Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:Multiple-choice questions are the primary assessment format for neurosurgical board certification. Creating high-quality examination questions requires significant expert time and resources. The goal of this study was to develop an automated system to generate board-style neurosurgical multiple-choice questions using state-of-the-art vision-language models and compare their quality with authentic self-assessment questions. METHODS:articles. We generated 89 587 synthetic questions: 45 689 with GPT-4o and 43 898 with Claude. Each question was associated with a single image extracted from the articles' figures. We evaluated the quality of synthetic questions through 5 surveys comparing 20 synthetic questions (10 from each model) with 10 authentic questions from the Self-Assessment for Neurological Surgeons (SANS) question bank. Each survey was completed by a neurosurgery resident and an attending who guessed the source [human vs artificial intelligence (AI)-generated] and rated suitability for board examination use. We also evaluated the question-answering performance of the generalist GPT-4o and the specialized CNS-Obsidian. RESULTS:). CONCLUSION/CONCLUSIONS:Although quality gaps exist between AI-generated and human-created neurosurgical board examination questions, our approach demonstrates the potential of vision-language models to augment assessment development in specialized medical fields, reducing the burden on examination boards and credentialing organizations.

INTRODUCTION/BACKGROUND:Juvenile myoclonic epilepsy (JME) is a genetic generalized epilepsy syndrome with onset typically in adolescence and a chronic course requiring long-term antiseizure medications (ASMs). Valproate (VPA) is the most effective treatment for seizure control in JME but use is limited by metabolic, cognitive, and teratogenic adverse effects (AEs). Levetiracetam (LEV) is an alternative ASM when VPA is contraindicated or not tolerated. Comparisons of the efficacy and long-term tolerability of VPA and LEV remain limited. METHODS:statistic. RESULTS: = 21.2%). Sensitivity analyses confirmed the robustness of the pooled estimates. CONCLUSION/CONCLUSIONS:VPA was associated with higher seizure remission rates and lower treatment discontinuation compared with LEV; however, these findings must be interpreted with caution given the substantial heterogeneity, the predominance of observational studies, and the serious risk of bias identified in most included studies VPA also demonstrated lower rates of treatment discontinuation, despite a higher burden of cognitive impairment and weight gain. No relevant differences were observed regarding dizziness. Large-scale randomized trials with standardized outcome definitions and longer follow-up are needed to define the comparative risk-benefit profiles of LEV and VPA in JME.

OBJECTIVE:Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC. MATERIALS AND METHODS/METHODS:The study included adult participants with linked FHIR-sourced EHR data. We characterized EHR diagnoses, procedures, medications, lab tests, and vital sign features associated with LC. A computable phenotyping algorithm was trained and validated against patient-reported symptoms. MAIN OUTCOME AND MEASURES/METHODS:We assessed model discrimination and calibration in a held-out test set. We describe important model features and evaluate model discrimination and calibration. RESULTS:The study included 1,501 RECOVER-Adult cohort participants with linked EHR data. 376 (25%) met criteria for highly symptomatic LC based on the RECOVER Long COVID Research Index (LCRI). EHR features associated with LC included clinician diagnosis of shortness of breath, malaise and fatigue, and cardiac dysrhythmias; documented treatment with albuterol, gabapentin, or duloxetine; or elevated heart rate. The algorithm identifying patients with highly symptomatic LC had an AUROC of 0.80 (95% confidence interval (CI) 0.74-0.85), and AUPRC of 0.58 (95% CI, 0.47-0.69). CONCLUSION AND RELEVANCE/CONCLUSIONS:These findings demonstrate that, using EHR data, a machine-learning model can accurately select patients with sets of self-reported LC symptoms. The model could help identify patients within a health system with the highest probability of the condition and facilitate screening, recruitment for clinical trials, and etiologic studies.

