Geschlechtsspezifische Unterschiede im chirurgischen Selbstvertrauen: Ergebnisse des Endo-Workshops der German Society of Residents in Urology 2022
Bibliographic record
Abstract
BACKGROUND: Simulation-based training is gaining importance in urologic residents training. OBJECTIVES: This prospective study evaluated the influence of the Endo Workshop of the German Society of Residents in Urology e. V. (GeSRU) on surgical confidence. MATERIALS AND METHODS: GeSRU Endo Workshop 2022 included 1 h simulation-based training sessions on stone removal using ureteroscopy (URS) and transurethral resection of the bladder (TURB). Using an online questionnaire, surgical confidence was assessed before and after the workshop. Surgical assessment relied on the global rating scale (GRS). RESULTS: Overall, 40 residents participated: 25 (62.5%) men and 15 (37.5%) women. In URS assessment, men vs. women achieved an average of 26.6 vs. 26.1/35 points on the GRS (p = 0.7) and completed the task in 8.1 ± 1.9 vs. 9.9 ± 0.4 min (p < 0.001). In TURB assessment, men vs. women achieved an average of 26.0 vs. 27.3/35 points on the GRS (p = 0.3) and required 7.6 ± 1.9 vs. 7.7 ± 2.2 min (p = 0.9), respectively. Among participants who answered the baseline survey and the evaluation (n = 33), 16 (80%) men vs. 3 (23%) women had surgical confidence to perform URS before (p = 0.01), and 19 (95%) men vs. 7 (54%) women after the workshop (p = 0.03). Regarding the performance of TURB, 10 (50%) men vs. 7 (54%) women reported surgical confidence before (p = 0.1), and 15 (75%) men vs. 10 (77%) women after the workshop (p = 1.0). An increase in surgical confidence to perform URS and TURB was reported by 9 (45%) and 10 (50%) men and 9 (69%) and 8 (62%) women, respectively. CONCLUSIONS: Endourologic simulation-based training increases surgical confidence of both female and male residents. Despite comparable surgical outcomes, women approach URS with lower surgical confidence compared to their male counterparts.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".