Quantitative assessment of a new preparatory tool for board certification in urology
Bibliographic record
Abstract
OBJECTIVE: To analyse the performance of candidates in a Canadian national mock-examination for final-year urology residents with respect to North American speciality examinations in urology. METHODS: In 1997 the Queen's Urology Examination Skills Training Program (QUEST) was established as an annual national mock examination for final-year Canadian urology residents. It consists of a short answer question component and an objective structured clinical examination. During the 5-year period (1997-2001), 91 final-year residents from all 11 Canadian urology residency-training programmes participated in QUEST and the Royal College of Physicians and Surgeons of Canada certifying examinations (RCPSCE); 43 (47%) of candidates also attempted the American Board of Urology part 1 qualifying examinations (ABU 1). Performance on QUEST was correlated with the RCPSCE and ABU 1 in a blinded fashion after submitting QUEST scores to governing bodies. Thresholds were determined to help to predict a candidate's performance on the RCPSCE and ABU 1, based on QUEST scores. RESULTS: There was a moderately close correlation between overall QUEST and RCPSCE performance (r = 0.68, P < 0.001) and a moderate correlation between overall QUEST and ABU 1 performance (r = 0.42, P = 0.005). For the following QUEST scores, the probability of success on the RCPSCE was: < 65%, 67% pass; 66-75%, 80% pass; > 75%, 100% pass (P = 0.002). For ABU 1, QUEST overall score of 80% gave a 69% probability of scoring > or = 70% on ABU 1 (P = 0.003). CONCLUSIONS: QUEST is a moderate predictor of performance on speciality examinations in urology. We consider that the time, effort and expense to maintain QUEST are justified.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".