Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 31 appraisals
European radiology
Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI
OBJECTIVES: Motion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. Single-shot sequences offer a time-efficient alternative but suffer from reduced image quality. This study aimed to evaluate the diagnostic performance of a deep learning (DL) framework combining compressed sensing (CS) and convolutional neural networks (CNNs) to enhance T2-weighted single-shot MRI (T2-SSHDL) compared with conventional CS-based reconstruction (T2-SSHconv) and routinely acquired high-resolution T2-weighted sequences. MATERIALS AND METHODS: This prospective single-center study included 62 pediatric patients (mean age, 7.4 ± 4.9 years; 36 males, 26 females), who underwent T2-weighted single-shot brain MRI (29 sedated, 33 awake). Raw data were reconstructed using a DL-based pipeline and compared with conventional CS-based reconstructions. Quantitative metrics included apparent contrast-to-noise ratio (aCNR), apparent signal-to-noise ratio (aSNR), and edge rise distance (ERD). Two radiologists rated images for artifacts, sharpness, lesion conspicuity, and overall quality on a 5-point Likert scale. RESULTS: T2-SSHDL-sequences showed significantly higher aCNR (29.9 ± 22.6 vs. 26.7 ± 16.5; p < 0.001), aSNR (41.6 ± 27.9 vs. 38.2 ± 20.8; p = 0.003), and improved sharpness (ERD 0.90 ± 0.35 mm vs. 1.35 ± 0.42 mm; p < 0.001). Qualitative assessments confirmed superior image quality, lesion conspicuity, and sharpness (p < 0.001). Compared with high-resolution T2-weighted sequences, T2-SSHDL-sequences showed fewer motion artifacts and comparable lesion conspicuity in non-sedated patients. CONCLUSION: DL-based reconstruction significantly enhances the diagnostic quality of T2-weighted single-shot brain MRI in pediatric patients, enabling clinically usable, ultrafast, motion-robust imaging with potential to reduce the need for sedation. KEY POINTS: Question Can deep learning-based reconstruction elevate motion-robust single-shot T2-weighted pediatric brain MRI to diagnostic image quality levels, enabling reliable imaging without sedation? Findings Both quantitative and qualitative evaluations confirmed significantly improved image quality of deep learning-enhanced single-shot T2-weighted brain MRI compared with conventional reconstruction. Clinical relevance Deep learning-enhanced reconstruction improves image quality in ultrafast, motion-robust single-shot pediatric brain MRI, potentially reducing the need for sedation while preserving diagnostic accuracy. This approach may enhance patient safety and shorten examination time in routine neuroimaging.
2 Aug 2026
Read appraisal →Seminars in fetal & neonatal medicine
Intraventricular hemorrhage in preterm infants: A systematic review of risk- and outcome-prediction models
Intraventricular hemorrhage (IVH) is a major complication of prematurity and one of the top causes of mortality and neurodevelopmental impairment. We conducted a systematic review of PubMed, Scopus, and Web of Science (From 1980 to 2025), identifying 40 studies evaluating prediction models (regression and machine learning) for risk of IVH occurrence and short- and long-term outcomes in preterm infants with IVH. Across these published studies, IVH risk prediction models included combinations of perinatal clinical variables, physiologic and hemodynamic indices, and serum biomarkers. Outcome-prediction models likewise varied. IVH grade was commonly included, with varying inclusion of comorbidities and neuroimaging-based injury markers. Most models performed acceptably, with machine-learning models showing better discrimination in larger datasets. However, the generalizability of the models is limited by heterogeneity in predictors, limited sample sizes, and inconsistent timing of predictor measurements. IVH severity remained the most consistent predictor across all outcome-prediction models. Despite promising performance assessments, clinical relevance is limited due to the infrequent reporting of model calibration, internal validation, and external validation. Future research that standardizes predictor definitions, leverages multicenter cohorts, and ensures thorough validation can advance early IVH risk and outcome-prediction models, thereby meaningfully improving clinical relevance and neonatal care.
2 Aug 2026
Read appraisal →Indian journal of pediatrics
Emerging Applications of Artificial Intelligence in Pediatric Care
Artificial intelligence (AI) technologies such as machine learning (ML), deep learning (DL), predictive analytics and other tools are rapidly changing pediatric health care, using large amounts of health data. AI tools aid in triage, real-time monitoring and risk stratification in acute care settings, towards improving overall outcomes and fewer complications. During newborn resuscitation, AI analyses real-time data, can guide decisions and enhance training. Computer vision systems with AI tools can generate reliable neonatal bilirubin estimates without the need for blood sampling. AI technology is also being used in the management of necrotising enterocolitis, respiratory distress syndrome, and screening and early diagnosis of retinopathy of prematurity. ML models assist in detecting brain injuries on MRI for conditions such as hypoxic-ischemic encephalopathy, intraventricular hemorrhage; MRI biomarkers can be analyzed using AI to predict neurodevelopmental outcomes. AI-based clinical decision support systems have been deployed to enhance workflows and outcomes by early detection of disease, reducing medication errors and help clinicians improve decision-making. However, there remain ethical and practical challenges in the use of AI including data privacy, the need for high-quality pediatric datasets, rigorous clinical validation and transparency, to ensure that AI strengthens clinical judgement and is trustworthy.
2 Aug 2026
Read appraisal →Cell reports methods
AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala
Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.
22 July 2026
Read appraisal →Neurosurgical review
Mechanical thrombectomy for pediatric large-vessel occlusion: a systematic review and meta-analysis with post hoc analysis
OBJECTIVE: To investigate the outcome of pediatric large vessel occlusion (LVO) treated with mechanical thrombectomy (MT). INTRODUCTION: Pediatric LVO is a rare subtype of ischemic stroke. In adults, MT is recommended; however, the efficacy and safety of MT in pediatric LVO are less explored. The available studies are mostly observational studies; yet, these studies show inconsistent findings. METHOD: This review adhered to the PRISMA 2020 guideline. Studies comparing MT with medical management in pediatric with LVO were included. The primary endpoint was the favourable outcome. The mortality rate was sought as well. Subgroup analysis compared MT with intravenous thrombolysis (IVT) and conservative management. Post hoc exploratory analyses comprised Bayesian inference and trial sequential analysis (TSA). RESULT: Six studies comprising a total of 230 MT-treated and 206 non-MT patients were included. In the primary analysis, favourable outcome did not differ significantly between the MT and medical management groups (OR 1.83; 95% CI 0.97-3.43), while supported by exploratory Bayesian inference (OR 1.75; 95% CrI 1.05-2.67; BF10 5.05). Subgroup analysis showed favourable outcomes of MT over IVT (OR 2.39; 95% CI 1.19-4.79) and conservative treatment (OR 2.22; 95% CI 1.41-3.51), although the Bayesian model did not support these findings. The available evidence was insufficient to detect or exclude a difference in mortality between MT and medical management (OR 1.32; 95% CI 0.45-3.89). TSA indicated that the required information size was not reached and that additional trials are needed to reach the minimum sample requirements. CONCLUSION: In this synthesis of observational data, the MT may be associated with favourable outcome in pediatric LVO, however, these findings are hypothesis-generating only. Adequately powered prospective studies are needed before MT can be recommended for pediatric LVO. CLINICAL TRIAL NUMBER: not applicable.
