Does Pass/Fail on Medical Licensing Exams Predict Future Physician Performance in Practice? A Longitudinal Cohort Study of Alberta Physicians
Bibliographic record
Abstract
The purpose of this longitudinal study was to gather extrapolation evidence of validity by assessing whether performance on a national medical licensing exam, in addition to practice and socio-demographic variables, is predictive of future physician performance in practice. The study focused on a cohort of 3,404 physicians who were registered with the College of Physicians and Surgeons of Alberta (CPSA) and who completed the Medical Council of Canada Qualifying Examination (MCCQE) Parts I and II between 1992–2017. Separate multivariate quasi-Poisson regression models were run to assess the degree of relationship between first-time pass/fail status on the MCCQE I and II, and several CPSA socio-demographic variables and several CPSA socio-demographic variables, in addition to complaints/physician and various prescribing flags. Candidates who failed the MCCQE I on their first attempt had 27% more complaints lodged against them, compared to those who passed. Physicians who failed the MCCQE II on their first attempt prescribed 2+ benzodiazepines and 2+ opioids to 30% more patients than those who passed. Conclusions: Performance on the MCCQE Part I and II is an important predictor of physician performance. Combined with other critical variables, these measures provide important evidence to aid in risk modeling efforts and to guide educational interventions for physicians at an early stage of their careers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".