Neurology for internal medicine residents: Working towards a national Canadian curriculum consensus
Bibliographic record
Abstract
BACKGROUND: Partly due to the absence of a standardized neurology curriculum, internal medicine residents often perceive neurology lowest in terms of the level of knowledge and clinical confidence. AIMS: To compare the learning needs of internal medicine residents with the perceived learning needs of neurology and internal medicine program directors and to integrate these needs by developing a focused nationwide neurology curriculum for internal medicine residents rotating through neurology. METHODS: Medical residents and neurology and internal medicine program directors from programs across the Canada were asked to complete an online survey and to rank an exhaustive list of neurology topics. A modified Delphi approach was used to obtain consensus on the top 20 topics to include in the curriculum. RESULTS: Over 80% of residents felt their competency in neurology was average or below after completing their neurology rotation. There was very high correlation between the topics ranked by residents and staff. We were able to achieve consensus on 20 topics to be included in a neurology curriculum for internal medicine residents. CONCLUSION: Through a modified Delphi approach we were able to produce a neurology curriculum for internal medicine residents rotating through neurology based on the input of program directors across the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".