Ambient Noise Levels and Wireless Headsets for Communication in Aerosolizing Otolaryngology Surgery During COVID‐19
Bibliographic record
Abstract
The objective of this short scientific communication is to describe and test a strategy to overcome communication barriers in coronavirus disease 2019 (COVID‐19) era otolaryngology operating rooms. Thirteen endoscopic sinus surgeries, 4 skull base surgeries, and 1 tracheotomy were performed with powered air‐purifying respirators. During these surgeries, surgical team members donned headsets with microphones linked via conference call. Noise measurements and survey responses were obtained and compared to pre–COVID‐19 data. Noise was problematic and caused miscommunication as per 93% and 76% of respondents, respectively. Noise in COVID‐19 era operating rooms was significantly higher compared to pre–COVID‐19 era data (73.8 vs 70.2 decibels, P =. 04). Implementation of this headset strategy significantly improved communication. Respondents with headsets were less likely to encounter communication problems (31% vs 93%, P <. 001). Intraoperative measures to protect surgical team members during aerosolizing surgeries may impair communication. Linking team members via a conference call is a solution to improve communication.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".