Citations

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

Purpose: To understand the current state of epilepsy surgery education delivered to epilepsy fellows in the United States. Methods: An online survey focused on characteristics of epilepsy surgery education was distributed to all 93 epilepsy fellowship program directors listed on the ACGME website (accessed in May 2022). Programs were stratified per the number of fellows currently enrolled: 0 to 3 (group A) and ≥4 (group B). Results: Forty-one of 93 (44%) epilepsy fellowship programs were included in the study. The average number of resective surgeries, ablations, or corpus callosotomies per year was mostly 0 to 30 (54%) in group A and mostly >30 (80%) in group B ( P = 0.05). The average number of intracranial implantations per year was mostly 0 to 20 (58%) in group A and mostly >20 (80%) in group B ( P < 0.05). The average number of neurostimulation implantations per year was 15 (range 0–90; vagal nerve stimulation), 7 (range 0–25; responsive neurostimulation), and 4 (range 0–10; deep brain stimulation). In 78% of programs, fellows are not required to present a minimum number of epilepsy surgery cases in multidisciplinary conference before graduation. Roughly half of programs (51%) reported not using objective measures to assess fellow competency in epilepsy surgery. Conclusions: Our results suggest significant variability in epilepsy surgery volume and, consequently, fellow exposure to surgical cases and lack of standardized, objective measures in fellow teaching and assessment in the field of epilepsy surgery across programs in the United States. We advocate development of a core epilepsy surgery curriculum including minimum standards at the national level.