Competency‐based EEG education: a list of “must‐know” EEG findings for adult and child neurology residents
Bibliographic record
Abstract
The competency-based model has been guiding medical education on an international level over the last decades [1].This model is learner-centered and has mastery of specific knowledge and skills as its unit of progression [2].In the realm of electroencephalography (EEG), there have been continued efforts to ensure that residents have the competence to accurately and reliably interpret EEGs by the time they complete residency training.Achieving this goal is imperative, especially in countries where EEGs are typically read by neurologists without clinical neurophysiology or epilepsy fellowship training [3,4], due to the deleterious consequences of EEG misinterpretation and epilepsy misdiagnosis [3].In an attempt to define minimum EEG competency milestones, we herein propose a prioritized list of routine EEG findings that all adult and child neurology residents should be able to identify and interpret on completion of training.Resident EEG education is guided by well-formulated milestones proposed by organizations such as the Accreditation Council for Graduate Medical Education (ACGME) [5] and International League Against Epilepsy (ILAE) [6].These milestones, however, are not meant to be used to determine whether a trainee is competent to graduate; additionally, the milestones do not specify particular EEG findings that should be mastered by trainees.For example, the ACGME EEG Level 3 milestone encapsulates recognition of "common EEG abnormalities"; these "abnormalities", nonetheless, are not specified.We surveyed a group of EEG/epilepsy experts to delineate a list of routine EEG findings rated by their clinical yield for adult and child neurology resident education.The authors (FN, JJ, MBW, SB) designed an online survey (see supplementary material) in which a comprehensive set of adult and pediatric routine EEG findings were listed under four major sections: normal findings, artifacts, normal variants, and abnormal findings.Neonatal EEG findings were not included.EEG/epilepsy experts were asked to rate each EEG finding on a 5-point Likert rating scale (1 = "not
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".