinician nudges, or no nudges. The primary outcome was the proportion of encounters for low back pain with low-value care, defined as non-indicated lumbar spine imaging test, opioid prescription at discharge, or both. We calculated odds ratios (ORs), adjusted for baseline and clustering. RESULTS: There were 3770 encounters for low back pain during the study period. The overall baseline prevalence of low-value care was 41.6%. During the intervention period, the proportion of encounters with low-value care reduced to 36.4% with patient nudges versus 38.1% without, but the difference was not significant (adjusted OR 0.80, 95% confidence interval [CI] 0.51 to 1.27). The proportion of encounters with low-value care was 39.4% with clinician nudges versus 35.0% without (adjusted OR 1.31, 95% CI 0.84 to 2.05). We did not observe an interaction effect between the interventions (p = 0.4). The patient nudge may have reduced strong beliefs among patients in the value of imaging for low back pain. We found no important differences in secondary outcomes. INTERPRETATION: Nudges - including waiting room information posters targeting patients and electronic health record alerts targeting clinicians - did not reduce low-value care in emergency departments. TRIAL REGISTRATION: www.anzctr.org.au, ACTRN12623001000695.
8 Apr 2026
Read appraisal →EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
Ischaemic and bleeding events after complex versus non-complex PCI: a systematic review and meta-analysis
BACKGROUND: Complex percutaneous coronary intervention (PCI) is increasingly performed among patients undergoing myocardial revascularisation. AIMS: We conducted a systematic review and meta-analysis to evaluate the association between complex PCI and the risk of ischaemic and bleeding outcomes. METHODS: Hazard ratios (HRs) were pooled using a random-effects model within a Bayesian framework. The primary analysis was restricted to studies providing adjusted risk estimates, whereas the secondary analysis included unadjusted risk estimates. The primary outcomes were myocardial infarction and major bleeding. The secondary outcomes were all-cause death, stent thrombosis, cardiovascular death, target lesion or vessel revascularisation, and stroke. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework for prognostic studies was used to determine the level of certainty in the association between complex PCI and the risk of events. RESULTS: We included 290,039 patients, of whom 94,633 (33%) underwent complex PCI. Compared with non-complex PCI, patients undergoing complex PCI had a higher risk of myocardial infarction (adjusted HR 1.71, 95% credible interval [CrI]: 1.49-1.96), major bleeding (adjusted HR 1.24, 95% CrI: 1.14-1.35), all-cause death (adjusted HR 1.21, 95% CrI: 1.12-1.32), cardiovascular death (adjusted HR 1.29, 95% CrI: 1.15-1.46), stent thrombosis (adjusted HR 1.76, 95% CrI: 1.49-2.14), target lesion or vessel revascularisation (adjusted HR 1.99, 95% CrI: 1.58-2.50), and stroke (adjusted HR 1.21, 95% CrI: 1.03-1.42). The posterior probability of a higher risk associated with complex versus non-complex PCI was >99% for all study outcomes. Except for stroke (which was low certainty), the certainty of evidence was moderate to high for all other outcomes. Secondary analysis, including unadjusted risk estimates, provided consistent results. CONCLUSIONS: Patients undergoing complex PCI have an increased risk of both ischaemic and bleeding events compared with patients undergoing non-complex PCI (PROSPERO: CRD420250656254).
8 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
AI-derived therapeutic development of a serotonin receptor-targeting drug for the treatment of opioid use disorder
The opioid epidemic has led to a devastating loss of life nationwide. Of those dependent on opioids, many individuals desire to quit or reduce use, but their efforts are often unsuccessful given the powerful reinforcing properties associated with opioid drugs, including fentanyl. Here, we developed a therapeutic drug based on an AI-based platform, which was rationally designed to identify markers of dysregulation from human drug user postmortem brain tissue. Two top candidate compounds, GATC-021 and GATC-1021, were synthesized and validated with in vitro screening for target specificity. Thereafter, the drug candidates were examined for their effectiveness in modulating opioid reinforcement and intake. GATC-1021 emerged as the most efficacious compound as it significantly decreased fentanyl intake in both male and female rats, without any notable side effects. GATC-1021 was further found to modulate gene expression patterns in addiction-relevant brain regions following fentanyl-induced alterations. These findings validate GATC's AI-based platform with its polypharmacy-focused drug development approach and further support the clinical potential of GATC-1021 as a promising therapeutic for those suffering from opioid use disorder.
7 Apr 2026
Read appraisal →PloS one
A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning
Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Implemented in Python, the approach incorporates two datasets: Dataset 1, which consists of Demographic data medical history, and lifestyle data, and the second dataset, which includes the normal condition and the affected stroke condition CT scan images. Imputation of missing values, feature normalization by Min-Max scaling, and handling of imbalanced classes with SMOTE make the data pre-processing procedures exhaustive. In FL architecture three clients -Client A, Client B, and Client C - process a split multimodal dataset containing static and sequential information. Each client independently trains an MLP-GRU model. Each is applied with MLP handling static features from Dataset 1 and GRU handling sequential features from Dataset 2. To update models, Federated Averaging is used on a central server, to create a global model that is then returned to the clients for further refinement. The accuracy of the proposed method averages 99.00% and surpasses other models by 2.5% including CNN, LSTM, Random Forest, and SVM. By enhancing MLP with GRU and applying them to a privacy-preserving FL framework,The study addresses the fragmented use of multimodal medical data, where clinical records and imaging are generally evaluated separately, resulting in inadequate diagnostic support. The strategy integrates complementary modalities to create a more comprehensive perspective of patient health, enhancing healthcare predictive accuracy and decision-making. This incentive is essential for improving computational methods and linking technical advancement with medical objectives like fast diagnosis and therapy planning. The introduction emphasises the therapeutic necessity of harmonising organized and unstructured data to reduce diagnostic ambiguity. A translational approach is used to discuss how multimodal integration might improve clinical workflows, develop collaborative healthcare systems, and support sustainable medical practices. This repeated emphasis links methodological advances to real-world healthcare issues, boosting the study's academic relevance sets.
7 Apr 2026
Read appraisal →Translational vision science & technology
Diagnosis of High Intracranial Pressure by Non-Optic Nerve Retinal Image Features
PURPOSE: To assess the performance of an artificial intelligence deep learning (DL) model compared with neuro-ophthalmologists for the classification of subjects with elevated intracranial pressure (ICP) and papilledema versus control subjects, based on retinal images with and without masking the optic nerve heads (ONHs). METHODS: Widefield retinal images were obtained in 32 subjects (70 images) with elevated ICP and papilledema and 31 control subjects (62 images). ONH-unmasked images were generated by cropping the image to a 30° circular region. ONH-masked images were generated by masking the ONH and peripapillary region. Classification was performed using a convolutional neural network model and by two neuro-ophthalmologists (graders). RESULTS: For the ONH-unmasked images, the classification accuracies of the model, grader 1, and grader 2 were 83% (area under the receiver operating characteristic curve [AUC] = 0.94), 93%, and 86%, respectively. For the ONH-masked images, the classification accuracies of the model, grader 1, and grader 2 were 79% (AUC = 0.90), 66%, and 76%, respectively. The classification performance of the DL model and graders did not significantly differ for both datasets (P ≥ 0.38). There was no significant effect of ONH masking on the performance of the DL model (P = 1.0) and grader 2 (P = 0.38), whereas the performance of grader 1 was significantly reduced (P = 0.02). CONCLUSIONS: Both expert graders and the DL models demonstrated excellent performance for classifying retinal images of subjects with elevated and normal ICP using images with or without masking the ONH. TRANSLATIONAL RELEVANCE: Methods for assessment of non-optic nerve retinal image features have the potential to improve diagnosis and monitoring the progression and response to treatment of elevated ICP.
7 Apr 2026
Read appraisal →Prehospital and disaster medicine
Effectiveness of Video Call-Assisted Versus Voice Call-Assisted Dispatcher-Guided CPR in Untrained Laypersons: A Randomized Simulation Study
INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) remains a major cause of mortality world-wide. Early bystander cardiopulmonary resuscitation (CPR) is a critical determinant of survival; however, many witnessed arrests are managed by untrained laypersons. Dispatcher-assisted CPR (DA-CPR) increases bystander intervention rates, but telephone-based guidance limits real-time assessment of compression quality. Video-assisted CPR (V-CPR) may overcome these limitations by enabling visual feedback and demonstration-based guidance. STUDY OBJECTIVE: The aim of this study was to evaluate whether video call-assisted dispatcher guidance incorporating simultaneous real-time demonstration improves CPR performance quality compared with voice call-assisted guidance in untrained laypersons during a simulated adult OHCA scenario. METHODS: This prospective, randomized, single-blind, manikin-based trial included 85 university students without prior CPR training. Participants were randomized to telephone-assisted CPR (T-CPR; n = 40) or video-assisted CPR (V-CPR; n = 45). All participants performed standardized hands-only CPR for five minutes following dispatcher instructions. In the V-CPR group, the dispatcher simultaneously demonstrated CPR on a manikin during the video call. The primary outcome was the composite CPR Quality Score generated by the manikin feedback system. Secondary exploratory outcomes included compression depth, compression rate, interruption time, and Emergency Medical Services (EMS)-related time intervals. Robust regression analysis adjusted for age, sex, dominant hand, height, and weight was performed. RESULTS: The mean age of participants was 20.13 (SD = 1.81) years, and 54.1% were female. The CPR Quality Score was significantly higher in the V-CPR group than in the T-CPR group (median difference -47; 95% CI, -60 to -36; P < .001). The V-CPR group demonstrated greater mean compression depth, higher proportions of compressions within recommended rate and depth ranges, and shorter interruption times between compressions. The T-CPR group showed shorter time from case recognition to EMS call, while the interval from dispatcher contact to CPR initiation was similar between groups. In multivariable robust regression analysis, allocation to the V-CPR group remained independently associated with higher CPR Quality Score and improved compression performance metrics. CONCLUSION: Video call-assisted dispatcher guidance incorporating simultaneous real-time visual demonstration significantly improves CPR quality in untrained lay rescuers compared with voice-only guidance. These findings suggest that structured visual modeling integrated into DA-CPR systems may enhance bystander resuscitation performance and help bridge gaps in community CPR training.
