Risk for Surgical Team Hearing Loss With Vitrectomy
Bibliographic record
Abstract
Purpose: To assess sound-level exposure during vitrectomy using 3 of the most common commercially available machines. Methods: This noninterventional cross-sectional study examined sound emission from the Constellation, Stellaris, and EVA vitrector systems. For each machine, a noise dosimeter was used to measure the sound-level exposure of the surgeon during 3 surgical cases in which vitrectomy was performed. Sound levels associated with progressively increasing cut rates and vacuum pressures were also measured. Finally, sound measurements were taken during the use of various additional functions of each machine, including diathermy, laser, and extrusion. Sound levels were compared with occupational health guidelines in Canada and the United States. Results: The maximum sound level recorded during vitrectomy surgery was 88.2 dBA. The mean sound level during vitrectomy surgical cases ranged from 58.5 to 66.8 dBA. A strong positive linear correlation was found between the cut rate and sound level ( r = 0.88-0.98) and the vacuum pressure and sound level ( r = 0.83-0.97). This relationship was consistent across the 3 vitrector systems ( P < .001). Conclusions: Noise exposure during vitrectomy procedures was acceptable but may be sufficient for surgical team activity interference, as described by World Health Organization recommendations. A strong correlation was found between the cut rate and noise exposure. If cut rates continue to increase, attention should be given to ensure that the resulting noise exposure does not threaten the hearing of vitreoretinal surgeons and the operating room staff.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".