Does Surgical “Warming up” Improve Laparoscopic Simulator Performance?
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to determine if preoperative warming up by obstetrics and gynecology trainees, using a validated bench model for intracorporeal suturing, improves efficiency, precision, and quality of laparoscopic suturing. METHODS: A randomized crossover design was used. Fourteen obstetrics and gynecology residents were randomized [3 junior (year 2) and 11 senior (years 3-5) residents]. Participants were randomized to warm-up or no warm-up and then acted as their own controls at least 2 weeks later. Warm-up consisted of the use of a laparoscopic bench model to practice intracorporeal suturing for 15 minutes. All participants performed a prevalidated intracorporeal suturing task (after either warm-up or no warm-up), which was scored based on time, precision, and knot strength. Each participant also completed a questionnaire anonymously to determine if they believed that warming up improved their performance, regardless of the score they received. RESULTS: Thirteen participants completed the study. There was no difference in score when warm-up was compared with no warm-up for the group as a whole. When the junior residents were excluded from the analysis, however, analysis of variance showed a significant improvement in score only when a warm-up was completed in the second session (P = 0.022). The questionnaire revealed that 81.8% of participants felt that warming up subjectively improved their ability, independent of their actual score. CONCLUSIONS: This study demonstrates that a preoperative warm-up, combined with repetition, is beneficial in improving senior obstetrics and gynecology residents' laparoscopic suturing performance. This demonstrates a novel approach to resident education for teaching advanced laparoscopic skills.
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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) | 0.000 | 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 | 0.000 |
| 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".