Impact of background music on the performance of laparoscopy teams
Bibliographic record
Abstract
BACKGROUND: Acoustic conditions in the operating room have different impacts on surgeon's performance. Their effects on the performance of surgical teams are not well documented. We investigated if laparoscopic teams operating under pleasant acoustic conditions would perform better than under noisy conditions. METHODS: We recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under music, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions. RESULTS: A music environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p < 0.001) and collaborative suturing tasks (78.6 ± 5.4 vs. 67.2 ± 11.1; p < 0.001). Task times in the music and noisy environments was subtracted to produce a music-noisy difference time. Pearson correlation coefficient analysis showed a significant negative relationship between the team experience score and the music-noisy difference time on the object transportation (r = - 0.246, p = 0.046) and collaborative suturing tasks (r = - 0.248, p = 0.044). CONCLUSIONS: As to individuals, music enhances the performance of a laparoscopy team while a noisy environment worsens performance. The negative correlation between team experience and music-noisy difference time suggests that laparoscopy teams composed of experienced surgeons are less likely affected by an acoustic distraction than the noisy teams. Team resistance to acoustic distraction may lead to a new way for assessing team 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 | 0.005 |
| 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 | 0.001 | 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".