Warm-up in a Virtual Reality Environment Improves Performance in the Operating Room
Bibliographic record
Abstract
In Brief Objective: To assess the impact of warm-up on laparoscopic performance in the operating room (OR). Background: Implementation of simulation-based training into clinical practice remains limited despite evidence to show that the improvement in skills is transferred to the OR. The aim of this study was to evaluate the impact of a short virtual reality warm-up training program on laparoscopic performance in the OP. Methods: Sixteen Laparoscopic Cholecystectomies were performed by 8 surgeons in the OR. Participants were randomized to a group which received a preprocedure warm-up using a virtual reality simulator and no warm-up group. After the initial laparoscopic cholecystectomy all surgeons served as their own controls by performing another procedure with or without preoperative warm-up. All OR procedures were videotaped and assessed by 2 independent observers using the generic OSATS global rating scale (from 7 to 35). Results: There was significantly better surgical performance on the laparoscopic Cholecystectomy following preoperative warm-up, median 28.5 (range = 18.5–32.0) versus median 19.25 (range = 15–31.5), P = 0.042. The results demonstrated excellent reliability of the assessment tool used (Cronbach's α = 0.92). Conclusion: This study showed a significant beneficial impact of warm-up on laparoscopic performance in the OP. The suggested program is short, easy to perform, and therefore realistic to implement in the daily life in a busy surgical department. This will potentially improve the procedural outcome and contribute to improved patient safety and better utilization of OR resources. This study evaluates the impact of warm-up in a virtual reality environment on performance in the operating room. Results suggest that a short period of warm-up (15 minutes) does improve the quality of technical performance. Further studies should replicate this finding for other procedures and determine the effects of warm-up on nontechnical performance and patient outcomes.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".