A NOVEL APPROACH TO ENDOUROLOGICAL TRAINING: TRAINING AT THE SURGICAL SKILLS CENTER
Bibliographic record
Abstract
PURPOSE: We investigated the effects of didactic teaching and supervised hands-on practice on endourological skills using high fidelity genitourinary bench models at a surgical skills laboratory. We also validated a global rating scale and checklist designed specifically for endourological tasks. MATERIALS AND METHODS: We assessed 17 urology residents for the ability to remove a mid ureteral stone using a high fidelity genitourinary model on 3 occasions, including a pre-test at the beginning of the study to assess baseline skills, after a didactic teaching session and after a supervised practice session on high fidelity models. Performance was graded according to a global rating scale, checklist, pass rating and time needed to complete task. RESULTS: Senior residents achieved significantly higher pre-test global rating scores than junior residents (p <0.01). One-way repeated measures analysis of variance revealed a significant effect of training on the endoscopic global rating score (p <0.001). Post-hoc tests demonstrated significant improvement in the global rating scores from the pre-test to the post-didactic session (p <0.05) and from the post-didactic to the post-practice session (p <0.01). Interrater reliability using the global rating scale was high (Pearson's r = 0.82, p <0.01). Significant but less powerful results were observed in the checklist score, pass rating and time. CONCLUSIONS: There was a positive effect of training at the surgical skills laboratory on endourological skills. The global rating scale showed good construct validity and reliability for assessing endourological tasks, more so than the checklist, pass rating or time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".