Procedure‐specific simulation for vaginal surgery training: A randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Vaginal surgery has a superior outcome profile compared with other surgical routes, yet skills are declining because of low case volumes. Graduating residents' confidence and preparedness for vaginal surgery has plummeted in the past decade. The objective of the present study was to investigate whether procedure-specific simulation skills, vs usual training, result in improved operative competence. MATERIAL AND METHODS: We completed a randomized controlled trial of didactic and procedural training via low fidelity vaginal surgery models for anterior repair, posterior repair (PR), vaginal hysterectomy (VH), recruiting novice gynecology residents at three academic centers. We evaluated performance via global rating scale (GRS) in the real operating room and for corresponding procedures by attending surgeon blinded to group. Prespecified secondary outcomes included procedural steps knowledge, overall performance, satisfaction, self-confidence and intraoperative parameters. A priori sample size estimated 50 residents (20% absolute difference in GRS score, 25% SD, 80% power, alpha 0.05). CLINICALTRIALS: gov: Registration no. NCT05887570. RESULTS: We randomized 83 residents to intervention or control and 55 completed the trial (2011-23). Baseline characteristics were similar, except for more fourth-year control residents. After adjustment of confounders (age, level, baseline knowledge), GRS scores showed significant differences overall (mean difference 8.2; 95% confidence interval [CI]: 0.2-16.1; p = 0.044) and for VH (mean difference 12.0; 95% CI: 1.8-22.3; p = 0.02). The intervention group had significantly higher procedural steps knowledge and self-confidence for VH and/or PR (p < 0.05, adjusted analysis). Estimated blood loss, operative time and complications were similar between groups. CONCLUSIONS: Compared to usual training, procedure-specific didactic and low fidelity simulation modules for vaginal surgery resulted in significant improvements in operative performance and several other skill parameters.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".