7 Apr 2026
Read appraisal →Lancet (London, England)
Percutaneous coronary intervention versus coronary artery bypass grafting for unprotected left main stenosis: 10-year final results from the randomised, open-label, non-inferiority NOBLE trial
BACKGROUND: Coronary artery bypass grafting (CABG) is recommended over percutaneous coronary intervention (PCI) for patients with significant unprotected left main coronary artery disease. We aim to provide long-term outcome data comparing PCI with newer generation drug-eluting stents and CABG, which are scarce. METHODS: This previously published, prospective, randomised, open-label, non-inferiority trial enrolled patients with unprotected left main coronary artery stenosis at 36 hospitals in Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Norway, Sweden, and the UK. Eligibility was determined by a multidisciplinary heart team and defined by clinical criteria (chronic or acute coronary syndrome and a life expectancy of >1 year) and angiographic criteria (left main coronary artery diameter stenosis ≥50% or fractional flow reserve ≤0·80 in the left main ostium, mid-shaft, or bifurcation). Patients with ST elevation myocardial infarction within 24 h or considered at too high risk for CABG or PCI were excluded. Patients with angiographically confirmed significant left main coronary artery disease were randomly assigned (1:1) to PCI or CABG using an online system and stratified by site, sex, distal left main coronary artery bifurcation lesions, and diabetes. The primary outcome was the difference in 10-year all-cause mortality in the intention-to-treat (ITT) population, which was analysed using Kaplan-Meier estimates and unadjusted Cox regression. Patients were censored at the date of death, emigration, withdrawal, or loss to follow-up. Variation in all-cause mortality was assessed in prespecified subgroups. The trial is registered with ClinicalTrials.gov, NCT01496651 (active, not recruiting). FINDINGS: From Dec 9, 2008, to Jan 21, 2015, 1201 patients were randomly assigned to PCI (n=598) or CABG (n=603). 17 patients were lost to follow-up before 1 year. 592 patients in each group were included in the ITT population. Mean age was 66·2 years (SD 9·9) in the PCI group and 66·2 years (9·4) in the CABG group. 256 (22%) of 1184 participants were female and 928 (78%) were male. There was no difference in all-cause mortality at 10 years (136 [23%] of 592 in the PCI group and 145 [25%] of 592 in the CABG group; hazard ratio 0·93 [95% CI 0·74-1·18]; p=0·56). No significant difference in all-cause mortality with SYNTAX score was identified. INTERPRETATION: There was no significant difference in all-cause mortality at 10 years between PCI and CABG for patients with unprotected left main coronary artery disease and no additional complex lesions, indicating that PCI is equally as safe as CABG in patients eligible for both treatments. These results will aid heart teams in developing an individualised patient-centred strategy. FUNDING: Biosensors and Aarhus University Hospital.
6 Apr 2026
Read appraisal →Annals of medicine
Immersive virtual reality-assisted anatomy training improves endotracheal intubation performance in simulation: a randomized controlled trial among Chinese non-anesthesiology residents
INTRODUCTION: This study aimed to compare immersive virtual reality (IVR)-assisted versus conventional anatomy training for teaching endotracheal intubation (ETI) to novice non-anesthesiology residents enrolled in China's Standardized Residency Training program. METHODS: A total of 90 non-anesthesiology residents without prior ETI experience were randomly assigned to either an IVR group receiving IVR-assisted anatomy training (n = 45) or a control group receiving conventional anatomy training (n = 45). All participants underwent a standardized teaching protocol. The primary endpoint was residents' ETI performance on a simulator, assessed using both the Global Rating Scale (GRS) and a task-specific checklist. The secondary endpoints included changes in written multiple-choice question (MCQ) scores and residents' evaluations of the course. RESULTS: In practical ETI assessments on a manikin, the IVR group achieved significantly higher scores on the task-specific checklist than the control group (90.34 ± 2.89 vs. 87.20 ± 3.29; p < 0.001), whereas GRS scores were comparable between groups. Both groups showed significant post-training improvement in knowledge scores (p < 0.001), with the IVR group showing a greater gain in theoretical knowledge (54.0% vs. 36.3%; p < 0.001). Participants in the IVR group also expressed a stronger preference for their training method (80.8%) and reported higher levels of motivation, confidence, and enjoyment (all p < 0.05). CONCLUSION: IVR-assisted anatomy training enhances the effectiveness of ETI training for novice non-anesthesiology residents, offering an interactive, engaging, and reproducible approach within China's Standardized Residency Training framework.
5 Apr 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
AI-Assisted Lung Sliding Detection in Point-of-Care Ultrasound by Marine Corps Corpsmen: A Multi-Reader Study
BACKGROUND: Artificial intelligence (AI) has the potential to address training limitations and inter-operator variability that constrain the use of lung ultrasound (LUS) in austere and prehospital settings. This pilot study evaluated whether AI-based decision support could improve the diagnostic accuracy and confidence of United States Marine Corps Corpsmen in identifying absent lung sliding, a key indicator of pneumothorax, during LUS interpretation. METHODS: This pilot-prospective multi-reader, multi-case study involved five military medics, all novices in point-of-care ultrasound, each interpreting 50 de-identified LUS video clips twice, once without AI assistance (control) and once with AI assistance (ATLAS, Deep Breathe Inc., London, Canada), in randomized order with at least a 2-hour washout between sessions. Expert consensus served as a reference standard. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Differences were analyzed using the Random-Reader Random-Case method. Per-clip reader confidence ratings were compared using the Stuart-Maxwell test. RESULTS: AI assistance significantly improved diagnostic performance across all measured outcomes. The mean AUROC increased from 0.72 (SD 0.16) without AI to 0.93 (SD 0.04) with AI (P=.03). Sensitivity rose from 0.63 (SD 0.14) to 0.90 (SD 0.09), specificity from 0.70 (SD 0.15) to 0.86 (SD 0.10), and overall accuracy from 0.67 (SD 0.10) to 0.88 (0.06) (McNemar's test, P<.001). Reader confidence also improved, with high-confidence ratings nearly doubling from 20% to 37%, and low-confidence ratings decreasing from 38% to 33%. These distributional changes were statistically significant (Stuart-Maxwell χ², P<.001). CONCLUSION: AI support markedly improved the diagnostic accuracy and confidence of novice LUS interpretation for detecting absent lung sliding. These findings suggest that real-time AI-based decision support may help improve access to high-quality LUS in military and other resource-limited care settings.
5 Apr 2026
Read appraisal →Medicine
Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms
This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients. Clinical information of ischemic stroke patients admitted to our hospital from January 2023 to December 2024 was retrospectively collected. First, independent risk factors associated with frailty in ischemic stroke patients were identified through univariate and multivariate logistic regression analyses. Subsequently, a nomogram was constructed based on regression analysis results and validated using 10-fold cross-validation. Data were divided into training (70%) and validation (30%) sets. A SVM predictive model was constructed on the training set. The predictive performance of both models was compared using receiver operating characteristic curves. This study included 867 ischemic stroke patients, among whom 296 (34.14%) were classified as frail. The entire cohort was randomly divided into a training set (n = 607) and a validation set (n = 260) at a 7:3 ratio. Logistic regression analysis identified hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive status as independent risk factors for frailty in ischemic stroke patients (P < .05). The nomogram demonstrated an area under the receiver operating characteristic curve (AUC) of 0.814 for frailty prediction, while the SVM model achieved an AUC of 0.842, indicating superior predictive capability. Further comparison revealed that while the nomogram offers greater intuitiveness and operational convenience in clinical practice, the SVM model demonstrated superior predictive accuracy. Hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive states are independent risk factors for frailty in ischemic stroke patients. Both predictive models (nomogram and SVM) demonstrated high predictive accuracy; however, the SVM model outperformed the nomogram in predictive capability, while the latter retains unique advantages due to its simplicity and clinical applicability.
