Efficacy of aerosol reduction measures for dental aerosol generating procedures
Bibliographic record
Abstract
Aerosol particles generated by dental procedures could facilitate the transmission of infectious diseases and contain carcinogen particles. Such particles can penetrate common surgical masks and reach the lungs, leading to increased risk for dental care professionals. However, the risk of inhaling contaminated aerosol and the effectiveness of aerosol reduction measures in dental offices remain unclear. The present study aimed to quantify aerosols produced by drilling and scaling procedures and to evaluate present recommendations for aerosol reduction. The concentration of aerosol particles released from the mock scaling and drilling procedures on dental mannequin were measured using a TSI Optical Particle Sizer (OPS 3330) during 15-min sessions carried out in a single-patient examination room. Using a drilling procedure as the aerosol source, the aerosol reduction performance of two types of high-volume evacuators (HVEs) and a commercial off-the-shelf air purifier was evaluated in a simulated clinical setting. Using either HVEs or the air purifier individually reduced the aerosol accumulated over the course of a 15-minutes drilling procedure at a reduction rate of 94.8 to 97.6%. Using both measures simultaneously raised the reduction rate to 99.6%. The results show that existing HVEs can effectively reduce aerosol concentration generated by a drilling procedure and can be further improved by using an air purifier. Following current regulatory guidelines can ensure a low risk of inhaling contaminated aerosol for dentists, assistants, and patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".