BACKGROUND:The risk of intracerebral hemorrhage (ICH) hematoma expansion (HE) is highest in the first hours after onset, and coagulopathy is believed to further increase this risk. However, there is a paucity of data comparing the risk of HE over time for various antithrombotic agents. METHODS:We conducted a retrospective study of spontaneous ICH patients enrolled at a comprehensive stroke center between December 2016 and May 2022, excluding those who underwent surgical evacuation. ICH volumes were calculated using ABC/2 methodology and HE was coded for a ≥ 33% and/or ≥ 6-mL increase in ICH volume. Multivariable logistic regression and Cox proportional hazards models were constructed to evaluate risk of HE among patients exposed to the following antithrombotics: aspirin, P2Y12 inhibitors, aspirin + P2Y12 inhibitors, warfarin, oral factor Xa inhibitors, direct thrombin inhibitors, full dose heparinoids, and combined antiplatelet + anticoagulant. RESULTS:Of 319 patients with ICH, 141 (44%) were on an antithrombotic at the time of ICH and 65 (20%) had HE in a median of 10 h (interquartile range (IQR) 7-21) from last known normal (LKN). In multivariable logistic regression analyses, the odds of HE decreased by 2% for every hour from LKN (adjusted odds ratio (aOR) 0.98, 95% CI 0.97-0.99, P = 0.038), and HE occurred significantly more often in patients taking combined antiplatelet + anticoagulant (9/24, 38%) compared with those who were not (56/295, 19%, aOR 2.71, 95% CI 1.09-6.73, P = 0.032). No other antithrombotic was significantly associated with HE. In multivariable Cox analysis adjusting for admission National Institutes of Health Stroke Scale (NIHSS), only antiplatelet + anticoagulant use was associated with significantly increased rates of HE (adjusted HR (aHR) 2.33, 95% CI 1.14-4.75, P = 0.020), with the highest probability of HE occurring immediately after ICH onset. CONCLUSIONS:Use of combined antiplatelet + anticoagulant was associated with a twofold increased hazard of HE, with the highest probability of expansion occurring early after ICH onset. No increased rate of HE was observed for other antithrombotics.

PURPOSE OF REVIEW/OBJECTIVE:Aberrant activation of the RAS-RAF-MEK-ERK signaling pathway contributes to numerous malignancies, congenital syndromes, and cardiovascular disorders, establishing MEK inhibitors (MEKi) as crucial therapeutic agents. MEKi demonstrate substantial clinical benefit as monotherapy (e.g., NF1-associated tumors) or combination therapy (e.g., melanoma, glioma, congenital RASopathies). Cardiovascular adverse events, primarily asymptomatic reductions in left ventricular ejection fraction (LVEF), have prompted intensive echocardiographic monitoring. RECENT FINDINGS/RESULTS:Emerging evidence indicates that isolated mild LVEF decreases rarely progress to symptomatic heart failure, raising concerns about unnecessary treatment interruptions driven by overly rigorous imaging protocols. Integrating cardiac biomarkers into monitoring strategies could offer a more pragmatic approach, guiding echocardiographic evaluations based on biomarker elevation or symptoms. Expert recommendations advocate personalized, risk-adapted surveillance frameworks. High-risk patients include those with baseline cardiovascular conditions, abnormal biomarker profiles, or significant clinical symptoms. Normal-risk patients can be safely monitored clinically, reserving imaging for biomarker changes or symptom onset. Prospective studies should seek to validate these recommendations.

PURPOSE OF REVIEW/OBJECTIVE:The coronavirus disease 2019 pandemic, shifting health policy and increasing vaccine hesitancy have all increased the likelihood healthcare practitioners will encounter vaccine-preventable diseases in children. Both front-line and consulting clinicians need to be able to identify these previously rare diseases and their neurologic complications, particularly in un- or under-vaccinated children. RECENT FINDINGS/RESULTS:As diseases previously considered rare or eliminated reemerge, more is known about direct and indirect consequences of peripheral and central nervous system infection in children-both in general and specific to individual vaccine-preventable diseases. Primary and secondary neurologic complications can be acute, subacute, chronic, or arise years later. Advances in imaging and molecular identification along with better understanding of underlying disease pathophysiology can all aid earlier identification, treatment, prognostication, and recovery. SUMMARY/CONCLUSIONS:While the benefits of childhood vaccination programs clearly outweigh the risks for the majority of children, in light of current realities the goal of this guide is to better prepare front-line and consulting clinicians to identify and manage vaccine-preventable diseases and their neurologic complications in un- or under-vaccinated children as they increasingly encounter them now and in the future.

