Predicting performance on the Royal College of Physicians and Surgeons of Canada internal medicine written examination.
Bibliographic record
Abstract
BACKGROUND: Although the written component of the Royal College of Physicians and Surgeons of Canada (RCPSC)internal medicine examination is important for obtaining licensure and certification as a specialist, no methods exist to predict a candidate's performance on the examination. METHOD: We obtained data from 5 Canadian universities from 1988 to 1998 in order to compare raw scores from the American Internal Medicine In-Training Examination (AIMI-TE) with raw scores and outcomes (pass or fail) of the written component of the RCPSC internal medicine examination. RESULTS: Mean scores on the AIMI-TE correlated well with scores on the RCPSC internal medicine written examination for all postgraduate years (r = 0.62, r = 0.55 and r = 0.65 for postgraduate years 1, 2 and 3 respectively). Scores above the 50th percentile on the AIMI-TE w/ere predictive of a low failure rate (< 1.5%) on the RCPSC internal medicine written examination, whereas scores at or below the 10th percentile were associated with a high failure rate (about 24%). INTERPRETATION: Candidates who are eligible to take the written component of the RCPSC certification examination in internal medicine can use the AIMI-TE to predict their performance on the Canadian examination. The AIMI-TE is a useful test for residents in all levels of training, because the examination scores have a strong relation to expected performance on the Canadian examination for each year of postgraduate training.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".