13 July 2026
Read appraisal →European journal of pediatrics
Congenital cytomegalovirus screening by dried blood spot: a systematic review
UNLABELLED: Congenital human cytomegalovirus (cCMV) infection is a leading cause of sensorineural hearing loss and neurological sequelae in children. Although newborn screening strategies remain controversial, dried blood spots (DBS) collected for routine metabolic screening have been proposed as a low-cost method for large-scale detection. This systematic review assessed studies that evaluated the accuracy of DBS testing for screening newborns for cCMV infection. A systematic review was conducted following the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and reported in accordance with PRISMA-DTA. The PIRT strategy included the following: Population (newborns undergoing screening for cCMV), Index test (detection of cCMV using DBS samples), Reference standard (confirmatory testing using saliva or urine PCR collected within 21 days of life), Target condition (cCMV infection). Searches were performed in PubMed/MEDLINE, Scopus, Bireme, Embase, LILACS, SciELO, and CENTRAL. The search identified 365 articles; after removing 210 duplicates, 155 were screened by title and abstract, and 9 studies were included. The included studies tested between 1174 and 551,034 DBS samples each. Confirmatory testing used PCR in urine (seven studies) or saliva (two studies). Three studies used paired samples for screening; sensitivity ranged from 28.3% to 79.3%, specificity from 99.9% to 100%. Positive predictive value (PPV) ranged from 39 to 100%, and negative predictive value (NPV) from 99.6% to 99.9%. CONCLUSION: Neonatal screening for congenital cytomegalovirus using DBS samples is promising, since it would allow early identification for timely interventions to reduce sequelae. WHAT IS KNOWN: • Congenital CMV is a leading cause of sensorineural hearing loss and neurodevelopmental sequelae, with many cases asymptomatic at birth. Diagnosis relies on early viral detection, and while saliva/urine PCR is sensitive, newborn screening lacks consensus and feasibility is unknown in many settings. WHAT IS NEW: • DBS PCR emerges as a scalable, feasible low-cost screening alternative for cCMV screening. A few states in the USA and provinces in Canada have implemented newborn cCMV screening programs using DBS, but the sensitivity appears lower than in research studies. Improving DNA extraction and PCR methods are required as well as assessing feasibility of using other specimens (e.g., urine or saliva) for screening.
28 June 2026
Read appraisal →Journal of medical Internet research
Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study
BACKGROUND: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing research using various video data sources and analytical approaches, no comprehensive framework exists to classify these technologies. OBJECTIVE: This scoping review aimed to develop and validate a taxonomy for AI technologies in vision-based epilepsy monitoring and to characterize visual AI approaches in epilepsy care. METHODS: Using an extended taxonomy development framework, we developed the taxonomy in 5 iterative cycles, drawing on theory and practice. We conducted a scoping review, market analysis, and applicability evaluation with market-ready solutions. We searched Scopus, Web of Science, and PubMed, including MeSH (Medical Subject Headings) terms; the final search was completed on January 16, 2026. We included primary studies from 2013 onward on AI-based or machine learning-based monitoring or prediction of epileptic seizures in humans using visual data. We excluded reviews, non-English publications, nonepilepsy studies, studies focused only on electroencephalography or wearables, animal studies, and pre-2013 publications. Evidence was charted through narrative and tabular synthesis and descriptive frequency analysis. In line with scoping review guidance, we did not conduct a meta-analysis or critical appraisal. To assess validity and practical relevance, 9 domain experts evaluated the taxonomy using a Delphi technique. RESULTS: We included 40 original studies. Study analysis yielded 16 dimensions, including data acquisition source, tracking target, image processing, classifier type, performance metrics, environment, seizure classification, data privacy, and user interface. Expert feedback added 4 further dimensions, including communication mode and information purpose. The final taxonomy comprises 23 dimensions with 102 characteristics. The review identified structural evidence gaps across settings, evaluation maturity, and reporting practices. Detection and classification in stationary settings predominated, whereas predictive approaches and real-time feedback were limited. Deep learning detection methods were common, but performance reporting was inconsistent, and patient-facing functionalities were limited. Privacy safeguards and standardized metrics were often incompletely reported, reducing comparability and maturity assessment. The taxonomy translates these patterns into guidance for benchmarking, procurement evaluation, user interface, and explainable AI design. We synthesized 5 main findings and 10 implications for research and practice. Key challenges concern standardization, seizure prediction, and real-time applicability. CONCLUSIONS: Vision-based AI technologies for epilepsy monitoring are still dominated by proof-of-concept and pilot evaluations, indicating a gap between technical feasibility and deployment-ready systems. This scoping review presents an implementation-oriented taxonomy integrating application context, system architecture, visual analysis, AI models, performance reporting, and feedback design into a single classification framework. Unlike prior work that mainly maps methods or data sources, the taxonomy provides a shared structure for consistent system-level characterization and comparison across studies and emerging solutions. It may support benchmarking, implementation-focused evaluation, procurement, and translation into clinical and home settings.
25 June 2026
Read appraisal →Reviews in the neurosciences
Probiotics and seizure susceptibility in preclinical epilepsy models: mechanistic insights from a systematic review and meta-analysis
The gut microbiota can affect neuronal excitability, inflammation, and oxidative balance via the gut-brain axis, shaping seizure susceptibility. To translate these findings into clinical approaches, a synthesis of preclinical microbiome-based evidence is needed. This systematic review and meta-analysis examined the putative anticonvulsant, anti-inflammatory, antioxidant, and neuroprotective effects of probiotics in rodent models. An extensive systematic search up to July 2025 identified eligible animal studies in which probiotics were administered in seizure models. Reported outcomes included seizure latency, duration, severity, and frequency, as well as inflammation, oxidative stress, and behavioral measures. Of the 24 studies that met the inclusion criteria, 19 provided sufficient data to be included in the meta-analysis. The most frequently used strains belonged to the Lactobacillus (e.g., acidophilus, casei, fermentum) and Bifidobacterium genera (e.g., bifidum, longum), with occasional synbiotic combinations. Probiotics significantly increased seizure latency (MD = 22.09; 95 % CI: 10.52 to 33.67) and reduced seizure severity (MD = -1.08; 95 % CI: -1.39 to -0.76) and duration (MD = -23.19; 95 % CI: -35.56 to -10.82). Probiotics significantly reduced IL-1β, IL-6, and TNF-α levels while MDA showed a nonsignificant trend toward reduction (p = 0.076). Behaviorally, improvements in spatial learning (p < 0.05) and reduced anxiety-like behavior (p < 0.001) were observed. Probiotic supplementation appears to exert anticonvulsant, anti-inflammatory, antioxidant, and behavioral benefits in preclinical epilepsy models, although the evidence is heterogeneous and limited to animal studies. Mechanistic evidence indicates modulation of the gut-brain axis, enhanced GABAergic signaling, and improved mitochondrial function. These findings support further investigation of specific probiotic formulations as promising adjunct candidates in well-designed, mechanism-driven clinical trials.
25 June 2026
Read appraisal →Behavioural brain research
Sex differences in the use of spatial strategies in humans: An updated systematic review of the last decade.
