Preoperative Practice Paired With Instructor Feedback May Not Improve Obstetrics-Gynecology Residents' Operative Performance
Bibliographic record
Abstract
BACKGROUND: There is evidence that preoperative practice prior to surgery can improve trainee performance, but the optimal approach has not been studied. OBJECTIVE: We sought to determine if preoperative practice by surgical trainees paired with instructor feedback improved surgical technique, compared to preoperative practice or feedback alone. METHODS: We conducted a randomized controlled trial of obstetrics-gynecology trainees, stratified on a simulator-assessed surgical skill. Participants were randomized to preoperative practice on a simulator with instructor feedback (PPF), preoperative practice alone (PP), or feedback alone (F). Trainees then completed a laparoscopic salpingectomy, and the operative performance was evaluated using an assessment tool. RESULTS: A total of 18 residents were randomized and completed the study, 6 in each arm. The mean baseline score on the simulator was comparable in each group (67% for PPF, 68% for PP, and 70% for F). While the median score on the assessment tool for laparoscopic salpingectomy in the PPF group was the highest, there was no statistically significant difference in assessment scores for the PPF group (32.75; range, 15-36) compared to the PP group (14.5; range, 10-34) and the F group (21.25; range, 10.5-32). The interrater correlation between the video reviewers was 0.87 (95% confidence interval 0.70-0.95) using the intraclass correlation coefficient. CONCLUSIONS: This study suggests that a surgical preoperative practice with instructor feedback may not improve operative technique compared to either preoperative practice or feedback alone.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".