Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
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 →Neurosurgical review
Management of pediatric brain arteriovenous malformation: a systematic review of retrospective studies
INTRODUCTION: Pediatric brain arteriovenous malformations (bAVMs) are a major cause of hemorrhagic stroke in children, yet management decisions rely largely on heterogeneous cohorts. Contemporary retrospective studies were systematically reviewed to summarize outcomes across treatment modalities and to characterize interstudy variability. METHODS: PubMed/MEDLINE was searched over the past decade for retrospective pediatric intracranial bAVM cohorts. Eligible studies reported management with observation/conservative care, endovascular embolization, microsurgical resection, stereotactic radiosurgery (SRS), or multimodality therapy. Screened studies had data extracted using a standardized form. Outcomes included obliteration, hemorrhage (first presentation and post-treatment), functional outcomes, complications, and recurrence after apparent cure. Treatment categories were harmonized into mutually exclusive groups for descriptive synthesis. RESULTS: Of 113 identified records, 20 retrospective cohort studies met the inclusion criteria. Hemorrhage was the most common presentation. Across cohorts reporting Spetzler-Martin (SM) grade, low-grade lesions (SM I-II) comprised 740 patients (44.1%), SM III 645 (38.4%), and SM IV-V 293 (17.5%). Treatment strategies varied substantially; among 1,973 classifiable patients, cohorts reported microsurgery-only, SRS-only, endovascular-only, multimodality, and conservative approaches. Definitions of obliteration and assessment methods were inconsistent (DSA-confirmed vs. MRI-based). Microsurgical series reported obliteration rates of 81%-100% in selected populations (unweighted mean ~ 93% across reporting cohorts), whereas SRS-only cohorts demonstrated obliteration rates of 51%-68.5% in the three largest unselected pediatric series (range 36%-84% across all SRS-inclusive cohorts), with latency-period hemorrhage rates of approximately 1.1%-3.2% per series. Recurrence after apparent cure was reported in 7 cohorts, with rates ranging from < 1% to 29%, occurring years after angiographic obliteration. CONCLUSIONS: Contemporary retrospective pediatric bAVM data indicate that modality-specific outcomes are strongly influenced by lesion selection, outcome definitions, and follow-up duration. Recurrence after apparent cure is not uncommon and supports explicit long-term surveillance. Standardized definitions and harmonized reporting are needed to improve interpretability and comparative inference.
29 May 2026
Read appraisal →European journal of pediatrics
Machine learning-based integration of respiratory oscillometry and spirometry for predicting clinical outcomes in spinal muscular atrophy
UNLABELLED: Assessing respiratory function in spinal muscular atrophy (SMA) is challenging due to the effort-dependent nature of traditional spirometry. While respiratory oscillometry offers a passive alternative, its clinical utility in predicting clinical status remains underexplored. This study evaluates the efficacy of integrating spirometric and respiratory oscillometric indices through machine learning (ML) to predict ambulatory status and the requirement for bilevel positive airway pressure (BiPAP) in SMA patients. We retrospectively analyzed forty-five patients with a genetically confirmed diagnosis of SMA. To address data imbalance, we utilized the SMOTETomek technique and validated Random Forest models through leave-one-out cross-validation. SHAP (SHapley Additive exPlanations) analysis was used to interpret the contribution of individual physiological markers. In univariate logistic regression, forced vital capacity (FVC), peak expiratory flow (PEF), and forced expiratory flow at 75% of vital capacity (FEF75) were significant predictors for walking support. FEF25, FEF50, FEF75, and FEF 25-75 were associated with the requirement for BiPAP use. None of the respiratory oscillometric parameters reached statistical significance in regression models. However, ML models achieved high predictive accuracy, with SMOTETomek improving BiPAP requirement recall from 0.00 to 1.00. SHAP analysis revealed that respiratory oscillometry gained significant predictive weight that traditional models overlooked. R20Hz emerged as a key predictor for ambulatory status, while R5-20 Hz was identified as a decisive feature for predicting the BiPAP use. CONCLUSION: The integration of effort-independent respiratory oscillometry with traditional spirometry via ML has the potential to enhance risk stratification in SMA. WHAT IS KNOWN: • SMA requires lung function monitoring, but traditional spirometry is challenging due to the need for forceful expiratory maneuvers. • Respiratory oscillometry provides an effort-independent assessment of respiratory mechanics through tidal breathing. WHAT IS NEW: • Machine learning successfully integrates effort-independent respiratory oscillometry with spirometry to significantly enhance clinical predictions in SMA. • It identifies R20Hz and R5-20 Hz as critical physiological markers for predicting ambulatory status and the requirement for respiratory support, respectively.
