OSCE performance among Quebec urology residents: A retrospective study from 2008–2019
Bibliographic record
Abstract
INTRODUCTION: We aimed to compare objective structured clinical examinations (OSCE) performance of residents from four Canadian urology programs, based on resident and station characteristics. We also aimed to evaluate OSCE contents by station type and subspecialty. METHODS: Scores of 109 postgraduate year (PGY)-3 to PGY-5 residents were retrospectively reviewed from 19 OSCEs from May 2008 to February 2019. Scores were grouped by station type/subspecialty, PGY level, medical graduate type (Canadian medical graduate [CMG], international medical graduate [IMG]), sex, and choice of fellowship/practice. Linear mixed modelling was performed to obtain least square means to account for repeated measures. RESULTS: Score increases from PGY-3 to PGY-5 were significant for all station types and subspecialties (p≤0.001). Scores were similar between male and female residents, and between CMGs and IMGs, except in visual recognition examinations (VREs) (males: 44.3±1.0, females: 39.0±1.6, p=0.005; IMG: 47.3±1.7, CMG: 41.6±0.9, p=0.004). Relative to uro-oncology stations, scores were lower in andrology (p=0.010) and functional urology (p<0.001). More female residents chose pediatric (14.3% vs. 1.5%, p=0.024) and functional urology fellowships (17.9% vs. 2.9%, p=0.021). More male residents chose endourology/robotic fellowships (30.9% vs. 10.7%, p=0.042). No associations between subspecialty scores and choice of fellowship/practice were found. Oral stations and VREs were more frequent than telephone stations. Uro-oncology and pediatric urology were more frequent than other subspecialties. CONCLUSIONS: Scores improved with higher PGY level. IMGs and male residents scored better in VREs. Scores were lower in functional urology. There was no correlation between subspecialty score and choice of fellowship/practice. Subspecialties and forms of evaluation were not equally represented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".