Objective Assessment and Standard Setting for Basic Flexible Ureterorenoscopy Skills Among Urology Trainees Using Simulation-Based Methods
Bibliographic record
Abstract
Objective: To objectively assess the performance of graduating urology residents performing flexible ureterorenoscopy (fURS) using a simulation-based model and to set an entrustability standard or benchmark for use across the educational spectrum. Methods: Chief urology residents and attending endourologists performed a standardized fURS task (ureterorenoscopy and repositioning of stones) using a Boston Scientific © Lithovue ureteroscope on a Cook Medical © URS model. All performances were video-recorded and blindly scored by both endourology experts and crowd-workers (C-SATS) using the Ureteroscopic Global Rating Scale, plus an overall entrustability score. Validity evidence supporting the scores was collected and categorized. The Borderline Group (BG) method was used to set absolute performance standards for the expert and crowdsourced ratings. Results: A total of 44 participants (40 chief residents, 4 faculties) completed testing. Eighty-three percent of participants had performed >50 fURS cases at the time of the study. Only 47.7% (mean score 12.6/20) and 61.4% (mean score 12.4/20) of participants were deemed “entrustable” by experts and crowd-workers, respectively. The BG method produced entrustability benchmarks of 11.8/20 for experts and 11.4/20 for crowd-worker ratings, resulting in pass rates of 56.9% and 61.4%. Conclusion: Using absolute standard setting methods, benchmark scores were set to identify trainees who could safely carry out fURS in the simulated setting. Only 60% of residents in our cohort were rated as entrustable. These findings support the use of benchmarks to earlier identify trainees requiring remediation.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".