13 May 2026
Read appraisal →Current pain and headache reports
Telemedicine in Pediatric Headache in a Post-COVID-19 World: A Narrative Review and Practical Implementation
PURPOSE OF REVIEW: Pediatric migraine is a leading cause of disability worldwide, yet access to pediatric headache specialists remains limited due to workforce scarcities and geographic disparities. This review examines the evolution of telemedicine in headache care, evaluates current evidence for safety and efficacy, and explores its role in improving access and multidisciplinary management. RECENT FINDINGS: Randomized trials and observational studies demonstrate that telemedicine provides clinical outcomes comparable to in-person visits with high patient satisfaction and reduced logistical burden. Emerging data support its safety in non-acute headache evaluation as well as benefit of supporting multidisciplinary care. Professional societies, including the American Headache Society and the American Academy of Pediatrics, endorse telemedicine as an appropriate modality for headache management. Telemedicine has matured into a sustainable, evidence-based approach for pediatric headache care. When implemented thoughtfully, it expands access, supports multidisciplinary treatment, and maintains safety, positioning it as an essential component of future headache care delivery models.
12 May 2026
Read appraisal →Pediatric neurology
Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy
BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiveness remain underdeveloped. This study evaluated various machine learning (ML) models in predicting responses to KDT among DRE patients based on electroencephalography data. METHODS: Using leave-one-out cross-validation, this study evaluated 19 ML classifiers in predicting the outcomes of 90 DRE patients based on absolute and relative power, as well as functional connectivity measures (phase-locking value, phase lag index [PLI], and weighted PLI) across standard frequency bands. KDT significantly reduced seizure frequency at three and 6 months after initiation. RESULTS: The most effective classifier at 3 months was a Coarse Tree classifier trained on absolute power (recall = 0.933, precision = 0.767, F2 = 0.894, area under the receiver operating characteristic curve = 0.607). The most effective classifier at 6 months was a Gaussian Naive Bayes classifier trained on weighted PLI + relative power (recall = 0.759, precision = 0.854, F2 = 0.776, area under the receiver operating characteristic curve = 0.603). CONCLUSIONS: This study identified the most effective ML models for predicting KDT outcomes in DRE patients. The results highlight the potential of electroencephalography-based ML tools for guiding KDT treatment in clinical practice.
9 May 2026
Read appraisal →Developmental cognitive neuroscience
Deep learning in fetal, infant, and toddler neuroimaging research.
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analysis has progressed more slowly due to high clinical stakes, limited availability of labeled data, and substantial variability in imaging protocols and population. These challenges are further pronounced in the field of fetal, infant, and toddler (FIT) neuroimaging, where datasets are especially scarce and subject to large amounts of anatomical variability. However, deep learning (DL), a specific method within machine learning, which is itself a subfield of AI, has emerged as a powerful framework to adapt to the challenges of medical image analysis. This review is written for the broad FIT research community, including clinicians, neuroscientists, and develop mental scientists who may not have formal training in AI. To make the material accessible, we provide a concise overview of DL concepts before reviewing a selected, and non-exhaustive, list of applications of DL in FIT neuroimaging, including structural image analysis, enhancement of data acquisition, modeling of cognitive and perceptual processes, and automated video tagging. In closing, we discuss best practices for data curation, ongoing challenges, and opportunities for future research.
4 Apr 2026
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