Although sex differences in spatial cognition have been widely studied, the extent to which men and women differ in their use of egocentric and allocentric spatial strategies remains unclear. This systematic review summarizes findings from 21 studies published between 2014 and 2024 that examined sex differences in spatial strategy use throughout development, adulthood, and aging. Studies were identified through searches of Scopus, Web of Science, PubMed, and PsycINFO, in accordance with PRISMA guidelines. Eligible articles examined egocentric and allocentric spatial strategies in humans and reported results by sex. Results demonstrated male advantages in allocentric strategy, as observed in thirteen studies involving children, young adults, and older adults. Men also outperformed women in several navigation outcomes, including target retrieval, route efficiency, and directional accuracy. Evidence for a female advantage in egocentric strategies was limited and inconsistent. Seven studies reported no sex differences, particularly those using small-scale, familiar, or less immersive tasks. This suggests that methodological factors and task demands strongly influence the observed effects. Age also influenced outcomes. Male advantages in allocentric strategies emerged in early childhood. Older women showed relatively preserved egocentric abilities, but greater decline in allocentric tasks. Men showed stronger hippocampal-parietal connectivity supporting allocentric processing, while women exhibited enhanced fronto-temporal connectivity. Hormonal studies highlight the context-dependent effects of testosterone and estrogen. These findings indicate that sex differences in spatial strategies are highly sensitive to task complexity, environmental familiarity, cognitive load, and individual factors. These findings emphasize the necessity of integrative models that consider developmental, neurobiological, and sociocultural contributors.
25 June 2026
Read appraisal →PloS one
Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection
Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on high-quality annotated training data. However, scalp EEG is susceptible to high noise levels, which in turn leads to imprecise annotations of the seizure timing and characteristics. This "label noise" presents a significant challenge in model training and generalization. In this paper, we introduce Bayesian UncertaiNty-aware Deep Learning (BUNDL), a novel algorithm that informs a deep learning model of label ambiguities, thereby enhancing the robustness of seizure detection systems. By integrating domain knowledge into an underlying Bayesian framework, we derive a novel KL-divergence-based loss function that capitalizes on uncertainty to better learn seizure characteristics from scalp EEG. Thus, BUNDL offers a straightforward and model-agnostic method for training deep neural networks with noisy training labels that does not add any parameters to existing architectures. Additionally, we explore the impact of improved detection system on the task of automated onset zone localization. We validate BUNDL using a comprehensive simulated EEG dataset and two publicly available datasets collected by Temple University Hospital (TUH) and Boston Children's Hospital (CHB-MIT). Results show that BUNDL consistently identifies noisy labels and improves the robustness of three base models under various label noise conditions. We also conduct ablation experiments on uncertainty quantification, evaluate cross-site generalizability to Siena EEG dataset, and quantify computational cost of all methods. Furthermore, we demonstrate that BUNDL improves seizure onset zone localization accuracy. Ultimately, BUNDL presents as a reliable method that can be seamlessly integrated with existing deep models used in clinical practice, enabling the training of trustworthy models for epilepsy evaluation.
25 June 2026
Read appraisal →European journal of pediatrics
Subtypes in developmental coordination disorder: what do we know so far? A systematic review.
UNLABELLED: Developmental coordination disorder (DCD) is a highly heterogeneous disorder. This systematic review examined whether statistically derived clusters can effectively distinguish subgroups of children with DCD. Web of Science, Scopus, PubMed, Embase, and PsycInfo were systematically searched (CRD420251237685) using controlled terminology and free-text terms. Studies were eligible if they included children (aged 0-18 years) with DCD with(out) comorbidities and employed statistical clustering to identify subtypes. Methodological quality was assessed using an adapted version of the Critical Appraisal Skills Programme and certainty of the evidence with the Grading of Recommendations Assessment, Development and Evaluation. Ten of the 1719 search results were included, three of which included both children with DCD and typical development. Two studies explored clusters unidimensionally (motor-only and mental health-only). Eight studies included multiple domains of functioning: motor (n = 8), sensory functions (n = 5), fitness (n = 3), cognitive functions (n = 3), executive functions (n = 2), mental health (n = 1), and participation (n = 1). Studies identified between four (n = 4) and six (n = 1) subtypes, which primarily reflected gradients of severity rather than distinct domain-specific profiles. The overall certainty of evidence for distinct subtypes in DCD was rated low to very low. CONCLUSION: Evidence suggests that DCD is better conceptualized as a spectrum of pervasiveness rather than discrete subtypes. Existing findings lack consistency across studies due to methodological shortcomings and variable measurement domains. Although evidence-based clinical subtypes cannot yet be recommended, future research should prioritize subtyping studies focused on primary symptoms to better identify potential phenotypes within the DCD population. WHAT IS KNOWN: • DCD is a heterogeneous condition, and identifying discrete subtypes is a long-standing research goal to improve clinical support. • Researchers have proposed various motor and non-motor profiles, but no consensus exists on a definitive subtyping model. WHAT IS NEW: • This review found no consistent discrete subtypes, suggesting DCD is better viewed as a spectrum of pervasiveness rather than categories. • Future research should focus on subtyping primary motor symptoms to determine whether distinct, stable subgroups exist within the broader DCD population.
22 June 2026
Read appraisal →Progress in neuro-psychopharmacology & biological psychiatry
Neural correlates of late talking: A systematic review of electrophysiological and neuroimaging studies.
BACKGROUND: Late talking (LT), a delay in expressive vocabulary in early childhood, affects a significant minority of toddlers and can be a precursor to persistent language and literacy impairments. While behavioral profiles are well-documented, a systematic synthesis of its underlying neurobiology is lacking. This systematic review aimed to identify, evaluate, and synthesize evidence from electrophysiological (EEG) and neuroimaging (MRI) studies on the neural correlates of late talking in children. METHODS: We conducted a systematic search of Scopus, Web of Science, and PubMed from inception to September 2025, following PRISMA guidelines. Studies investigating children formally identified as late talkers without comorbid neurological or global developmental conditions, using EEG/ERP or MRI modalities, were included. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool. RESULTS: Sixteen studies (8 EEG, 8 neuroimaging) comprising 231 LT children in EEG studies and 315 in neuroimaging studies were included. EEG studies revealed a neurodevelopmental cascade, beginning with atypical oscillatory activity (increased frontal gamma power) and deficits in auditory discrimination (reduced Mismatch Negativity), progressing to higher-order impairments in phonological (absent PMN) and lexical-semantic (altered N400) processing. Neuroimaging studies consistently identified structural and functional anomalies within a distributed perisylvian network, including reduced gray matter in left temporal regions, atypical right-hemispheric lateralization, reduced activation in cortical-subcortical circuits, and altered white matter connectivity. These neural markers demonstrated significant predictive value for later language outcomes. CONCLUSION: Late talking is a clear neurodevelopmental disorder with a cascading pattern of neural dysfunction. The identified neural signatures are not just findings but have strong clinical potential as biomarkers for early identification and prognosis. This evidence advocates for a shift to a precision medicine approach, opening the door to novel, neurobiologically-informed interventions like neuromodulation that target the underlying brain circuits.
