Assessment of urology postgraduate trainees’ competencies in flexible ureteroscopic stone extraction
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess flexible ureteroscopic stone extraction skill of urology postgraduate trainees (PGTs) at an Objective Structured Clinical Examination (OSCE) and to determine whether previous experience in the operating theatre or practice on the simulator correlated with performance. METHODS: After obtaining ethics approval, PGTs from postgraduate years (PGYs) 3-5 were recruited from all four Quebec urology training programs during an OSCE. After a short orientation to the UroMentor™ simulator, PGTs were asked to perform Task 10 for 15 minutes, where two small stones from the left proximal ureter and renal pelvis were extracted using a basket. Competency of PGTs in performing the task was assessed using objective assessment from the simulator and subjective evaluations using Ureteroscopy-Global Rating Scale (URS-GRS). Simulator performance reports and URS-GRS scores were analyzed. RESULTS: Thirty PGTs (9 PGY-3, 11 PGY-4, 10 PGY-5) participated in this study. PGTs had performed a mean of 55.9 semi-rigid and 45.7 flexible ureteroscopies prior to the study. Mean URS-GRS score of the participants was 20.0±4.4. Using norm-referenced method with three experts, cutoff score of 19 on the URS-GRS was determined to indicate competency. Sixty percent (18/30) of PGTs were competent. All eight PGTs who had practiced on the simulator were competent. Previous experience in the operating theatre and PGY level did not correlate with performance. CONCLUSIONS: This study confirmed the feasibility of incorporating the UroMentor at OSCEs to assess competency of urology PGTs in ureteroscopic stone extraction skill. PGTs who practiced on the simulator scored significantly higher than those who did not practice; however, the software needs to be updated to improve its face validity and to include more complex tasks, such as holmium laser lithotripsy. Future studies with larger sample sizes and more complex cases are needed to confirm these results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".