Education Research: The current state of neurophysiology education in selected neurology residency programs
Bibliographic record
Abstract
OBJECTIVE: Prior research has illustrated there is a knowledge gap in neurology residents' neurophysiology education (EEG and EMG), and we sought to understand whether this is still an issue and to recognize the barriers in order to create solutions and improve education. METHODS: Surveys were developed for adult neurology residents and one for program directors asking about confidence in neurophysiology knowledge, percent of graduates reaching level 4 ACGME (American Council of Graduate Medical Education) milestones in EEG and EMG, methods of learning used, interest in the subjects, and suggestions for improvements. RESULTS: Twenty-six program directors (19% responder rate) and 55 residents (from at least 16 different programs) completed the survey. Program directors thought that 85% of graduating residents met level 4 milestones in EEG and only 75% in EMG. Structured rotations and more time allocated to education of these topics were frequent barriers mentioned. Postgraduate year 4 residents were 60% and 67% confident in EEG and 64%, 59%, and 62.3% in EMG level 4 milestones. Learning to read EEGs was considered important throughout residents' training; however, this interest and value decreased over time with EMG. CONCLUSION: In our study, program directors suspect up to a quarter of residents may graduate not meeting level 4 ACGME milestones, and residents expressed lack of confidence in these areas. The educational methods used to instruct residents in EEG and EMG were similar as were the barriers they face across programs. This information hopefully will help fuel curriculum design and interest in these important neurology techniques.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".