20 June 2026
Read appraisal →Neurology
Vamorolone Safety, Pharmacokinetics, and Exploratory Efficacy in Duchenne Muscular Dystrophy: A Phase II, Nonrandomized, Multiple-Dose Study in 2-<4-Year-Old Boys
BACKGROUND AND OBJECTIVES: Vamorolone is a dissociative corticosteroid (CS) approved by the US Food and Drug Administration in 2023 for treating Duchenne muscular dystrophy (DMD). This study evaluated the safety, tolerability, and pharmacokinetics (PK) of vamorolone in young boys with DMD; exploratory objectives included efficacy and patient-reported outcomes. METHODS: A 12-week, phase II, open-label, multiple-dose study (VBP15-006) enrolled CS-naive boys with DMD aged 2-<4 years in Canada. Participants received 2 or 6 mg/kg/d oral vamorolone. An ongoing expanded access protocol (EAP) provided longer-term data. Primary end points were the occurrence of treatment-emergent adverse events (TEAEs) and changes from baseline to week 12 in height, weight, and body mass index (BMI); other end points included PK and effects of vamorolone on muscle function. RESULTS: Twenty boys (mean age [SD] 3.4 [0.39] years) with similar baseline characteristics were enrolled; all completed VBP15-006 and 19 continued to receive vamorolone through EAP Canada. TEAEs were mostly mild, with no deaths, serious TEAEs, or TEAEs leading to drug discontinuation during VBP15-006. TEAEs were more frequent with 6 mg/kg/d vamorolone than with 2 mg/kg/d, with gastrointestinal disorders and infections/infestations, respectively, most reported. Vamorolone use was associated with morning serum cortisol reductions in all patients, more pronounced with 6 mg/kg/d. Stable growth trajectories were observed at both doses throughout VBP15-006 and the EAP Canada follow-up period, with a total median (Q1; Q3) exposure to vamorolone of 2 years (1.7; 2.3). By EAP Canada last visit, 8/19 patients had weight and BMI z-scores changes >0.5, suggestive of excessive weight gain over time. Vamorolone displayed a dose-dependent PK profile with rapid absorption and no accumulation. Dose-dependent improvements in motor function (measured by Bayley-III gross motor scaled score) were observed. Glucose metabolism and bone turnover biomarkers remained stable. Ease of administration was rated favorably. DISCUSSION: Vamorolone was well tolerated in 2-<4-year-old boys with DMD, with no new safety concerns identified. A dose-dependent PK profile was observed, consistent with previous studies. Exploratory evidence suggested dose-dependent improvements in gross motor function. These findings are consistent with potential therapeutic benefit of vamorolone for young boys with DMD. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT05185622 (VBP15-006; clinicaltrials.gov/study/NCT05185622), NCT03863119 (EAP; clinicaltrials.gov/study/NCT03863119). First submitted November 9, 2021. First patient enrolled: March 21, 2022. CLASSIFICATION OF EVIDENCE: The VBP15-006 study provides Class IV evidence that in boys with DMD aged 2-<4 years, 12-week treatment with vamorolone was not associated with serious adverse events or changes in weight, height, or BMI. Gastrointestinal events and infections were the most reported TEAEs.
9 June 2026
Read appraisal →Brain structure & function
Automated interictal epileptic spike detection from simple and noisy annotations in MEG data
In drug-resistant epilepsy, presurgical evaluation can be considered for suitable candidates. Magnetoencephalography (MEG) has been shown to be an effective exam to inform the localization of the epileptogenic zone through the localization of interictal epileptic spikes. Manual detection of these pathological biomarkers remains a fastidious task due to the high dimensionality of MEG recordings, and inter-rater agreement has been reported to be only moderate. Current automated methods are unsuitable for clinical practice, either requiring extensively annotated data or lacking robustness on non-typical data. Considering a database of 82 patients, we demonstrate that deep learning models can be used for detecting interictal spikes in MEG recordings, even when only temporal and single-expert annotations are available, which represents real-world clinical practice. We propose two model architectures: a feature-based artificial neural network (ANN) and a convolutional neural network (CNN), evaluated against a state-of-the-art model to classify short time windows of signal. In addition, we employ an interactive machine learning strategy to iteratively improve our data annotation quality using intermediary model outputs. Both proposed models outperform the state-of-the-art model (F1-scores: CNN = 0.46, ANN = 0.44) when tested on 10 holdout test patients. The interactive machine learning strategy demonstrates that our models are robust to noisy annotations. Overall, results highlight the ability of our models to analyze complex and imperfectly annotated data. Our method of interactive machine learning offers great potential for faster data annotation, while our models represent useful and efficient tools for automated interictal spikes detection.
1 June 2026
Read appraisal →Neurology
Vamorolone for Duchenne Muscular Dystrophy: A Cross-Trial Efficacy Comparison With Classic Corticosteroids From the FOR-DMD Trial
BACKGROUND AND OBJECTIVES: Vamorolone demonstrated similar efficacy for Duchenne muscular dystrophy (DMD) compared with prednisone in a 24-week exploratory analysis and may reduce key side effects compared with classic corticosteroids. In this study, we compare the efficacy and anthropometric effect of vamorolone 6 mg/kg/d with prednisone 0.75 mg/kg/d and deflazacort 0.9 mg/kg/d in steroid-naïve boys aged 4 to <7 years using data from 2 trials. METHODS: VISION-DMD was a phase 2b, 48-week randomized, double-blind trial assessing vamorolone 2 and 6 mg/kg/d vs placebo and prednisone 0.75 mg/kg/d to 24 weeks. Finding the Optimum Steroid Regimen for DMD (FOR-DMD) was a double-blind, parallel-group randomized trial comparing daily prednisone 0.75 mg/kg/d, daily deflazacort 0.9 mg/kg/d, and intermittent prednisone 0.75 mg/kg (10/10 days on/off). Entropy balancing generated weighted data for mixed model for repeated measures analyses that compared VISION-DMD vamorolone 6 mg/kg/d efficacy and anthropometric outcomes with FOR-DMD prednisone and deflazacort outcomes at 6 and 12 months. Inclusion criteria were boys with genetically confirmed DMD, age 4 to <7 years, ambulatory, and able to complete a time-to-stand (TTSTAND) assessment in <10 seconds at baseline. RESULTS: All interventions showed motor improvements relative to baseline at 6 and 12 months. Using the weighted cohorts and at 12 months, vamorolone 6 mg/kg/d (n = 28) had similar changes in TTSTAND velocity, the primary motor endpoint component in both trials, vs daily prednisone (n = 50) or deflazacort (n = 55; mean baseline age 5.42 years all groups), but with CIs overlapping minimal clinically important thresholds of 0.023 rises per second (TTSTAND velocity least squares mean [LSM] difference [95% CI]: vamorolone 6 mg/kg/d vs prednisone 0.75 mg/kg/d, 0.004 rises per second [-0.025 to 0.032] and vs deflazacort 0.9 mg/kg/d, 0.001 rises per second [-0.027 to 0.028]). Body mass index (BMI) Z-scores increased in all groups, greatest with vamorolone (12-month LSM difference [95% CI]: vamorolone vs prednisone 0.57 [0.24-0.90]; vamorolone vs deflazacort 0.29 [0.03-0.56]), whereas growth slowdown occurred in prednisone- and deflazacort-treated boys but not vamorolone (12-month LSM difference in height Z-scores [95% CI]: vamorolone vs prednisone 0.57 [0.24-0.90]; vamorolone vs deflazacort 0.72 [0.53-0.91]). DISCUSSION: Vamorolone demonstrated numerically similar TTSTAND velocity changes to prednisone and deflazacort at 1 year; however, interpretations of differences are limited by 95% CIs crossing minimally important difference thresholds. Further evidence of the growth-protective effect of vamorolone was observed; however, all treatments increased BMI. Vamorolone provides a linear growth-protective classic corticosteroid alternative. TRIAL REGISTRATION INFORMATION: NCT03439670; NCT01603407. CLASSIFICATION OF EVIDENCE: This Class III study did not definitively identify differences in efficacy between vamorolone and classic corticosteroids but found that vamorolone protects linear growth in boys with DMD.