5 Apr 2026
Read appraisal →JAMA health forum
Sources of Variation in Cardiovascular Care Cascades
IMPORTANCE: Variation in cardiovascular care completion is well documented. However, less is known about differences originating from earlier, intermediate stages such as ordering or scheduling of testing or referrals, despite their role as key prerequisites for care access. OBJECTIVE: To examine the care cascades for coronary artery disease (CAD) after emergency department (ED) visits and to identify the specific stages at which variation emerges for CAD testing and cardiology referrals. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study using data and metadata from electronic health records from a large multicenter health system. Participants were adult patients with established primary care and no history of ischemic heart disease or cardiology care who presented to an ED from January 1, 2020, to June 30, 2022, and underwent a troponin test, a proxy for clinically suspected myocardial ischemia. Variation in cardiovascular follow-up care (CAD testing and cardiology referrals) was identified and analyzed. Analyses were restricted to patients with above-median electrocardiogram (ECG)-derived ischemia risk scores to enrich for higher likelihood of benefit from cardiovascular follow-up. MAIN OUTCOMES AND MEASURES: Receipt of an order for CAD testing (stress tests, coronary computed tomography angiography) or cardiology referral, scheduling of the service, and completion within 6 months. Outcomes were compared by insurance type, race and ethnicity, language, and sex using multivariable logistic regression adjusted for demographic characteristics, clinical factors, and ECG-derived cardiovascular risk. RESULTS: Among 16 475 patients with an ED visit (median [IQR] age, 67.4 [54.9-77.9] years; 36% female and 64% male individuals) and elevated cardiovascular risk, marked variation in follow-up care emerged. Compared to commercially insured patients, those with Medicare dual or disabled coverage had lower adjusted odds of completing CAD testing (adjusted odds ratio [aOR], 0.45; 95% CI, 0.36-0.56) and cardiology referrals (aOR, 0.47; 95% CI, 0.39-0.57); similar patterns were seen for Medicaid coverage. Patients whose primary language was not English were less likely to complete either service (CAD testing aOR, 0.77; 95% CI, 0.61-0.98; referral aOR, 0.75, 95% CI, 0.61-0.92), and female patients had lower adjusted odds of completing CAD testing (aOR, 0.86; 95% CI, 0.77- 0.96). Adjusted differences by race and ethnicity were modest. Variation was primarily associated with ordering differences and with additional scheduling barriers for select groups. Once scheduled, completion rates exceeded 75%, without differences between groups. CONCLUSIONS AND RELEVANCE: This retrospective cohort study found that among patients who visited the ED with elevated ischemic risk, attrition in follow-up care was concentrated early in care cascades and most pronounced among those with noncommercial health insurance. This stepwise analytic framework offers a novel, reproducible approach for health systems to identify where and for whom care gaps arise, which can enable targeted interventions to improve equity and efficiency.
5 Apr 2026
Read appraisal →PloS one
Clinical impact of broad- versus narrow-spectrum empiric therapy in acute cholangitis: A Japanese claims database study
The clinical benefit of broad-spectrum empiric therapy in patients with acute cholangitis is unclear. We aimed to evaluate the impact of broad-spectrum and narrow-spectrum empiric therapies on patient outcomes using a Japanese claims database. The study included patients who were diagnosed with acute cholangitis between April 2014 and August 2022, aged 18-99 years, received antibiotics, had blood cultures collected, and underwent biliary drainage. Patients who received empiric therapy with carbapenems, piperacillin/tazobactam, or fourth-generation cephalosporins were included in the broad-spectrum group, whereas others were included in the narrow-spectrum group. Of the 4,755 eligible patients, 3,377 were categorized into the narrow-spectrum group and 1,378 into the broad-spectrum group. In the multivariate logistic regression analysis, older age, higher Charlson Comorbidity Index, the presence of sepsis, and intensive care unit admission were associated with increased 30-day in-hospital mortality, whereas the receipt of broad-spectrum empiric therapy was not (adjusted odds ratio, 1.37 [95% confidence interval {CI}, 0.84-2.23]). In the propensity score matching analysis, there was also no association between the receipt of broad-spectrum empiric therapy and 30-day in-hospital mortality (odds ratio, 1.43 [95% CI, 0.82-2.50]). Furthermore, in the propensity score-matched cohort, the broad-spectrum group had longer duration of intravenous antibiotic therapy (median interquartile range [IQR]: 8 [6 -11] day vs. 9 [7 -13] day; difference 1 day [95% CI, 0.31-1.69 day]) and length of hospital stay (median [IQR]: 13 [9 -20] day vs. 16 [11 -25] day; difference 3 day [95% CI, 1.87-4.13 day]), compared with the narrow-spectrum group. In this large-scale study using a Japanese claims database, broad-spectrum empiric therapy was not associated with improved clinical outcomes, compared with narrow-spectrum empiric therapy. Therefore, the necessity of broad-spectrum empiric therapy may be limited in clinical practice, and narrow-spectrum empiric therapy may represent an effective treatment strategy for acute cholangitis.
5 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Delphi Consensus on Early Neurologic Improvement after Mechanical Thrombectomy
BACKGROUND AND PURPOSE: Early neurologic improvement (ENI) following mechanical thrombectomy (MT) has been a topic of substantial interest. The literature has more than 40 ENI definitions, yet no consensus has been reached. In this Delphi study, we sought to investigate experts' perspectives on the available ENI definitions and determine whether a consensus can be achieved. MATERIALS AND METHODS: The Delphi approach assessed experts' perspectives on available ENI definitions. Following a systematic literature review, the first survey round was prepared and sent to 20 experts using a Web-based anonymous survey platform (qualtrics.com). The first round of questions was publicly shared on the letsgetproof.com platform to gather perspectives of neurointerventionalists worldwide on ENI definitions. Expert and volunteer answers formed the basis of subsequent rounds, in which closed-ended questions were sent only to experts. Consensus was defined as an agreement of ≥70% for binary closed-ended questions and ≥50% for closed-ended questions with >2 response options. RESULTS: Experts completed 3 survey rounds. They recognized that the heterogeneity in ENI definitions is the most critical factor limiting the use of ENI in clinical practice and stroke research. All experts acknowledged that stroke physicians need a standardized universal ENI definition. Consensus was reached on the notion that a unified ENI definition should focus on predicting functional independence at 3 months. They agreed that NIHSS improvement of ≥8 points at 24 hours could serve as the unified ENI definition. They acknowledged that incorporating prestroke comorbidities, occlusion location, baseline NIHSS scores, and procedure-related parameters would complicate the ideal ENI definition and limit its use in clinical settings. The experts could not reach a consensus on the potential utility of ENI as a primary end point in MT trials. CONCLUSIONS: In our Delphi analysis, experts agreed on an ENI definition. They did not endorse using different ENI criteria for patients with comorbidities, posterior circulation occlusions, or high baseline NIHSS scores. Further prospective studies are needed to validate or refine ENI definitions and to address the paucity of data on the prognostic utility of ENI in specific clinical scenarios.
5 Apr 2026
Read appraisal →Biomedical physics & engineering express
Detecting early signs of patient deterioration at home using wearable sensors: a personalized anomaly detection approach
In the rapidly emerging field of transferring healthcare from hospitals to patients' homes, it is essential that signs of remote deterioration at home should be detected early to timely upscale care when needed. As patient deterioration is a rare event, datasets have insufficient event data for supervised machine learning. Personalized anomaly detection (AD) may be a good alternative. Therefore, this study aimed to obtain insight into the use of personalized AD models in detecting early signs of patient deterioration at home using wearable sensor data. To address this, Isolation Forest and Local Outlier Factor models were applied to detect signs of deterioration in terms of mortality or unplanned readmission in the 24 preceding hours in two datasets: one of heterogeneous patients, the other of postoperative patients. A pipeline was developed for continuously updating personalized AD models over time and applying them to detection windows twice per day. Results were compared with the Remote Early Warning Score. Isolation Forest (AUROC: 0.69) and Local Outlier Factor (AUROC: 0.67) models were able to find some early signs of deterioration in heterogeneous patients (n = 113). However, in postoperative patients (n = 193), Isolation Forest (AUROC: 0.41) and Local Outlier Factor (AUROC 0.44) performed badly. The Remote Early Warning Score was able to find some early signs of deterioration for both groups (AUROC: 0.63-0.76). Based on these findings, three requirements were formulated that should be fulfilled for a potentially successful application of personalized AD. First, the training set should be normal and representative of non-deteriorating patients. Second, signs of deterioration should exhibit abnormal characteristics. Third, non-deteriorating patients should not exhibit abnormal characteristics.
5 Apr 2026
Read appraisal →Scientific reports
Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.
Large language models (LLMs) are increasingly applied in clinical contexts; however, to our knowledge, their performance in acute kidney injury (AKI) has not been directly compared with physician performance on standardized clinical knowledge assessments. We conducted a cross-sectional study to evaluate the performance of 13 publicly available LLMs and 123 volunteer participants at the 131st Annual Congress of the German Society of Internal Medicine in Wiesbaden, Germany. Both groups completed an identical AKI knowledge assessment consisting of two case vignettes and 15 single-best-answer multiple-choice questions. LLMs achieved a mean score of 13.5 out of 15 (90%), with several models reaching a perfect score, while human participants averaged 7.3 out of 15 (48.7%). Only 16.3% of participants scored 11 points or higher. As an illustrative example, ChatGPT-4o completed the test in approximately 0.5 minutes, whereas humans required a mean of 7.3 minutes. These findings demonstrate that LLMs substantially outperformed a heterogeneous group of medical professionals in AKI knowledge assessments and did so with markedly greater efficiency. While this highlights the potential of LLMs as rapid, cost-effective tools for clinical knowledge support, their role in real-world patient care remains undetermined, and human clinical judgment remains essential to ensure safe, context-sensitive, and patient-centered care.