BACKGROUND:People with multiple sclerosis (pwMS) treated with anti-CD20 monoclonal antibodies have an elevated infection risk, yet long-term comparative data with oral fumarates are limited. OBJECTIVE:To compare infection incidence, healthcare resource utilization, and relapse outcomes among pwMS receiving fumarate or anti-CD20 therapy for ⩾2 years. METHODS:This retrospective propensity-matched (1:2) analysis used US Komodo Health claims (1 January 2016-31 May 2022) for pwMS aged 18-64 years with ⩾2 years of continuous follow-up. Outcomes were assessed using Poisson generalized linear models. RESULTS: = 0.021). CONCLUSIONS:Fumarate therapy was associated with a lower infection risk versus anti-CD20s. The infection rate in the fumarates group remained relatively stable over time, but it progressively increased in the anti-CD20 group, with the most prominent differences observed at year 4 and beyond.

OBJECTIVE:To assess the efficacy and safety of fenfluramine in patients with Dravet syndrome (DS) stratified by age, number of previously attempted antiseizure medications (ASMs), and SCN1A pathogenic variant status. METHODS:In this post hoc analysis, data from three randomized controlled trials (RCTs) in patients with DS (2-18 years) were pooled and stratified by age (<4; ≥4 years), number of previous ASMs (1-3; 4-6; ≥7), and SCN1A pathogenic variant status (SCN1A+; SCN1A-). Stratified groups were assessed and compared with the pooled placebo group (change in monthly convulsive seizure frequency [MCSF], longest convulsive seizure-free interval, and Clinical Global Impression-Improvement [CGI-I] scale scores rated by parents/caregivers and investigators), and safety (treatment-emergent adverse events [TEAEs]: frequency, days to onset, and proportion resolved). RESULTS:Among 348 patients included in the RCTs, 216 were randomized to fenfluramine (0.7 mg/kg/day, n = 88; 0.4 mg/kg/day [with stiripentol], n = 43; 0.2 mg/kg/day, n = 85) and 132 to placebo. Compared with placebo, fenfluramine treatment (all doses combined) resulted in greater MCSF reductions, greater increases in longest convulsive seizure-free intervals, and a higher proportion of parents/caregivers and investigators reporting clinically meaningful improvement ("Much Improved", "Very Much Improved") on CGI-I scores across all stratified groups. CGI-I scores were consistent across fenfluramine doses in most stratified groups, but patients with the fewest number of previous ASMs had the greatest frequency of clinically meaningful improvement on investigator-rated CGI-I scores. Safety outcomes were similar across all strata. Most TEAEs resolved by end-of-study. SIGNIFICANCE/CONCLUSIONS:Fenfluramine treatment was associated with improved seizure outcomes and global functioning compared with placebo regardless of age, number of previous ASMs, and SCN1A status in patients with DS. Fenfluramine was well-tolerated; no new safety signals were identified. Further studies with larger sample sizes (including adults) and a priori inferential analyses of stratified groups are warranted. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:Patients with Dravet syndrome struggle with seizures and everyday life. In three studies, patients aged 2-18 years received fenfluramine or placebo (sugar pill). Fenfluramine lowered seizures without many side effects. Researchers combined results from these studies to see how fenfluramine worked in different patient groups based on age, number of previous medications, and a gene called SCN1A. They looked at seizure reduction and whether doctors felt patients had improved. In all groups, fenfluramine worked better than placebo, with similar side effects. Researchers believe fenfluramine helped these patients, but some groups were small, so these results need to be confirmed.