30 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 →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 →European journal of pediatrics
Associations of socioeconomic status and nurturing environment with developmental outcomes in very preterm children without sensorimotor impairment: a secondary analysis of the INTACT study
UNLABELLED: The objective of this study is to retrospectively evaluate the associations between socioeconomic status (SES), including nurturing environment, and developmental quotients (DQ) at age 3 in children born very preterm with birth weight ≤ 1500 g. This secondary analysis used follow-up data from the Improvement of NICU Practices and Team Approach Cluster Randomized Controlled Trial. Participants born < 32 weeks' gestation without major sensorimotor impairments were stratified into three groups: 22-24, 25-27, and 28-31 weeks. Multiple linear regression evaluated the associations between prenatal SES and DQ scores of the Kyoto Scale of Psychological Development (KSPD), adjusted for sex, parity, gestational age, small for gestational age, multiple births, and facilities. Post-discharge SES variables were subsequently added to assess associations with nurturing environment. Of 2148 eligible survivors, 1978 underwent KSPD testing and 1846 caregivers completed SES questionnaires. Higher parental education was positively associated with total DQ across all groups: 22-24 weeks (β = 2.246, 95% CI 0.015-4.477), 25-27 weeks (β = 1.589, 95% CI 0.408-2.770), and 28-31 weeks (β = 1.299, 95% CI 0.315-2.282). Group childcare utilization was associated with higher total DQ: 22-24 weeks (β = 3.397, 95% CI 0.893-5.901), 25-27 weeks (β = 1.851, 95% CI 0.591-3.111), and 28-31 weeks (β = 2.617, 95% CI 1.613-3.620). These associations remained after adjustments for postnatal morbidities. CONCLUSION: Among children born very preterm without major sensorimotor impairments, parental education and group childcare utilization were independently associated with more favorable cognitive outcomes at age 3. These results suggest the importance of socioeconomic and environmental support in optimizing developmental outcomes for this vulnerable population. WHAT IS KNOWN: • Prenatal and postnatal SES are associated with developmental outcomes in both preterm and term children. In addition, daily group childcare environments influence child development in the general population. WHAT IS NEW: • Among very preterm children without major sensorimotor impairments, including those born at 22-24 weeks' gestation, parental education and group childcare utilization are independently associated with more favorable cognitive and language outcomes at age 3.
25 May 2026
Read appraisal →The Cochrane database of systematic reviews
Gastrostomy plus fundoplication or gastro-jejunal tube or gastrostomy alone for treating or preventing gastro-esophageal reflux in children and adolescents with neurological impairment
RATIONALE: Children with neurological impairment (NI) represent a vulnerable pediatric population who often experience multiple co-occurring chronic conditions and may develop worsening comorbidities over time due to primary dysfunctions of the nervous system. Among the most significant of these are gastro-esophageal reflux (GER) and dysphagia, which likely account for much of the associated morbidity and mortality. There is substantial variation in the management of GER, and few studies have been conducted to evaluate the effects of antireflux procedures in preventing and improving outcomes in this population. OBJECTIVES: To assess the benefits and harms of the two most common enteral feeding tubes and associated antireflux procedures. We aimed to compare: 1) gastrostomy tube (GT) plus fundoplication, 2) gastrostomy plus insertion of a gastro-jejunal (GJ) tube, and 3) GT alone for treating or preventing GER in children and adolescents with NI. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, six other databases, and two trials registers, together with reference checking and contact with two experts in the field, to identify studies for inclusion in the review. There were no restrictions on language. The latest search date was 17 September 2024. ELIGIBILITY CRITERIA: Randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSIs) in children with NI, with or without GER, comparing the three interventions. We excluded studies of neurologically normal children. OUTCOMES: Our critical outcomes were symptoms of GER and mortality. Important outcomes included major surgical complications (perforation or peritonitis), length of stay (LOS), number of hospitalizations for respiratory morbidity (pneumonia), number of emergency department (ED) visits, and child's quality of life (QoL). RISK OF BIAS: We used the ROBINS-I tool to assess bias in NRSIs. We did not find any RCTs. SYNTHESIS METHODS: We meta-analyzed the results for each outcome where possible (inverse-variance, random-effects). Where this was precluded by the nature of the data, we synthesized results according to Synthesis Without Meta-analysis (SWiM) guidelines. We used GRADE to assess the certainty of evidence. INCLUDED STUDIES: We included 11 NRSIs with a total of 3122 children with NI. Among these, seven cohort studies compared GT plus fundoplication versus GT alone (2654 participants); one cohort study compared GJ tubes versus GT alone (50 participants); and three cohort studies compared GT plus fundoplication versus GJ tubes (418 participants). SYNTHESIS OF RESULTS: GT plus fundoplication compared to GT alone for GER in children with NI The evidence is very uncertain about the effect of GT plus fundoplication on symptoms of GER (odds ratio [OR] 2.02, 95% confidence interval [CI] 0.64 to 6.44; 3 NRSIs, 180 participants; very low-certainty evidence); mortality (OR 2.62, 95% CI 0.41 to 16.80; 3 NRSIs, 415 participants; very low-certainty evidence); major surgical complications (OR 2.61, 95% CI 0.46 to 14.87; 3 NRSIs, 412 participants; very low-certainty evidence); and LOS (1 NRSI with no comparison, 130 participants; very low-certainty evidence). GT plus fundoplication may result in little to no difference in the number of hospitalizations for pneumonia (mean difference [MD] 0.04 higher, 95% CI 0.01 lower to 0.09 higher; 1 NRSI, 2054 participants; low-certainty evidence). The evidence is very uncertain about the effect of GT plus fundoplication on the number of ED visits (OR 1.82, 95% CI 0.78 to 4.27; 1 NRSI, 130 participants; very low-certainty evidence). Child's QoL was not reported. GJ tubes compared to GT alone for GER in children with NI The evidence is very uncertain about the effect of GJ tubes on mortality (OR 5.38, 95% CI 0.40 to 73.09; 1 NRSI, 50 participants; very low-certainty evidence) and child's QoL (effect estimate not reported; no effect of the type of tube on quality of life; 1 prospective NRSI, 50 participants; very low-certainty evidence). Symptoms of GER, major surgical complications, LOS, number of hospitalizations for respiratory morbidity (pneumonia), and number of ED visits were not reported. GT plus fundoplication compared to GJ tubes for GER in children with NI The evidence is very uncertain about the effect of GT plus fundoplication on symptoms of GER (3 NRSIs with conflicting results, including 1 unmatched cohort, 1531 participants; very low-certainty evidence); mortality (OR 1.12, 95% CI 0.52 to 2.41; 3 NRSIs, 418 participants; very low-certainty evidence); major