Bibliographic record
Abstract
Noise levels in hospital operating rooms have not been well characterized. Therefore, noise levels were measured using a sound level meter in a sample of surgeries included in a multihospital intervention study, assessing the effectiveness of a recommended work practice to decrease occupational exposure to blood during surgery. The duration of the measurements ranged from 15 min to several hours. Among types of surgery for which at least four measurements were done, the Leq for orthopedic surgery was the highest at 70.1 dB(A) (range 60.8–75.1), followed by 63.7 dB(A) for neurosurgery (range 57.4–68.1), and 62.8 dB(A) (range 58.5–70.3), for general surgery. Gynecological surgery had the lowest Leq, 60.8 dB(A) (range 56.5–62.5). Peak levels were found to be as high as 132.8 and 132.6 dB(A), in general and orthopedic surgery, respectively, and lowest in neurosurgery at 102.6 dB(A). These noise levels are consistent with those from a comprehensive U.S. study, and substantially exceed EPAs recommended level of 45 dB(A) for hospitals. [Work funded by Ontarios 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".