Noise levels with the potential to impede communication during surgery
Bibliographic record
Abstract
Surgical teams consist of personnel who may or may not regularly work together, among whom communication can be more difficult because of surgical masks, face shields, or goggles, factors believed to have contributed to a recent serious medication error that occurred when the anaesthetist misunderstood the surgeon’s request. In a multihospital, occupational health intervention study a sound level meter was used to measure ambient noise for 15 min or > during 256 surgeries included in the study. Results revealed an Leq of 70.7 dB(A) and peak of 115.8 dB(A) (at the 95th percentile). Since the clarity of words depends on maintaining a signal-to-noise ratio of at least 15 dB(A), personnel working with such noise would likely increase their speaking levels up to 75–85 dB(A), a level substantially higher than normal speech ranging from 55–65 dB(A). Although the effect of noise levels on patient safety, or the health of OR personnel who must raise their voices to be heard, as well as on the overall climate that results when noise is elevated, have not been thoroughly investigated, evidence of negative effects is growing. And, while further characterization of OR noise is justified, this should be carried out to develop tailored interventions that can then be evaluated. [Work was funded by Ontario’s Workplace Safety Insurance Board.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".