surgical complications (OR 2.84, 95% CI 0.45 to 17.82; 2 NRSIs, 190 participants; very low-certainty evidence); and LOS (1 NRSI with no comparison, 79 participants; very low-certainty evidence). GT plus fundoplication may result in little to no difference in the number of hospitalizations for pneumonia (MD 0.05 lower, 95% CI 0.21 lower to 0.11 higher; 1 NRSI, 228 participants; low-certainty evidence). Number of ED visits and child's QoL were not reported. We downgraded the certainty of evidence for risk of bias and imprecision. Most studies were at serious risk of bias due to confounding, except for hospitalizations for pneumonia for the comparisons of GT plus fundoplication versus GT alone and versus GJ tubes. AUTHORS' CONCLUSIONS: In children with NI, the evidence is very uncertain about the effect of GT plus fundoplication on symptoms of GER, mortality, major surgical complications, and LOS, when compared to GT alone or GJ tubes (very low-certainty evidence). GT plus fundoplication may result in little to no difference in the number of hospitalizations for pneumonia when compared to GT alone or GJ tubes (low-certainty evidence). The evidence is very uncertain about the effect of GT plus fundoplication on the number of ED visits when compared to GT alone (very low-certainty evidence). The evidence is very uncertain about the effect of GJ tubes on mortality and child's QoL when compared to GT alone (very low-certainty evidence). We found no RCTs, and our results should be interpreted with caution due to the limited number of studies and the limitations of NRSIs. Additional research is necessary. It is likely that RCTs will be difficult to conduct; however, better-designed NRSIs could improve the quality of evidence in this area. FUNDING: This review was funded by Foundation Fresno, Universidad Católica de Chile, for librarian support only. The foundation had no role in the design or conduct of this review. REGISTRATION: Protocol available via DOI: 10.1002/14651858.CD015007.
21 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Normal polysomnography parameters in healthy children: a systematic review and meta-analysis
INTRODUCTION: There is a lack of normative values for polysomnographic parameters in childhood. This study presents a comprehensive meta-analysis of paediatric polysomnography parameters scored using recent criteria to establish normative values adjusted for age and sex. METHODOLOGY: A systematic search was conducted in Web of Science and Scopus for studies that performed overnight polysomnography in healthy children and reported polysomnographic parameters scored using the American Academy of Sleep Medicine criteria (2007 or 2012). Children with previously diagnosed health conditions were excluded. Estimates for sleep, arousals and cardiorespiratory parameters were pooled using a random-effects meta-analysis and the influence of sex and age was assessed using meta-regression. RESULTS: Of 3612 articles, 66 studies were eligible, resulting in a sample size of 2947 healthy children. Pooled estimates and 95% confidence intervals for 34 polysomnographic parameters were established. Meta-regression revealed age-related decrease in total sleep time (TST), sleep period time, TST in stage N3 and REM, number of sleep cycles, total arousals, total leg movements, periodic leg movement index, central apnoea index, apnoea-hypopnoea index (AHI), mean peripheral oxygen saturation (S pO2 ), mean heart rate in sleep and REM, while the number of awakenings, TST in stage N2, number of stage shifts, and S pO2 nadir increased with age. Sleep latency was the only parameter influenced by sex (change of -1.27 min per 10% increase in the proportion of males). CONCLUSION: This meta-analysis provides normative polysomnographic data for the paediatric population that can be used as control values in both clinical and research context.
15 May 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacological treatments for Friedreich ataxia
RATIONALE: Friedreich ataxia (FRDA) is a rare inherited autosomal recessive neurological disorder, characterised initially by unsteadiness in standing and walking, and slowly progressing to wheelchair dependency, usually in the late teens or early twenties. Scoliosis, pes cavus and cardiomyopathy are often present at diagnosis. As the disease progresses, individuals usually develop slurred speech, auditory impairment (especially in a noisy environment), urinary tract morbidity, diabetes, anxiety, depression, muscle spasticity and visual disturbances. Cardiac abnormalities cause premature death in 60% of people with FRDA. There is no easily defined clinical or biochemical marker to assess progression and no known curative treatment. This is the third update of a review published in 2009 and updated in 2012 and 2016. OBJECTIVES: To assess the effects of pharmacological treatments for people with Friedreich ataxia (FRDA) after 12 months of treatment. SEARCH METHODS: To identify studies for inclusion in this review, we searched CENTRAL, MEDLINE, Embase, CINAHL Plus, TRIP, Orphanet, WHO International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov. We also checked reference lists of relevant studies and contacted study authors. The most recent search was on 4 February 2025. ELIGIBILITY CRITERIA: We were interested in randomised controlled trials (RCTs) and quasi-RCTs of pharmacological treatments (including vitamins) in people with genetically-confirmed FRDA. To be included in the review, studies had to last at least 12 months. OUTCOMES: We assessed the following outcomes after 12 months of treatment: change in score on a validated ataxia rating scale; change in interventricular septal thickness in diastole (IVSTd) by cardiac magnetic resonance imaging or echocardiogram; change in activities of daily living (ADL) using a validated questionnaire; change in upper limb dexterity; and change in cardiopulmonary exercise testing (CPET). We also assessed treatment-related adverse events with medication continued for the study period, and treatment-emergent adverse events leading to cessation of medication or death during the study period. RISK OF BIAS: Using the Cochrane risk of bias tool RoB 2, we assessed the risk of bias in the seven outcomes reported in our summary of findings tables. SYNTHESIS METHODS: We synthesised the results of the studies for each outcome using meta-analysis, where possible. We calculated the mean difference (MD) or standardised mean difference (SMD) for continuous outcomes, and the risk ratio (RR) for dichotomous outcomes, all with 95% confidence intervals (CIs). We used GRADE to assess the certainty of evidence for our prespecified outcomes as high, moderate, low or very low. INCLUDED STUDIES: We included eight RCTs in this updated review. We included seven of them in meta-analysis, and these studies enroled a total of 574 participants, with sample sizes ranging from 29 to 232 participants per study. One study was international, with centres in North America, Europe and Australia. There were four other multicentre studies (three in Europe and one in North America). Single-centre studies were conducted in the UK, Italy and France. Participants in the studies ranged from eight to 70 years of age at enrolment, with the mean age in each study being between 18 years and 35 years (with an unweighted pooled mean age across studies of 25.9 years). All of the studies enroled both males and females, with