5 Apr 2026
Read appraisal →European journal of pediatrics
Facilitator-guided vs self-guided debriefing in immersive virtual reality paediatric emergency training: a randomised pilot study on learning outcomes and feasibility
UNLABELLED: This study evaluated the feasibility and effectiveness of immersive virtual reality (VR)-based paediatric emergency training, comparing facilitator-guided with self-guided debriefing methods. In a single-blinded, randomised controlled pilot trial, 24 healthcare professionals (12 interprofessional dyads) were randomised to VR-based paediatric emergency training followed by either facilitator- or self-guided debriefing. Both groups received a standardised preparatory lecture and skills workshop. We collected data on the feasibility of the interventions and additional trial procedures. For pre- and post-performance assessment, all dyads were video-recorded whilst managing a mannequin-based paediatric emergency. Two independent, blinded raters assessed the dyads' teamwork, ABCDE adherence, and time to critical actions based on the videos. We also collected data on participants' cognitive load and perceived effectiveness of the two debriefing methods. The study demonstrated the feasibility of interprofessional VR-based paediatric emergency training with both facilitator- and self-guided debriefing in a clinical setting. Twenty-two healthcare professionals completed the intervention, and 20 completed follow-up. VR was acceptable and usable, though 46% of participants reported some cybersickness. Additional trial procedures were feasible but required substantial logistical coordination. VR training significantly improved teamwork (facilitator-guided mean 1.2, confidence interval (CI) 0.5 to 1.9, p = 0.005; self-guided mean 1.4, CI 0.8 to 2.0, p < 0.001) and ABCDE adherence (facilitator-guided mean 0.5, CI 0.1 to 1.0, p = 0.02; self-guided mean 0.5, CI 0.1 to 0.9, p = 0.01), with similar outcomes between the debriefing methods. However, participants perceived the facilitator-guided debriefing to be more effective (mean difference 1.4, CI 0.1 to 2.7, p = 0.04). CONCLUSION: Immersive VR-based paediatric emergency training with facilitator-guided and self-guided debriefing was feasible in a pilot context and yielded comparable performance outcomes. A larger, adequately powered trial is warranted to formally evaluate non-inferiority between debriefing methods. WHAT IS KNOWN: • Simulation-based paediatric emergency training improves clinical outcomes, but widespread implementation is limited by logistical challenges and high costs. • Immersive virtual reality (VR) may help overcome these barriers, yet evidence on its feasibility and effectiveness in this context is limited. WHAT IS NEW: • VR-based paediatric emergency training is feasible and effectively improves interprofessional performance. • Self-guided and facilitator-guided debriefing yielded comparable outcomes, highlighting the potential for scalable use.
5 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Reliability of Pseudocontinuous Arterial Spin-Labeling in Ischemic Stroke and Comparison with Other Perfusion Techniques: Systematic Review and Meta-Analysis
BACKGROUND: Pseudocontinuous arterial spin-labeling (pCASL) is a noninvasive, gadolinium-free MRI technique for assessing brain perfusion. PURPOSE: The test-retest reliability of pCASL-derived CBF measurements in ischemic stroke (IS) were evaluated and pCASL metrics were compared with those from DSC, CTP, and PET. DATA SOURCES: Systematic searches were conducted in MEDLINE, EMBASE, and LILACS. Risk of bias and methodologic quality were evaluated using QUADAS-2 and Quality Appraisal for Reliability Studies tools. STUDY SELECTION: Eleven studies, encompassing 383 participants with IS were included. DATA ANALYSIS: Random-effects meta-analysis with inverse variance method and heterogeneity assessed using I 2 statistics were used. DATA SYNTHESIS: No PET studies were identified. Only 1 low-quality, high-risk-of-bias study assessed test-retest reliability, finding no significant differences in repeated absolute CBF measurements over time and excellent reliability across regions. Most studies comparing pCASL with DSC, and all comparing pCASL with CTP, had a low risk of bias. In the hyperacute phase of IS, pCASL and DSC showed no significant differences in infarct core relative CBF (95% CI, -0.11 to 0.13; I 2 = 0%) or hypoperfusion volumes (95% CI, -0.28 to 0.68; I 2 = 69%). Evidence on the comparability of pCASL with other DSC or CTP metrics was limited. LIMITATIONS: The small number of included studies prevented a meta-analysis for the primary outcome and limited conclusions for secondary outcomes. CONCLUSIONS: Robust conclusions about the test-retest reliability of pCASL or the comparability of metrics obtained with this technique and DSC, CTP, or PET in different phases after IS could not be drawn.
5 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Flow Diversion versus Coiling with or without Stenting for Unruptured Wide-Neck Intracranial Aneurysms: A Randomized Comparison
BACKGROUND AND PURPOSE: There are few randomized trials comparing coiling (with or without stent placement) and flow diversion (FD) for the treatment of wide-neck unruptured intracranial aneurysms. MATERIALS AND METHODS: EVIDENCE was an investigator-led randomized (1:1) trial conducted in 7 French university hospitals. Patients with 7 to 20-mm intradural unruptured intracranial aneurysms with a 4- to 10-mm neck and a "dome/neck" ratio of ≥1 were randomly allocated to coiling with or without adjunctive stent placement or FD alone. The composite primary efficacy outcome was "treatment failure," defined as initial failure to treat the aneurysm; aneurysm rupture or retreatment during follow-up; death or dependency (mRS> 2); or an angiographic residual aneurysm adjudicated by an independent core laboratory at 12 months. The primary hypothesis (revised for slow accrual) was that FD would decrease treatment failures from 35% to 10%, requiring 90 patients. Primary analyses were intent to treat. RESULTS: Among the 91 enrolled patients, four (two in each group) withdrew consent; 87 patients were included in the analysis: 43 in the FD group and 44 in the control group. Most patients had <10 mm (57/87; 65.5%) asymptomatic ophthalmic aneurysms (75/87, 86.2%). A poor primary outcome, ascertainable in 86 patients, was reached in 8/43 patients with FD (18.6%; 95% CI, 9.7%-32.6%) compared with 10/43 coiling patients (23.3%; 95 CI, 13.1%-37.7%) (RR = 0.80; 95% CI, 0.35-1.83; P = .60). Serious adverse events were similar. CONCLUSIONS: For patients with mostly unruptured, wide-neck ophthalmic aneurysms of <10 mm, FD was not superior to coiling with or without stent placement.
5 Apr 2026
Read appraisal →Open heart
Women's IschemiA TRial to Reduce Events In Non-ObstRuctive CAD (WARRIOR): a randomised controlled trial
IMPORTANCE: Women with angina due to suspected ischaemia referred for coronary angiography often have no obstructive coronary artery disease (ANOCA/INOCA). OBJECTIVE: To determine if intensive medical treatment (IMT) reduces major ischaemic events (major adverse cardiovascular event, MACE) among women with suspected ANOCA/INOCA. DESIGN: Randomised, prospective, blinded-outcomes evaluation. SETTING: 71 sites in the USA. PARTICIPANTS: 2476 women with suspected ANOCA/INOCA. INTERVENTIONS: IMT-high intensity statin, ACE inhibitor (ACEi) or angiotensin receptor blocker (ARB) and aspirin versus usual care (UC). MAIN OUTCOMES AND MEASURES: Primary: all cause death, myocardial infarction, stroke/transient ischaemic attack, hospitalisation for angina or heart failure (MACE). Secondary: components of the primary, quality of life and win ratio. RESULTS: Recruitment was lower than planned (n=2476), yielding an aged population (mean, 64 years) with well-controlled blood pressure and low-density lipoprotein cholesterol at baseline, and relatively high rates of statin and ACEI/ARB use. At 2.5 years, 421 events occurred (221 in IMT, 200 in UC) with no difference in the primary outcome (HR=1.13 (95% CI 0.94 to 1.37) for IMT vs UC, p=0.20) or secondary outcomes. Hospitalisations for angina were the dominant contributor to MACE. Sensitivity analysis of contamination provided an estimated HR for IMT versus UC of 0.74 95% CI (0.352 to 1.558), p=0.43. CONCLUSIONS AND RELEVANCE: Among women with suspected ANOCA/INOCA, outcomes were dominated by chest pain and IMT did not improve outcomes, although limited power precludes concluding that it may not be helpful. The findings support the need for more investigation in this population with high burden of angina hospitalisation, health resource consumption and poor quality of life. TRIAL REGISTRATION NUMBER: NCT03417388.
4 Apr 2026
Read appraisal →BMJ paediatrics open
Mapping the global prevalence and socioecological drivers of child sexual abuse: a systematic review and synthesis
INTRODUCTION: Child sexual abuse (CSA) is a widespread global health concern with lasting impacts. CSA is multifactorial and layered, and interventions to tackle CSA require an analysis of the key contributing factors. The main objective of this study was to explore global prevalence estimates of CSA and key contributing factors. METHODS: We systematically searched PubMed, Scopus and Web of Science for studies reporting lifetime prevalence of contact (eg, inappropriate touching or sexual acts) and non-contact (eg, exposure to sexual content or ill-intended conversations) CSA. Of 5493 articles screened, 332 met inclusion criteria. Prevalence data were stratified by population, region and abuse type. Risk factors were synthesised thematically using a socioecological framework. RESULTS: Median prevalence of reported non-contact CSA was 57.0% (range: 31.1%-80.0%). Contact CSA prevalence was higher among females (median: 14.9%, range: 2.2%-40.2%) than males (median: 6.0%, range: 0.6%-21.2%). Regional variations were notable, with sub-Saharan Africa reporting the highest contact CSA prevalence (median: 25.5%) and Latin America and the Caribbean the lowest (median: 8.5%). Vulnerable groups, including Indigenous children (median: 39.2%), orphans (37.9%) and the incarcerated (31.1%), faced higher risk. Risk factors spanned individual (shame, stigma), familial (poor supervision, dysfunction), institutional (lack of safeguards) and societal (weak legal frameworks, normalisation of abuse) levels. CONCLUSIONS: Prevalence of CSA was globally high, with 14 in 100 females reporting having experienced CSA. Multilevel interventions, particularly strengthening family supervision and institutional safeguarding, legal accountability and community awareness, are critical for the prevention of CSA.
