D.07 Clinical electromyography training in Canada: The experience of neurology and physiatry residents
Bibliographic record
Abstract
Background: There are currently no national standards for clinical electromyography (EMG) training for residents in neurology and physiatry in Canada. The purpose of this study was to obtain demographic and qualitative data pertaining to EMG residency training in Canada, with the goal of facilitating discourse that could lead to national standards for EMG training. Methods: An online survey was distributed to senior neurology and physiatry residents (post-graduate years 3-5), at seven tertiary Canadian centres. The study authors, who are trainees and consultants with a broad range of EMG expertise (junior and senior resident, clinical neuromuscular fellows, senior physiatrist and neuromuscular neurologists), developed pertinent demographic and qualitative questions. Results: Thirty-eight residents completed the survey (23 neurology, 15 physiatry). There was inter-program variation in quantity of the training experience, content of the curriculum, access to expertise (including technologists) and goals for future training and practice. Similarly, differences were identified between the training experiences of neurology and physiatry residents. Conclusions: Inter-program variability in EMG training was identified. Additionally, differences were identified between neurology and physiatry resident training. This data provides evidence of training discrepancies across the country and can be used to establish national training standards for EMG in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".