the proportion of female participants ranging from 21% to 58%. Sixty-eight per cent of participants had severe ataxia, while the other 32% had moderate ataxia. The studies tested epoetin alpha, CoQ10 and vitamin E, idebenone, leriglitazone, omaveloxolone, and RT001. One study tested pioglitazone but has not published any results. Four of the studies were pharmaceutical-industry-controlled. SYNTHESIS OF RESULTS: Meta-analysis of seven studies demonstrated that pharmacological treatment probably makes little or no difference to scores on the ataxia rating scale after 12 months of treatment (SMD 0.02, 95% CI -0.23 to 0.26; I² = 42%; 7 studies, 513 participants; moderate-certainty evidence). The evidence was very uncertain about the effects of treatment on IVSTd (MD -0.51, 95% CI -1.10 to 0.09; I² = 80%; 2 studies, 72 participants; very low-certainty evidence) and on ADL (MD -0.59, 95% CI -1.39 to 0.21; I² = 24%; 3 studies, 167 participants; very low-certainty evidence). Meta-analysis of three studies showed that treatment probably improves upper limb dexterity (SMD -0.42, 95% CI -0.73 to -0.11; I² = 0%; 3 studies, 166 participants; moderate-certainty evidence). We are very uncertain about the effect of pharmacological treatment on CPET (SMD -0.16, 95% CI -0.46 to 0.13; I² = 0%; 3 studies, 181 participants; very low-certainty evidence). We are very uncertain whether pharmacological treatment has any effect on treatment-related adverse events (RR 0.88, 95% CI 0.63 to 1.22; I² = 0%; 2 studies, 104 participants; very low-certainty evidence). There were only 104 participants in this analysis out of a total of 545 participants, so the result may have little relevance. Meta-analysis of six studies found that there may be little to no difference between pharmacological treatment and placebo in treatment-emergent adverse events leading to cessation of medication or death (RR 1.24, 95% CI 0.44 to 3.48; I² = 0%; 6 studies, 313 participants; low-certainty evidence). Our certainty in the evidence ranged from very low to moderate. We downgraded all outcomes by one or two levels for imprecision, with further downgrades for inconsistency (for the outcomes of IVSTd, ADL, CPET) and suspected publication bias (for the outcomes of IVSTd, ADL, CPET and adverse events). AUTHORS' CONCLUSIONS: In this updated Cochrane systematic review, meta-analysis of results on the ataxia rating scale showed that pharmacological treatments probably make little or no difference compared with placebo after 12 months of treatment. Given this result, the probable improvement that we found in upper limb dexterity was unexpected. Treatment-emergent adverse events leading to cessation of medication or death may be no more common in treatment groups than placebo groups as there were few adverse events detected in the treated groups. However, the studies may not have detected all rare and serious adverse events. FUNDING: The authors in this review received no funding. REGISTRATION: Protocol (2009) DOI: 10.1002/14651858.CD007791 Original review (2009) DOI: 10.1002/14651858.CD99791.pub2 Update (2012) DOI: 10.1002/14651858.CD007791.pub3 Update (2016) DOI: 10.1002/14651858.CD007791.pub4.
14 May 2026
Read appraisal →Current pain and headache reports
Addressing the Treatment Gap for Pediatric Headache in Underserved Regions
PURPOSE OF REVIEW: This review explores innovative strategies to address the treatment gap for pediatric headache disorders in underserved regions worldwide. RECENT FINDINGS: Global Burden of Disease studies indicate that the fastest growth in pediatric headache burden occurs in low- and middle-income countries, where reported prevalence likely underestimates true disease burden due to healthcare access barriers. Telemedicine demonstrates comparable efficacy, safety, and satisfaction to in person care. Artificial intelligence based diagnostic models achieve high accuracy for pediatric migraine identification. School-based interventions leveraging existing educational infrastructure improve headache knowledge and coping behaviors. Task shifting and integrated care delivery models are increasingly recognized as essential components of sustainable care. Telemedicine, artificial intelligence, school-based interventions, task shifting, and integrated care networks represent scalable solutions for extending headache care to underserved pediatric populations. Realizing their potential requires integrating technological innovation with workforce capacity building, community engagement, and policy advocacy.
14 May 2026
Read appraisal →Journal of chemical information and modeling
Examining Genetic Variants Associated with FOXP1 Syndrome through Molecular Dynamics of Its DNA-Binding Domain and Self-Organizing Maps
Genetic mutations in the transcription factor FOXP1 (forkhead box protein P1) cause an autosomal dominant neurodevelopmental disorder called FOXP1 syndrome. To understand the structural impact of pathogenic variants associated with FOXP1 syndrome, we investigated the conformational changes resulting from six distinct missense variants in FOXP1 by combining molecular dynamics simulations, molecular docking, and machine learning via self-organizing maps. Our results reveal different conformational landscapes mapped by the FOXP1 variants and reduced interactions with the DNA for mutations residing in helix H3 of the DNA-binding domain. These analyses offer a framework for assessing the structural impact of missense variants implicated in the FOXP1 syndrome, highlighting the importance of structural inferences in interpreting genetic variants.
13 May 2026
Read appraisal →Epilepsy research
Independent evaluation of deep learning models for detecting focal cortical dysplasia
The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) from Magnetic Resonance images (MRI). These tools include DeepFCD, the Multi-center Epilepsy Lesion Detection (MELD) Classifier, and MELDGraph. T1-weighted and fluid-attenuated inversion recovery MRIs from 101 epilepsy patients with FCD and 101 epilepsy patients without FCD were retrospectively included. Classifiers were evaluated at patient-level by their ability to correctly identify the presence of any FCD lesions, and at lesion-level by their capacity to identify lesions within regions delineated by neuroradiologists in MRI reports. A calibrated threshold for DeepFCD prediction probabilities was empirically determined to improve classifier specificity. Classifier test-retest consistency was measured using the Dice coefficient on repeated MRI scans of 21 individuals. At patient-level, MELDClassifier achieved 52% accuracy (sensitivity=91%, specificity=14%), MELDGraph reached 61% accuracy (sensitivity=76%, specificity=47%) and DeepFCD performed with 56% accuracy (sensitivity=62%, specificity=50%) at an empirically determined threshold of 0.90. At lesion-level, MELDClassifier performed with a sensitivity of 70% and a positive predictive value (PPV) of 13%. MELDGraph reached 53% sensitivity and PPV of 36%, whereas the DeepFCD performed with 30% sensitivity and PPV of 19%. Test-retest reliability was low, with an average [min, max] Dice coefficient of 0.28 [0.0, 1.0] for MELDClassifier, 0.38 [0.0, 1.0] for MELDGraph, and 0.35 [0.05, 0.54] for DeepFCD. This study highlights the current limitations of using deep learning models in FCD diagnosis and emphasizes the need to enhance the tools' accuracy, reliability, and interpretability to improve clinical utility.
