The Effect of the Double Mask on Anesthetic Waste Gas Levels During Pediatric Mask Inductions in Dental Offices
Bibliographic record
Abstract
A significant portion of office-based general anesthesia for pediatric patients is performed in dental offices and involves mask inductions with inhaled drugs. This can lead to significant pollution with waste gases. We assessed occupational exposure to anesthetic drugs during pediatric general anesthesia in dental offices and assessed the effectiveness of the "double mask." Nine freestanding dental offices had measurements of anesthetic waste gas levels taken before and immediately after implementation of a double-mask system. Levels of nitrous oxide decreased from a median of 40.0 parts per million (ppm; interquartile range [IQR] = 23.0-46.0 ppm, n = 9) to 3.0 ppm, (IQR = 2.3-4.7 ppm, n = 9, P = 0.0055) and exceeded 25 ppm in 0% of the 9 offices (upper 95% confidence limit 34%) when using the double mask. Levels of sevoflurane decreased from a median of 4.60 ppm (IQR = 3.10-7.00 ppm, n = 9) to 0 ppm (IQR = 0-0.39 ppm, n = 9, P = 0.0024) and exceeded 2 ppm in 0% of the 9 offices (upper 95% confidence limit 34%) when using the double mask. We demonstrated in our study that the double-mask system, when used with dental "high-volumes" suctions (high-volume evacuators producing approximately 12 m(3)/h) in freestanding dental offices, was sufficient to decrease the exposure to anesthetic waste gas during pediatric mask induction in at least two thirds of offices when compared with the traditional mask.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".