4 Apr 2026
Read appraisal →Critical care explorations
Physician Gestalt Compared With Artificial Intelligence Model to Predict Intubation in Critically Ill Patients
IMPORTANCE: Accurate prediction of intubation in critically ill patients could enable interventions that improve patient outcomes. However, the performance of intensive care physicians compared with machine learning (ML) models remains unknown. OBJECTIVES: To investigate intensive care physicians' ability to predict the need for intubation within 24 hours and compare their performance against an established ML model, Vent.io. DESIGN, SETTING, AND PARTICIPANTS: This prospective observational study in two ICUs surveyed intensivists to test their ability to predict the need for intubation of adult patients under their care. Physician predictions of intubation were then compared with predictions from Vent.io. MAIN OUTCOMES AND MEASURES: Primary metrics include prediction sensitivity, specificity, and descriptive statistics for both physicians and ML model. Generalized linear mixed models investigated the fixed effect of the predictor (physician vs. Vent.io) on both sensitivity and specificity while accounting for the random effects from different physicians. Similar modeling was used to investigate the relationship between physician confidence and correctness. RESULTS: Overall, physicians were quite confident in their predictions of intubation with a median score of 8 (on a 0-10 point scale, with 0 being not at all confident and 10 being extremely confident) out of the 302 surveys administered. Sensitivity was 0.190 and 0.714 for physicians and Vent.io, respectively. Specificity was 0.960 and 0.673 for physicians and Vent.io, respectively. Generalized linear mixed modeling showed that physician confidence was associated with greater odds of correctly predicting intubation outcome (odds ratio [OR] 1.49; 95% CI, 1.22-1.84; p < 0.001). Vent.io had significantly greater odds of being correct when patients required intubation compared with physicians (OR 18.68; 95% CI, 1.87-186.31; p = 0.013). However, intensive care physicians outperformed Vent.io at correctly predicting when patients did not require intubation (OR 24.80; 95% CI, 13.22-46.52; p < 0.001). CONCLUSIONS AND RELEVANCE: Although predictive performance compared with human experts is promising, Vent.io needs real-time testing in a randomized clinical trial to determine if its deployment can improve clinical outcomes.
4 Apr 2026
Read appraisal →Journal of neurology
Sonographic optic nerve sheath diameter measurement for detecting intracranial hypertension in a suspected population: a systematic review and meta-analysis of diagnostic thresholds and influencing factors
OBJECTIVES: In this study, we aimed to assess the clinical efficacy of sonographic optic nerve sheath diameter (ONSD) measurements for detecting intracranial hypertension (ICH). Our objectives included evaluating its diagnostic precision compared with invasive intracranial pressure (ICP) monitoring; exploring potential sources of heterogeneity, such as measurement technique (multi vs. single-planar), population demographics (Asian vs. non-Asian), and major etiologies; and determining validated thresholds for prompt bedside ICP assessment in acute neurological crises. DATA SOURCES: We comprehensively searched eight databases, including four in English (PubMed, Web of Science, MEDLINE®, and Embase) and four in Chinese (China National Knowledge Infrastructure [CNKI], Wanfang, VIP, and SinoMed). The search covered literature published from the inception of the databases up to May 31, 2025. STUDY SELECTION: This review includes prospective, retrospective, and case-control studies that assess the efficacy of sonographic ONSD measurement for detecting ICH, defined as an ICP exceeding 20 mm Hg, 20 cmH2O, 25 cmH2O with invasive ICP measurement as the gold standard. DATA EXTRACTION: X.T. and Q.T. independently screened the studies and assessed the risk of bias using the QUADAS-2 tool. STATISTICAL ANALYSIS: Diagnostic accuracy was pooled using a bivariate random-effects model to generate summary receiver operating characteristic (SROC) curves. Subgroup analyses of the measurement technique, study population, and etiology were conducted. Publication bias was assessed using Deeks' test. DATA SYNTHESIS: The decision tree analysis revealed that an ONSD ≥ 5.1 mm is the critical diagnostic threshold for detecting intracranial hypertension. The regression tree model indicated a range of CDEI values from 0.64 to 0.95, with a root node value of 0.82 (95% CI: 0.79-0.85). The incorporation of a multiplanar assessment significantly enhanced the prediction of favorable outcomes (CDEI > 0.85; p < 0.001). Moreover, compared with a 20 mmHg threshold, a 25 cmH₂O threshold resulted in greater specificity (88.4% vs. 82.9%) and a higher positive likelihood ratio (LR+ 6.5 vs. 5.0). The utilization of multiplane techniques and experienced operators notably improved diagnostic accuracy, leading to a 50% reduction in missed diagnoses (LR- 0.1). Nontraumatic brain injury cases exhibited superior sensitivity (91.9% vs. 85.2%) and a 107% higher diagnostic odds ratio (63.3 vs. 30.5) than traumatic brain injury cases did. CONCLUSIONS: Findings from this study suggest that an ONSD threshold ≥ 5.1 mm is a promising diagnostic marker for elevated ICP (> 20 cmH₂O/mmHg or acute etiology), although external validation is needed. A multiplane evaluation improves its predictive accuracy for favorable outcomes. The developed decision model supports the use of sonographic ONSD measurement as a supplementary screening tool in emergency and critical care, particularly when invasive monitoring is unavailable. Additionally, an ONSD of approximately 6.0 mm is a potential prognostic indicator for unfavorable outcomes, highlighting a key area for future research. These findings can only be applied after their validation through multicenter studies and the establishment of standardized measurement protocols.
4 Apr 2026
Read appraisal →Journal of special operations medicine : a peer reviewed journal for SOF medical professionals
Combat-Related Orthopedic Trauma in the Russo-Ukrainian War: A Systematic Review
INTRODUCTION: The Russo-Ukrainian conflict has produced large numbers of military and civilian casualties, with orthopedic and musculoskeletal trauma representing a major proportion of combat injuries. Explosions, gunshots, and blast mechanisms frequently generate complex wounds that demand specialized surgical and rehabilitative management. This review summarizes injury types, mechanisms, severity, clinical responses, and gaps in current knowledge. METHODS: Following PRISMA guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and MEDLINE via WoS was conducted in February 2025. Eligible studies published from 2014 to 2024 included case reports, case series, cohort studies, and observational research describing orthopedic injuries in military or civilian populations affected by the conflict. Two reviewers independently screened studies, performed quality assessments using Joanna Briggs Institute tools and STROBE criteria, and synthesized data qualitatively with descriptive statistics when available. RESULTS: Thirty-one studies were included, primarily case reports and retrospective analyses from Ukrainian military hospitals and frontline facilities. Explosive and ballistic trauma produced high-energy fractures, traumatic amputations, severe soft-tissue loss, and neurovascular damage. Management strategies included external and internal fixation, bone grafting, microsurgical reconstruction, distraction osteogenesis, and the progressive use of 3D-printed implants to support limb salvage. Rehabilitation, including pain control, physiotherapy, and prosthetic training, was essential for functional recovery. Long-term complications included neuromas, phantom pain, stump morbidity, and psychological trauma. Telemedicine enabled remote consultation and continuity of care under resource-limited conditions. CONCLUSIONS: Combat-related orthopedic injuries in this conflict require advanced surgical techniques, coordinated multidisciplinary support, and prolonged rehabilitation. Expanded data collection, standardized reporting, and long-term follow-up are essential to improve functional outcomes and reintegration.
4 Apr 2026
Read appraisal →Current opinion in anaesthesiology
Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients
PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a predictive, personalized approach. This review examines how artificial intelligence and machine learning are redefining postoperative management by predicting complications before they manifest. RECENT FINDINGS: Recent literature (2023-2025) highlights three major advances: (a) the validation of gradient boosting algorithms (e.g. Extreme Gradient Boosting) that significantly outperform traditional scoring systems for predicting trauma-induced coagulopathy; (b) the development of interpretable, phenotype-specific models for venous thromboembolism risk stratification, particularly in traumatic brain injury; and (c) the emergence of real-time sepsis prediction tools that account for the sterile inflammation inherent to trauma. However, a recurring limitation in current research is the reliance on retrospective datasets and single-center validations, underscoring the critical need for rigorous external validation across diverse patient populations before widespread clinical adoption. SUMMARY: Artificial intelligence is not merely a monitoring tool but a driver of precision medicine in trauma. By leveraging diverse modalities, from computer vision in radiology to natural language processing in electronic health records, clinicians can now anticipate adverse events. To bridge the gap between algorithm and bedside, future efforts must focus on overcoming significant implementation barriers, such as data interoperability, and ensuring model generalizability.
