Bibliographic record
Abstract
Introduction: Even the most advanced surgical simulator should be integrated into a comprehensive curriculum in order to maximize educational value.We designed a flexible ureteroscopy (fURS) simulation-based training course (SBTC) utilizing a novel inanimate training model (Cook® URS model) and present validity evidence for the Cook® URS model.Methods: A comprehensive SBTC for fURS was designed for Jr-level (PGY1-3) residents at our institution.Baseline pre-and post-course assessments of fURS skill were conducted for a standardized task; fURS with basket manipulation of a lower pole stone into the upper pole.The pre-and post-test sessions were separated by a minimum of 2 weeks.Performances were video-recorded and reviewed by 2 blinded experts using a validated assessment device.Results: A total of 10 residents participated in the fURS course.There was a significant improvement in both mean post-course task completion time (9.37 vs. 15.76 mins, p=0.001) and performance score (25.25 vs. 19.20,p=0.007).Eighty percent of participants rated the Cook® URS model as realistic (≥4/5, mean=4.20)and 5 independent expert endourologists rated
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.738 | 0.347 |
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".