Simulation-based flexible ureteroscopy training using a novel ureteroscopy part-task trainer
Bibliographic record
Abstract
INTRODUCTION: Simulation-based training (SBT) is being increasingly used for novice trainees as a means of overcoming the early learning curve associated with new surgical skills. We designed a SBT flexible ureteroscopy (fURS) course using a novel inanimate training model (Cook Medical, Bloomington, IN; URS model). We evaluated the course and validated this Cook URS model. METHODS: A 2-week SBT fURS course was designed for junior level urology trainees at 2 Canadian universities. The curriculum included didactic lectures, hands-on training, independent training sessions with expert feedback, and use of the Cook URS part-task model. Baseline and post-course assessments of trainee fURS skills were conducted using a standardized test task (fURS with basket manipulation of a calyceal stone). Performances were video-recorded and reviewed by 2 blinded experts using a validated assessment device. RESULTS: Fifteen residents (postgraduate years [PGY] 0-3) participated in the course. Of the participants, 80% rated the Cook URS model as realistic (mean = 4.2/5) and 5 endourology experts rated it as useful as a training device (mean = 4.9/5), providing both face and content validity. The mean overall performance scores, task completion times, and passing ratings correlated with trainee clinical fURS experience - demonstrating construct validity for the Cook URS model. The mean post-course task completion times (15.76 vs. 9.37 minutes, p = 0.001) and overall performance scores (19.20 vs. 25.25, p = 0.007) were significantly better than at baseline. Post-course performance was better in all domains assessed by the validated assessment device. CONCLUSIONS: This study demonstrates that a SBT curriculum for fURS can lead to improved short-term technical skills among junior level urology residents. The Cook URS model demonstrated good face, content and construct validity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".