Can we predict failure in licensure exams from medical students’ undergraduate academic performance?
Bibliographic record
Abstract
BACKGROUND: In 2015, the Medical Council of Canada increased the minimum pass level for the Medical Council of Canada Qualifying Examination Part I, and students had a higher rate of failure than in previous years. The purpose of this study was to predict students at an increased odds of examination failure to allow for early, targeted interventions. METHODS: We divided our dataset into a derivation cohort and two validation cohorts and used multiple logistic regression to predict licensing examination failure. We then performed receiver operating characteristics and a sensitivity analysis using different cutoffs for explanatory variables to identify the cutoff threshold with the best predictive value at identifying students at increased odds of failure. RESULTS: < 0.001). The probability of failure increased steeply when the pre-clerkship GPA fell below 80% and 76% was found to be the most efficient cutoff for predicting failure (OR 9.37, 95% CI [3.08, 38.41]). CONCLUSIONS: Pre-clerkship performance can predict students at increased odds of licensing examination failure. Further studies are needed to explore whether early interventions for at-risk students alter their examination performance.
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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 | 0.001 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.221 | 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".