‘The air that we breathe’: Assessment of laser and electrosurgical dissection devices on operating theater air quality
Bibliographic record
Abstract
Objectives To measure changes in air quality during surgery. Methods Operating room (OR) and hallway air quality was continuously monitored over a 3-month period. Rooftop monitoring was used to control for environmental changes and to account for the infiltration of outdoor air pollutants. Air quality measurements were correlated with operative times and electro-dissection equipment used. Results OR air is cooler and drier compared to the adjacent hallway. Volatile organic compounds and other gases are below indoor air exposure limit guidelines. Lasers create greater 2.5 μm particulate matter (PM 2.5 ) mass concentration, and greater fine and coarse particle number than cautery or cold tissue dissection. Cautery produces more ultrafine particles (UFP) than other dissection techniques. OR air has lower particle counts than outdoor environmental air by virtue of air conditioning HEPA filtration. Conclusion Compared to the outside air, operating room air has lower particle counts. Lasers produce higher concentrations of PM 2.5 mass and, fine and coarse particle number counts. Cautery produces higher concentrations of UFP number counts than other modalities and warrants consideration of the use of masks with ultrafine particle filtration capacity. Operating room air is consistently cooler with decreased humidity, which may cause airway irritation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".