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Record W4288513815 · doi:10.1684/epd.2022.1476

Competency‐based EEG education: a list of “must‐know” EEG findings for adult and child neurology residents

2022· article· en· W4288513815 on OpenAlex
Fábio A. Nascimento, Jin Jing, Roy E. Strowd, Irfan Sheikh, Dan Weber, Jay R. Gavvala, Atul Maheshwari, Adriana Tanner, Marcus Ng, Kollencehri Puthenveetti Vinayan, Saurabh R. Sinha, Elza Márcia Targas Yacubian, Vikram Rao, Μ. Scott Perry, Nathan B. Fountain, Ioannis Karakis, Elaine Wirrell, Fang Yuan, Daniel J. Friedman, Hatice Tankişi, Stefan Rampp, Rebecca Fasano, Jo M. Wilmshurst, Cormac A. O’Donovan, Donald L. Schomer, Peter W. Kaplan, Michael R. Sperling, Selim R. Benbadis, M. Brandon Westover, Sándor Beniczky

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEpileptic Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood Institute
KeywordsElectroencephalographyNeurologyPsychologyAudiologyMedical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.257
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it