9 May 2026
Read appraisal →European journal of pediatrics
Evaluation of the quality, reliability, and readability of ChatGPT-4 responses related to the treatment and rehabilitation of children with cerebral palsy
UNLABELLED: This study aims to evaluate the reliability, quality, and readability of ChatGPT-4's responses to questions about cerebral palsy (CP) and rehabilitation strategies. The 56 most frequently asked questions by families about CP, its treatment, and rehabilitation strategies were divided into five categories (A1-5) and asked in ChatGPT-4. The reliability, quality, and readability of the responses were assessed by two researchers, respectively, using the modified DISCERN (mDISCERN) tool, the Global Quality Scale (GQS), and the Flesch Reading Ease Scale (FRE). Median (IQR) values for mDISCERN ranged from 3 (3-3) in A5 to 4 (3-4) in A1, while GQS values ranged from 3 (3-3.5) in A2 to 3.5 (3-4) in A1. The mean readability values assessed with FRE ranged from 31.47 ± 12.92 to 44.84 ± 17.39. No statistically significant differences were observed between the categories. The ICC values suggested very good agreement for both scales, with 0.851 for the mDISCERN total score and 0.824 for the GQS total score. CONCLUSION: This study suggests that ChatGPT-4 provides moderate reliability and generally acceptable quality of responses regarding CP treatment and rehabilitation. FRE scores indicated that many responses were difficult for families to understand. While ChatGPT-4 may serve as a supportive source of general information, its outputs should be interpreted with caution, particularly in clinical contexts, and supervision and oversight by healthcare professionals remain essential for safe and effective use. WHAT'S KNOWN: • Children with cerebral palsy (CP) receive lifelong rehabilitation. Exercise plays a fundamental role in maintaining functional independence and quality of life, as well as physical development. The use of AI-based tools such as ChatGPT-4 for health information is increasingly widespread; however, uncertainties regarding the reliability, quality,and readability of these tools remain. WHAT IS NEW: • Our study shows that ChatGPT-4''s responses to questions about treatment and rehabilitation strategies for children with CP have moderate to good reliability and quality, but low readability.
27 Apr 2026
Read appraisal →Nature communications
Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids
Leigh syndrome (Leigh) is an untreatable mitochondrial disorder characterized by lactic acidosis and basal ganglia and midbrain pathology, leading to psychomotor regression and early death. We previously uncovered impaired neuronal morphogenesis in Leigh cerebral organoids carrying SURF1 gene variants. Leveraging this phenotype, we here develop a deep learning algorithm tailored for cell type-specific drug repurposing screening. In parallel, we perform a survival drug screen in a yeast model of Leigh. The two approaches independently converge on azole compounds, two of which - talarozole and sertaconazole - rescue neuronal morphogenesis in Leigh neurons and lower lactate release and improve growth rate in Leigh midbrain organoids. Mechanistically, these compounds modulate the retinoic acid pathway and membrane-associate lipid metabolism. The findings highlight azoles as promising candidates for Leigh and demonstrate the potential of combining in silico screens with human brain organoids as new approach methodologies (NAMs) to advance the discovery of therapeutics addressing rare neurodevelopmental disorders.
22 Apr 2026
Read appraisal →HGG advances
Gene-specific pathogenicity predictor for chromatin remodeling BAF complex-associated neurodevelopmental disorders
Advancements in whole-genome sequencing have increased the number of variants of uncertain significance (VUS) identified in human genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BRG1/BRM-associated factor (BAF) complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and area under the precision-recall curve than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.
12 Apr 2026
Read appraisal →Cell genomics
Variant-resolved prediction of context-specific isoform variation with a graph-based attention model
In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, we introduce Otari, an attention-based graph neural network framework trained on the human genomic sequence and long-read transcriptomes across 30 tissue types and brain regions. Otari predicts tissue-specific differential isoform abundance by integrating sequence-derived epigenetic and post-transcriptional signals, enabling isoform-resolved variant effect interpretation. Applied to large-scale variant datasets, including an autism cohort, Otari uncovers patterns of isoform dysregulation undetectable at the gene level, such as variant-driven perturbations in isoform abundance and microexon usage implicated in autism pathophysiology. We provide Otari as a resource for powering isoform-level analyses across tissues at scale.
11 Apr 2026
Read appraisal →Brain : a journal of neurology
Distinct cerebellar networks underpin clinical improvement in adolescent Tourette disorder.
Adolescence is frequently called the second period of brain maturation. In Tourette disorder (TD), the clinical trajectory of tics and associated psychiatric co-morbidities varies significantly across individuals during the transition from adolescence to adulthood. In this study, we aimed to identify patterns of resting-state functional connectivity that differentiate adolescents with Tourette disorder from their neurotypical peers, and to monitor symptom-specific functional changes over time. We employed a multivariate analysis based on a support vector machine (SVM) to predict patterns of resting-state functional connectivity in 64 adolescents with Tourette disorder and 61 sex- and age-matched healthy controls. Fifty-seven patients with Tourette disorder participated twice, with an interval of 15 months. Based on the results of the multivariate analysis, we implemented a general linear model to test within-group differences in resting-state functional connectivity over time, as well as their correlation with changes in severity of tics, depressive, obsessive-compulsive (OCD) and attention deficit and hyperactivity (ADHD) symptoms. The SVM significantly discriminated Tourette disorder from healthy controls with above-chance accuracy, specificity and sensitivity. The most discriminative connections were prefrontal, striatal and cerebellar networks. Between the visits, adolescents with Tourette disorder showed decreased functional connectivity between vermis 3 and the bilateral supplementary motor area and between vermis 4, 5 and the occipital cortex. The correlation analysis revealed that (i) an improvement between the visits in the severity of tics was associated with increased connectivity between the pre-supplementary motor cortex, anterior cingulate and inferior frontal gyrus; (ii) an improvement of ADHD was associated with decreased connectivity of the anterior cingulate cortex with the inferior temporal cortex; (iii) an improvement of OCD correlated with decreased cerebellar (lobule 8 and vermis 8, 9) connectivity with fronto-polar, superior temporal and superior frontal cortices, and increased cerebello-parietal connectivity, as well as fronto-parietal and fronto-frontal connectivity; and (iv) an improvement of depressive symptoms correlated with decreased connectivity of the cerebellum (lobules 4, 5 and vermis 6) with the precuneus. Our findings support pathophysiological models of Tourette disorder, in which aberrant patterns of functional connectivity are associated with specific comorbidities that may evolve differently throughout adolescence. Resting-state functional connectivity may provide unique endophenotypes, with developmental changes potentially linked to modifications in the severity of comorbid conditions. Distinct regions of the cerebellum and their connectivity with various frontal cortical regions emerge as candidate biomarkers to monitor, and possibly predict, the clinical trajectory of Tourette disorder symptoms, offering insights into disorder pathogenesis and likely guiding clinical decision-making.
9 Apr 2026
Read appraisal →Current opinion in neurology
AI in epilepsy neuroimaging
PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability of neuroimaging datasets has fuelled a rapid expansion in AI applications to epilepsy neuroimaging. This review summarizes the main applications of AI in epilepsy neuroimaging and suggests future directions for the field. RECENT FINDINGS: A range of different machine learning approaches, from multi-layer perceptrons to volumetric and graph-based convolutional neural networks, have been utilized for prediction of whether people will have epilepsy, detection of structural epilepsy lesions, localization of seizure onset zones, segmentation of resection cavities after epilepsy surgery as well as for image enhancement. SUMMARY: AI in epilepsy neuroimaging research has primarily focussed on lesion detection and localization, with a number of open and validated tools now available for evaluation across diverse settings. Additional applications of AI in epilepsy neuroimaging are either at earlier stages of development or emerging as new challenges. As these tools and their supporting evidence mature, further work addressing the hurdles of clinical integration is required.
4 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
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