4 Apr 2026
Read appraisal →Ultrasound in medicine & biology
From Recognition to Action: Integrating Deep Learning and Robotic Control in Transthoracic Echocardiography
Population aging has driven a rise in heart failure cases, increasing the clinical burden on cardiac diagnostics. As a first-line imaging method, transthoracic echocardiography (TTE) faces limitations due to operator dependence, patient variability, and workflow inefficiencies. Meanwhile, advances in artificial intelligence (AI) and robotic ultrasound systems offer new potential pathways toward automated diagnosis. This review examines the current landscape of AI-based image analysis and robotic-assisted echocardiography. It presents a detailed analysis of advancements in artificial intelligence (AI) applied to echocardiography and the evolution of robotic ultrasound systems, aiming to introduce a discussion on semantic-to-motion mapping. By synthesizing recent progress and outlining future directions, we can correctly recognize the current maturity level of artificial intelligence development in the field of ultrasound examination and prepare well for the subsequent work.
4 Apr 2026
Read appraisal →IEEE transactions on bio-medical engineering
Explainable Prediction of ICU Transfer in Acute Pancreatitis: A Neural Network Model With SHAP Interpretability for Clinical Decision Support
OBJECTIVE: Acute pancreatitis (AP) is a life-threatening disorder commonly observed in emergency departments. Patients with acute pancreatitis may necessitate transfer to the intensive care unit (ICU) if standard treatments prove ineffective. The development of a machine learning (ML) model that can precisely forecast the necessity for ICU admission in patients with acute pancreatitis might dramatically enhance in-hospital healthcare for patients. METHODS: For this study, data were extracted from the MIMIC-III Clinical Database Care-Vue subset (MIMIC-III v 1.4), Medical Information Mart for Intensive Care-IV (MIMIC-IV v 3.1), and eICU Collaborative Research Database (v 2.0). Fifteen features were identified through Boruta's algorithm, Least Absolute Shrinkage and Selection Operator (Lasso), and Recursive Feature Elimination (RFE) utilizing the XGBoost. RESULTS: Seven machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network (NNET), etc. were developed based on these features. The neural network approach demonstrated the highest reliability, attaining an AUC of 0.891, sensitivity of 0.852, and specificity of 0.767 in the test set. Meanwhile, an interpretability assessment of the most robust models was performed utilizing SHapley Additive exPlanations (SHAP) to prioritize feature importance and elucidate the final model. This model has been deployed on a web platform: https://icupredict.shinyapps.io/shiny6/. CONCLUSIONS: The neural network model effectively predicted whether patients with AP would be transferred to the ICU, demonstrating optimal performance. SIGNIFICANCE: We developed an interpretable ML model for prediction of ICU admission in AP patients. It provides clinicians with a reliable tool for early identification and treatment.
4 Apr 2026
Read appraisal →BMJ open
Use of ambient AI scribe in physicians' clinical documentation: a protocol for a systematic review on effectiveness, efficiency, and satisfaction
INTRODUCTION: Clinical documentation is a significant driver of burnout among physicians. Ambient artificial intelligence (AI) scribes, which leverage generative large language models to automate the creation of clinical notes from patient-physician conversations, are rapidly emerging as a potential solution. While these tools promise to enhance efficiency and reduce administrative tasks, concerns about the quality, accuracy and potential biases persist. There is now a need for a systematic synthesis of evidence to evaluate the impact of these technologies in clinical practice. To assess the effects of ambient AI scribes on physicians' clinical documentation, the specific objectives are to: (1) evaluate the effectiveness of these tools on documentation, including accuracy and completeness; (2) synthesise evidence on the impact on physician efficiency after adoption, including time spent on documentation and (3) examine physicians' satisfaction with these tools, including physicians' perceived burden. METHODS AND ANALYSIS: A systematic review of quantitative or mixed-method studies as well as preprints will be conducted. We will perform a comprehensive search of four electronic databases (PubMed, IEEE Xplore, APA PsycInfo and Web of Science, along with medRix and ClinicalTrials.gov for preprints) for empirical studies published between January 2023 and March 2026. The review will synthesise studies comparing physicians' use of ambient AI scribes with traditional documentation approaches. Given the anticipated heterogeneity of the studies, a narrative synthesis will be employed to summarise the findings. Where common quantitative outcomes exist, effect sizes will be calculated using Hedges' g, mean differences or risk ratios/odds ratios as appropriate. The overall quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. ETHICS AND DISSEMINATION: As no patient data are involved in the data collection, no ethical approval is acquired. Results will be disseminated in a peer-reviewed, open-access journal, and presented at relevant academic conferences. PROSPERO REGISTRATION NUMBER: CRD420251149086.
4 Apr 2026
Read appraisal →Current opinion in anaesthesiology
Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia
PURPOSE OF REVIEW: To synthesize recent advances in intraoperative resuscitation for trauma surgery, including fluid composition, transfusion thresholds, and coagulopathy management, and to identify emerging directions that address persistent gaps in precision. Particular attention is given to prospective evidence, as forward-looking evaluations better capture real-world performance, workflow integration, and clinical impact, culminating in the integration of artificial intelligence and machine learning as the next stage of innovation. RECENT FINDINGS: Major trials over the past 5 years have refined evidence for balanced transfusion ratios, restrictive transfusion thresholds, and the transition from crystalloid to hemostatic and physiology-guided strategies. However, outcome variability underscores the need for individualized, data-driven resuscitation. Early prospective machine learning evaluations - along with the US Food and Drug Administration-cleared point-of-care systems such as APPRAISE-HRI - demonstrate that multimodal physiologic modeling can predict hemorrhage and fluid responsiveness as accurately as expert clinicians, while offering real-time monitoring and guidance. SUMMARY: The field is shifting from protocol-based to precision-guided trauma resuscitation. Future progress will rely on randomized-controlled-trial-validated, adaptive, human-in-the-loop artificial intelligence workflows that integrate continuous physiologic data with clinical judgment to enhance the timing, composition, and safety of intraoperative fluid and transfusion therapy.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis
BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making in this population. Recently, with growing attention paid to machine learning (ML) in stroke care, some researchers have investigated the effectiveness of ML in predicting the mortality risk in stroke. However, systematic evidence is still lacking for its effectiveness. OBJECTIVE: This systematic review aims to evaluate the value of ML in predicting the stroke mortality risk. The findings are expected to offer an evidence-based basis for developing and assessing clinical risk prediction tools. METHODS: A search was made in Cochrane Library, PubMed, Embase, and Web of Science up to June 23, 2025, and studies that reported a complete performance of ML in predicting stroke mortality were included. Studies with only risk factors analyzed were excluded. The risk of bias of the included studies was assessed using PROBAST (Prediction model Risk of Bias Assessment Tool). Pooled risk ratios with 95% CIs and prediction intervals (PIs) were derived using the Hartung-Knapp-Sidik-Jonkman method under a random-effects model. Subgroup analyses were also conducted by model type, stroke type, patient source, and treatment background. Moreover, a metaregression was conducted on the C-index for out-of-hospital mortality at different time points to explore the influence of time factors on the model's predictive performance. RESULTS: Sixty-eight studies were included (23 predicting in-hospital mortality and 45 predicting out-of-hospital mortality), describing the development of 75 prediction models and 43 external validations. The follow-up period was 1 month to 15 years. For predicting in-hospital mortality, the external validation set had a pooled C-index of 0.727 (95% CI 0.677-0.781, 95% PI 0.521-1.000), with sensitivity and specificity of 0.64 (95% CI 0.57-0.70) and 0.74 (95% CI 0.70-0.77), respectively. For predicting out-of-hospital mortality, the pooled C-index was 0.847 (95% CI 0.808-0.887, 95% PI 0.750-0.956) in the external validation set, with sensitivity and specificity of 0.71 (95% CI 0.55-0.82) and 0.76 (95% CI 0.74-0.78), respectively. Comparatively, the overall pooled C-indexes were 0.788 (95% CI 0.766-0.810, 95% PI 0.621-0.999) and 0.812 (95% CI 0.798-0.826, 95% PI 0.693-0.952), respectively. The metaregression revealed a gradual decline in the predictive performance of the overall model and logistic regression model alone, whereas a random forest model maintained sustained performance. Age, National Institutes of Health Stroke Scale score, and stroke-related complications were the most frequently used variables for modeling. CONCLUSIONS: This is the first meta-analysis to demonstrate that ML-based prediction of stroke mortality is feasible. The performance of ML supports its role as an auxiliary tool for identifying high-risk populations, thereby optimizing clinical monitoring and resource allocation. However, due to substantial heterogeneity and a relatively high risk of bias in available studies, caution is warranted in real-world application. The effectiveness of ML may vary across settings, and external validation is recommended before broader implementation. TRIAL REGISTRATION: PROSPERO CRD420251086321; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251086321.
