Noise in Endoscopic Sinus and Skull Base Surgery Operating Rooms
Bibliographic record
Abstract
BACKGROUND: Noise in the operating room (OR) contributes to miscommunication among team members and may negatively impact patient outcomes. OBJECTIVES: This study aimed to quantify noise levels during endoscopic sinus and skull base surgery. The secondary aim was to understand how OR team members perceive noise during endoscopic sinus and skull base surgery. METHODS: (Faber Acoustical, Utah, USA) at the ear-level of the surgeon, scrub nurse, circulating nurse, and anesthesiologist. At the end of each surgery, OR team members were asked to complete a six-question questionnaire about noise during that surgery. RESULTS: One thousand four hundred and two noise measurements were recorded across 353 trials. The loudest mean noise measurement was 84.51 dB and maximum noise measurement was 96.21 dB at the ear-level of the surgeon. Noise was significantly higher at the ear-level of the surgeon and scrub nurse in comparison to the circulating nurse (p = .000) and anesthesiologist (p = .000). Forty percent of questionnaire respondents believed noise was a problem and 38% stated that noise caused communication issues during surgery. CONCLUSION: Surgeons and scrub nurses have significantly higher noise exposure in comparison to circulating nurses and anesthesiologists during endoscopic sinus and skull base surgery. For these members of the OR team, noise is also identified as problematic and causing issues with communication. Mechanisms to reduce potential noise may be implemented to improve communication and patient outcomes in endoscopic sinus and skull base surgery.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".