Feasibility and utilization of a national virtual <scp>EEG</scp> course for Canadian residents and fellows
Bibliographic record
Abstract
OBJECTIVE: Electroencephalography (EEG) is an essential tool for the diagnosis and management of epilepsy. There is a gap in EEG education for residents in Canadian neurology programs as EEG is only listed in the training requirements as a procedural skill. There is currently no standardized EEG curriculum among Canadian epilepsy fellowship programs. METHODS: We conducted two iterations of a structured virtual EEG course from June to October 2021, and from March to June 2022. Trainees were recruited via Canadian neurology residency and epilepsy fellowship programs and were required to join the Canadian League Against Epilepsy (CLAE) as junior members. We obtained trainee demographic information before and after each course as well as analytical data on the video recordings posted on the CLAE website. RESULTS: A total of 77 trainees registered for the two courses; majority of trainees were adult neurology residents (34%) and adult epilepsy fellows (32%). Prior theoretical EEG teaching was reported as limited by more than half (53%) of participants. The average number of unique viewers per recorded video in 2021 was 29.7 interquartile range (16-35.5), while in 2022, the average was 22.5, interquartile range (16-28). Post-course questionnaire data revealed that 82% of participants strongly agreed that the course enhanced their knowledge. All participants were either likely (27%) or very likely (73%) to recommend the course to their peers. SIGNIFICANCE: National virtual EEG education is both feasible and accessible; therefore, this is a promising modality of teaching to meet the significant demand for high-quality EEG education among neurology trainees.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".