4 Apr 2026
Read appraisal →Heart rhythm
Machine learning-enabled assessment of risk of drug-induced QT prolongation at the time of prescribing
BACKGROUND: Many medications are associated with long QTc. Current long QTc predictors have limited generalizability and/or modest performance. OBJECTIVE: This study aimed to compare the performance of machine learning-enabled approaches to drug-induced long QTc prediction with current risk scores, including Tisdale and RISQ-PATH. METHODS: We identified patients who began QTc-prolonging medications (QTdrugs) and had follow-up 12-lead electrocardiograms (ECGs) within 1 year. Using 5-fold cross-validation, we trained XGBoost (XGB), an ECG-based deep neural network, and combined models using electronic health record data and ECG traces to predict QTc of ≥500 ms within 1 year. We assessed performance using the area under the receiver operating characteristic (AUROC) curves and compared them with corresponding Tisdale and RISQ-PATH scores. RESULTS: QTdrug records were identified for 345,371 patients, 5.7% of whom had long QTc within 1 year. In the subset with baseline ECGs available (N = 182,448; 7.7% events), both the XGB and deep neural network models demonstrated high performance (AUROC 0.869 and 0.864, respectively) but their combination yielded no significant improvement (AUROC 0.874). Therefore, focusing on the XGB model, we observed superior performance vs RISQ-PATH in the overall population (AUROC 0.859 vs 0.701), as well as Tisdale in predominantly inpatients (N = 110,558; 8.8% events; AUROC 0.855 vs 0.770). Positive predictive value was 61.5% vs 28.35% and 54.0% vs 28.3% at equivalent operating points for the XGB model vs RISQ-PATH and Tisdale, respectively. CONCLUSION: Development and retrospective validation of 3 machine learning-based models for predicting drug-induced long QTc at the time of new QTdrug starts demonstrated superior performance compared with current clinical risk calculators and may be useful tools to support medical decision making when initiating new therapies.
4 Apr 2026
Read appraisal →Physiological measurement
Deep learning approach for stroke volume variation estimation: retrospective modeling and prospective deployment in real-world practice
Objective. We developed stroke volume variation (SVV) Net, a deep learning-based model for estimating SVV, and validated its performance and clinical applicability through both large-scale retrospective modeling and prospective real-time deployment within intraoperative settings.Approach. A SVVNet was trained on arterial blood pressure (ABP) waveforms from 2849 patients undergoing general anesthesia at Asan Medical Center (AMC). For retrospective validation, we utilized AMC cohorts and a publicly accessible database to assess agreement, trending ability, noise robustness (quantifies via signal quality index), and inter-individual deviation against ABP-based pulse wave analysis (ABP-PWA) and other DL models. For prospective validation, the optimized model was deployed onto a tablet and prospectively evaluated in real-time during 67 surgeries.Main results. In the retrospective external validation, SVVNet achieved higher agreement mean absolute error (MAE = 0.70, Pearsonρ= 0.92), compared to ABP-PWA algorithm (MAE = 1.30, Pearsonρ= 0.80) and lower inter-individual deviation. It was more robust to signal noise (MAE = 4.33 vs 30.87). In the prospective real-time deployment, our deployed model achieved an MAE of 1.33 and a Pearson correlation of 0.87 against the ground truth.Significance. This study emphasizes the advantages of the DL-based model compared to ABP-PWA-based algorithms and presents prospective results that support the clinical applicability of SVVNet in the operating room. Our model has the potential to replace pre-existing monitoring systems and to enhance the complex surgical environment.
4 Apr 2026
Read appraisal →Respiratory medicine
A review of artificial intelligence-based research on chronic obstructive pulmonary disease.
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three major chronic diseases, has achieved remarkable progress in diagnosis, grading, and prognosis, which is of great significance for promoting the clinical transformation of respiratory diseases. To deeply explore the application of AI in the diagnosis and management of COPD, this paper reviews recent studies based on machine learning and deep learning, covering screening and diagnosis, disease grading and assessment, disease management and monitoring, and treatment. First, the technical basis of COPD-related research is analyzed from five perspectives: traditional research methods, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Then, the commonly used datasets and model evaluation metrics are summarized. Finally, the application scenarios of AI in COPD research are elaborated, focusing on three aspects: early screening and diagnosis, disease monitoring and risk prediction, and disease classification and risk stratification. This paper summarizes the main research hotspots at home and abroad over the past five years with representative models, and analyzes and compares the advantages and limitations of each type of model in specific COPD tasks through comparative experiments. The study also outlines prospects for the future development of this field, aiming to provide theoretical references and insights for subsequent research.
4 Apr 2026
Read appraisal →Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting
Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.
4 Apr 2026
Read appraisal →Critical care clinics
Innovation, Technology, and Telemedicine in Critical Care
Critical care medicine is undergoing a major transformation driven by rapid technological innovation, digital integration, and telemedicine. This article examines how modern intensive care units (ICUs) are evolving into data-rich, precision environments equipped with advanced monitoring, organ support, and predictive analytics. Innovations such as artificial intelligence-enhanced decision support, portable ultrasound, and tele-ICU platforms are expanding diagnostic accuracy, remote access, and timely interventions. By tracing historical milestones to emerging technologies like smart ICUs and wearable biosensors, this article highlights the shift toward anticipatory, personalized care. It underscores the importance of evidence-based adoption to ensure these tools improve outcomes.
4 Apr 2026
Read appraisal →PLoS medicine
Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial
BACKGROUND: Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than there are available ambulances are common. Machine Learning (ML) techniques offer a promising but largely untested approach to assessing relative risks among these patients. The study aims to establish whether the provision of ML-based risk scores predicting patient outcomes improves the ability of dispatchers to identify patients at high risk for deterioration in RCS and dispatch the first available ambulance to them. METHODS AND FINDINGS: We performed a parallel-group, randomized trial of adult patients assessed by a dispatch nurse at two study sites in Sweden as requiring a low-priority ambulance response in RCS. Patients were randomized 1:1 to be prioritized with the aid of an ML-based risk assessment tool, or per current clinical practice. The primary outcome was defined in terms of whether the first available ambulance was sent to the patient with the highest National Early Warning Score (NEWS 2) based on subsequently collected vital signs. A total of 1,245 RCS were included in the study. In the intervention arm, 68.3% of RCS were assessed correctly per the primary outcome versus 62.5% in the control group, corresponding to an odds ratio of 1.28 (95% CI [1.00, 1.63], p = 0.047). This study was limited to only patients determined to require a low-priority ambulance response in two Swedish regions, and was underpowered for the primary outcome due to a smaller than expected sample size. CONCLUSION: This study suggests that clinical ML-based decision support tools may have the ability to influence care provider decisions and improve their capacity to rapidly differentiate between high- and low-risk patients at dispatch. Further research should establish the suitability of these tools in larger cohorts, for patients with both higher- and lower-levels of priority, and in other settings. The trial was registered at ClinicalTrials.gov (NCT04757194).
4 Apr 2026
Read appraisal →AJNR. American journal of neuroradiology
Multimodal CT Perfusion-Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios
BACKGROUND AND PURPOSE: Predicting the final location and volume of lesions in acute ischemic stroke is crucial for clinical management. While CTP is routinely used for estimating lesion outcomes, conventional threshold-based methods have limitations. We developed specialized outcome-prediction deep learning models that predict infarct core in successful reperfusion cases and the combined core-penumbra region in unsuccessful reperfusion cases. MATERIALS AND METHODS: We developed single-modal and multimodal deep learning models using CTP parameter maps to predict the final infarct lesion on follow-up DWI. Using a multicenter data set from multiple sites, we developed deep learning models and evaluated them separately for patients with complete recanalization (successful reperfusion [CR], n = 350) and no recanalization (unsuccessful reperfusion [NR], n = 138) after treatment. The CR model was designed to predict the infarct core region, while the NR model predicted the expanded, hypoperfused tissue encompassing both the core and penumbra regions. Five-fold cross-validation was performed for robust evaluation. RESULTS: The multimodal 3D nnU-Net model demonstrated superior performance, achieving mean Dice scores of 35.36% in patients with CR and 50.22% in those with NR. This model substantially outperformed the current clinically used method, providing more accurate outcome estimates than the conventional single-technique threshold-based measures, which yielded Dice scores of 15.73% and 39.71% for CR and NR groups, respectively. CONCLUSIONS: Our approach offered both successful reperfusion and unsuccessful reperfusion estimations for potential treatment outcomes, enabling clinicians to better evaluate treatment eligibility for reperfusion therapies and assess potential treatment benefits. This advancement facilitates more personalized treatment recommendations and has the potential to substantially enhance clinical decision-making in acute ischemic stroke management by providing more accurate tissue outcome predictions than conventional single-technique threshold-based approaches.
4 Apr 2026
Read appraisal →Stroke
Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health
Digital health solutions are being increasingly used to support care delivery across various medical fields. In stroke care, technology has contributed to enhancing prehospital workflows, facilitating communication among health care professionals, and enabling fast-track systems for hyperacute stroke management. Furthermore, digital tools are used to improve etiological diagnosis and allow for more tailored long-term follow-up of stroke patient survivors. Digital technologies have become an integral part of stroke management, and the rapid evolution of artificial intelligence continues to accelerate and drive this transformation. However, ethical and privacy considerations are central and represent a major challenge for the design and implementation of digital solutions in health care. Health care professionals involved in stroke care require a clear understanding of the digital tools they use, including their potential, limitations, and biases, and must be aware of the responsibilities inherent in relying on such systems for treatment decisions and outcome prediction. This topical review aims to examine current digital innovations in stroke care, with a particular focus on the key ethical and privacy challenges they raise.
