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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".