Doctor scores on national qualifying examinations predict quality of care in future practice
Bibliographic record
Abstract
OBJECTIVES: This study aimed to determine if national licensing examinations that measure medical knowledge (QE1) and clinical skills (QE2) predict the quality of care delivered by doctors in future practice. METHODS: Cohorts of doctors who took the Medical Council of Canada Qualifying Examinations Part I (QE1) and Part II (QE2) between 1993 and 1996 and subsequently entered practice in Ontario, Canada (n = 2420) were followed for their first 7-10 years in practice. The 208 of these doctors who were randomly selected for peer assessment of quality of care were studied. Main outcome measures included quality of care (acceptable/unacceptable) as assessed by doctor peer-examiners using a structured chart review and interview. Multivariate logistic regression was used to determine if qualifying examination scores predicted the outcome of the peer assessments while controlling for age, sex, training and specialty, and if the addition of the QE2 scores provided additional prediction of quality of care. RESULTS: Fifteen (7.2%) of the 208 doctors assessed were considered to provide unacceptable quality of care. Doctors in the bottom quartile of QE1 scores had a greater than three-fold increase in the risk of an unacceptable quality-of-care assessment outcome (odds ratio [OR] 3.41, 95% confidence interval [CI] 1.14-10.22). Doctors in the bottom quartile of QE2 scores were also at higher risk of being assessed as providing unacceptable quality of care (OR 4.24, 95% CI 1.32-13.61). However, QE2 results provided no significant improvement in predicting peer assessment results over QE1 results (likelihood ratio test: chi(2) = 3.21, P-value((1 d.f.)) = 0.07). CONCLUSIONS: Doctor scores on qualifying examinations are significant predictors of quality-of-care problems based on regulatory, practice-based peer assessment.
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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".