4 Apr 2026
Read appraisal →European radiology
Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography
OBJECTIVES: To evaluate GPT-4o's zero-shot ability to extract structured diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. MATERIALS AND METHODS: This retrospective study included radiography series of the clavicle (n = 1170), elbow (n = 3755), and thumb (n = 1978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To assess the impact of label uncertainty, "uncertain" labels of the training and validation sets were automatically reassigned to "true" (inclusive) or "false" (exclusive). Label-image pairs were used for multi-label classification using the ResNet50 architecture. Label extraction accuracy was manually verified on internal (clavicle: n = 233, elbow: n = 745, thumb: n = 393) and external test sets (n = 300 for each). Performance was assessed using macro-averaged receiver operating characteristic (ROC) area under the curve (AUC), precision, recall curves, sensitivity, specificity, and accuracy. AUCs were compared with the DeLong test. RESULTS: Automatic extraction was correct in 98.6% (60,618 of 61,488) of labels in the test sets. Across anatomic regions, label-based model training yielded competitive performance measured by macro-averaged AUC values for inclusive (e.g., elbow: AUC = 0.80 (range, 0.62-0.87)) and exclusive models (elbow: AUC = 0.80 (range, 0.61-0.88)). Models generalized well on external datasets (elbow (inclusive): AUC = 0.79 (range, 0.61-0.87); elbow (exclusive): AUC = 0.79 (range, 0.63-0.89)). No significant differences were observed across labeling strategies or datasets (p ≥ 0.15). CONCLUSION: GPT-4o extracted labels from radiologic reports to train competitive multi-label classification models with high accuracy. Detected uncertainty in the radiologic reports did not influence the performance of these models. KEY POINTS: Question Can GPT-4o automatically extract high-accuracy, uncertainty-aware diagnostic labels from routine radiologic reports of the clavicle, elbow, and thumb for use in training multi-label image classifiers? Findings GPT-4o extracted labels with > 98% accuracy, and multi-label classifiers for clavicle, elbow, and thumb radiographs performed consistently regardless of how uncertainty was handled. Clinical relevance Automated GPT-4o-based labeling of routine clavicle, elbow, and thumb radiologic reports enables the rapid conversion of radiologic reports into structured multi-label training datasets, supporting scalable development of dedicated image classification models.
4 Apr 2026
Read appraisal →Anaesthesia, critical care & pain medicine
Hemodynamic phenotyping 4.0.
The concept of integrating hemodynamic variables to define specific profiles or phenotypes has been established for decades. Describing hemodynamic phenotypes plays a key role in educating healthcare professionals about cardiovascular physiology, enhancing the understanding of shock mechanisms, and informing treatment strategies. Recently, two notable innovations have emerged to support bedside identification of hemodynamic phenotypes: machine learning (ML) algorithms and visual decision support tools. When it comes to "small data," such as a limited set of hemodynamic variables, ML algorithms may not be essential for data integration or interpretation. In addition, the hemodynamic phenotypes identified by ML techniques often mirror traditional textbook profiles, though occasionally with inconsistencies that may impact patient safety. This raises valid questions about the need to integrate complex and proprietary ML algorithms for bedside hemodynamic assessment. By contrast, visual tools leverage clinicians' innate ability to process graphical information rapidly, improving the understanding of cardiovascular physiology and enabling recognition of hemodynamic profiles at a glance. As such, they may offer a practical, accessible, and cost-effective alternative to ML-based solutions. Future studies comparing the clinical impact of visual versus ML-driven phenotyping are now needed to guide further development and implementation.
2 Apr 2026
Read appraisal →Expert review of medical devices
Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review
INTRODUCTION: Generalized tonic - clonic seizures (GTCS) is a major cause of sudden unexpected death in epilepsy (SUDEP); thus, continuous monitoring is essential. Electroencephalography (EEG) is the diagnostic gold standard; however, non-EEG wearables have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. AREAS COVERED: This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, Scopus, IEEE Xplore, and PubMed databases were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria. For seizure detection, combining electrodermal activity (EDA) with motion sensors - accelerometers (ACC) and gyroscopes (GYR) - and surface electromyography (sEMG) achieved high reliability, 98.6% precision, while using ACC and EDA alone yielded superior sensitivities up to 97.2% and a lower false alarm rate (FAR) of 0.53/24 h. Regarding seizure prediction, combining EDA, blood volume pulse (BVP), ACC, and temperature showed the highest sensitivity of 75.6%. EXPERT OPINION: Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP).Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life.Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups.Advances in wearable sensors and artificial intelligence could revolutionize epilepsy management by making continuous seizure monitoring and prediction possible in daily life. Although more evidence is required before widespread clinical adoption, these technologies hold strong potential to improve safety, autonomy, and quality of life for people living with epilepsy.
2 Apr 2026
Read appraisal →Kidney360
Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration
Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.
1 Mar 2026
Read appraisal →Curr Opin Pulm Med
Foreign body removal
PURPOSE OF REVIEW: Airway foreign body aspiration remains a potentially life-threatening emergency, predominantly affecting children under 5 years and adults over 65 years. This review synthesizes current evidence on diagnostic strategies, bronchoscopic extraction techniques, procedural outcomes, complication management, and emerging technologies in airway foreign body management. RECENT FINDINGS: Multidetector computed tomography with three-dimensional reconstruction has significantly improved diagnostic accuracy, achieving sensitivity of 98-99% for radiopaque objects and 85-92% for radiolucent materials. Flexible bronchoscopy has evolved from a diagnostic tool to a first-line therapeutic modality, with recent pediatric meta-analyses demonstrating 87% success rates and adult series showing comparable outcomes to rigid bronchoscopy for appropriately selected cases. Rigid bronchoscopy maintains superiority in asphyxiating presentations, and for large (>1.5 cm), sharp, or severely impacted foreign bodies. Technological innovations including robotic-assisted bronchoscopy, electromagnetic navigation systems, and artificial intelligence-powered imaging analysis are enhancing procedural precision and safety. SUMMARY: Successful airway foreign body management requires individualized, multidisciplinary approaches integrating patient clinical status, foreign body characteristics, and institutional expertise. Success depends on appropriate bronchoscopic modality selection, comprehensive preprocedural planning, availability of specialized retrieval instruments, and readiness to manage potential complications. Integration of advanced imaging, simulation-based training protocols, and telemedicine consultation are becoming essential components of contemporary practice.
1 Jan 2026
Read appraisal →Cell
Generation of antigen-specific paired-chain antibodies using large language models.
The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (monoclonal antibody generator), a sequence-based protein language model (PLM) fine-tuned for the task of generating paired human variable heavy- and light-chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.
1 Dec 2025
Read appraisal →Intensive Care Med
Critical care management of the patient with pharmaceutical poisoning
Poisonings account for up to 10% of admissions to intensive care units and represent a common yet complex challenge in critical care. While most patients recover with supportive care alone, a significant proportion require targeted interventions and advanced organ support. This state-of-the-art review provides a practical, evidence-based guide to manage pharmaceutical poisoning in the intensive care unit, with emphasis on real-world applicability for intensivists. It highlights toxicant-specific considerations for early resuscitation, structured by the ABCDE approach. Essential principles of the diagnostic workup are also summarized. In addition, the review offers actionable guidance on when and how to escalate care, moving from supportive measures to specific interventions including gastrointestinal decontamination, extracorporeal toxicant elimination, antidote therapy, and rescue strategies such as intravenous lipid emulsion and extracorporeal membrane oxygenation. Key algorithms and decision pathways-such as the diagnostic evaluation, ABCDE-based stabilization, gastrointestinal decontamination, and extracorporeal elimination approaches-are presented with visually structured figures. A comprehensive table provides an overview of the most relevant antidotes used in critical care, including indications and dosing.
1 Dec 2025
Read appraisal →Blood Rev
Advances in the critical care management for patients with hematological malignancies
Hematological malignancies (HMs) are increasingly associated with life-threatening complications requiring intensive care unit (ICU) management. Recent advancements in therapies, diagnostics, and critical care protocols have improved outcomes for these patients, yet significant challenges persist. This manuscript explores the evolving landscape of critical care in hematology, emphasizing the unique complications, management strategies, and future directions in the field. Patients with HMs are particularly vulnerable to infections, sepsis, organ dysfunction, and treatment-related toxicities such as cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and coagulopathies. Innovations in the management of acute respiratory failure, septic shock, and invasive fungal infections have contributed to better survival rates, yet outcomes remain suboptimal for certain high-risk groups. Furthermore, new therapies, including CAR-T cells, bispecific antibodies, and immune checkpoint inhibitors, present both opportunities and challenges in the ICU setting due to their potential toxicities. Emerging trends emphasize the importance of early ICU admission, multidisciplinary collaboration, and precision medicine in improving patient care. The integration of biomarker-driven strategies, advanced diagnostics, and artificial intelligence holds promise for optimizing therapeutic interventions and enhancing antimicrobial stewardship. Additionally, patient-centered approaches, including time-limited trials and goal-oriented discussions, aim to balance aggressive care with quality-of-life considerations. This review underscores the need for continued research to address disparities in access to care, improve long-term outcomes, and develop standardized protocols for managing critically ill hematology patients. By advancing the integration of oncology and critical care, clinicians can better navigate the complexities of modern therapies and provide holistic, evidence-based care that aligns with patient values and priorities.
1 Nov 2025
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