Does Surgical “Warming up” Improve Laparoscopic Simulator Performance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it