Does Pass/Fail on Medical Licensing Exams Predict Future Physician Performance in Practice? A Longitudinal Cohort Study of Alberta Physicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it