Noise in Otolaryngology – Head and Neck Surgery operating rooms: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Noise in operating rooms (OR) can have negative effects on both patients and surgical care workers. Noise can also impact surgical performance, team communication, and patient outcomes. Such implications of noise have been studied in orthopedics, neurosurgery, and urology. High noise levels have also been demonstrated in Otolaryngology-Head and Neck Surgery (OHNS) procedures. Despite this, no previous study has amalgamated the data on noise across all OHNS ORs to determine how much noise is present during OHNS surgeries. This study aims to review all the literature on noise associated with OHNS ORs and procedures. METHODS: Ovid Medline, EMBASE Classic, Pubmed, SCOPUS and Cochrane databases were searched following PRISMA guidelines. Data was collected on noise measurement location and surgery type. Descriptive results and statistical analysis were completed using Stata. RESULTS: This search identified 2914 articles. Final inclusion consisted of 22 studies. The majority of articles analyzed noise level exposures during mastoid surgery (18/22, 82%). The maximum noise level across all OHNS ORs and OHNS cadaver studies were 95.5 a-weighted decibels (dBA) and 106.6 c-weighted decibels (dBC), respectively (P = 0.2068). The mean noise level across all studies was significantly higher in OHNS cadaver labs (96.9 dBA) compared to OHNS ORs (70.1 dBA) (P = 0.0038). When analyzed together, the mean noise levels were 84.9 dBA. CONCLUSIONS: This systematic review demonstrates that noise exposure in OHNS surgery exceeds safety thresholds. Further research is needed to understand how noise may affect team communication, surgical performance and patient outcomes in OHNS ORs.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".