Operating Room Noise and Team Communication during Facial Plastic and Reconstructive Surgery: A Multicenter Study
Bibliographic record
Abstract
Abstract Operating room (OR) noise contributes to team miscommunication. In facial plastic and reconstructive surgery (FPRS), many cases are completed under sedation. This creates a unique environment wherein patients are aware of OR noise. The objectives of this study were to quantify noise and evaluate team members' perspectives on communication inside of FPRS ORs. This study was completed across three surgical institutions. Objective noise measurements were recorded with SoundMeter X. A communication questionnaire was delivered to OR team members following each case. Four hundred and twenty-three noise measurements were recorded during facelift/neck, eye/brow, rhinoplasty, and fat transfer/lip surgeries. The mean and maximum noise levels were 66.1 dB (dB) and 87.6 dB, respectively. Measurements during cases with general anesthetic (221/423, 52.2%) had higher noise measurements (70.3 dB) compared with those with sedation (202/423, 47.8%) (69.7 dB) (p = 0.04). The OR was louder with suction on (72.3 dB) versus off (69.3 dB) (p <0.00). Suction (34.5%) and music (22.4%) were the largest noise contributors according to questionnaire replies. Intraoperative noise, awake patients, and suctions/music may negatively impact FPRS OR communication. Innovation to improve FPRS intraoperative communication should be considered for effective patient care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".