Procedure-Specific Simulation for Vaginal Surgery Training: A Randomised Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To investigate whether procedure-specific skills acquired in a surgical lab, versus usual training, result in improved operative competence. DESIGN: randomised controlled trial. Didactic and procedural training occurred using three low fidelity vaginal surgery models: anterior repair (AR), posterior repair (PR), vaginal hysterectomy (VH). POPULATION/SETTING: Junior gynaecology residents at three academic centres. METHODS: The primary outcome was performance evaluated by attending staff blinded to group, via global rating scale (GRS) in the real operating room and for corresponding procedures. Prespecified secondary outcomes included procedural steps knowledge, overall performance impression, resident 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). RESULTS: 83 residents were randomised to intervention or control and 55 completed the trial (2012-22). All characteristics were similar between groups. Adjusted GRS scores (by age, level and baseline knowledge) showed a significant group difference overall (mean difference 8.2; 95% CI 0.2,16.1; p=0.044) and for VH (mean difference 12.0; 95% CI 1.8, 22.3; p=0.02), but not for AR or PR. The intervention group also had significantly higher procedural steps knowledge, satisfaction and self-confidence for VH and PR (p<0.05 for all). Estimated blood loss, operative time and complications were similar between groups. CONCLUSIONS: Compared to usual training, surgical education modules using procedure-specific low fidelity models for vaginal surgery resulted in significant improvements in actual operative performance and several other skill parameters. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, [www.clinicaltrials.gov](http://www.clinicaltrials.gov), NCT05887570
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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 | 0.011 |
| 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 | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".