In Situ Measurement of Airborne Particle Concentration in a Real Dental Office: Implications for Disease Transmission
Bibliographic record
Abstract
Aerosols generated during dental procedures are one of the most significant routes for infection transmission and are particularly relevant now in the context of COVID-19 pandemic. This study aimed to assess the effectiveness of an indoor air purifier on dental aerosol dispersion in dental offices. The spread and removal of aerosol particles generated from a specific dental operation in a dental office are quantified for a single dental activity in the area near the generation and corner of the office. The effects of the air purifier, door condition, and particle sizes on the spread and removal of particles were investigated. The results show that, in the worst-case scenario, it takes 95 min for 0.5-μm particles to settle and that it takes a shorter time for the larger particles. The air purifier expedited the removal time at least 6.3 times faster than the case with no air purifier in the generation zone. Our results also indicate that particles may be transported from the source to the rest of the room even when the particle concentrations in the generation zone dropped back to the background. Therefore, it is inaccurate to conclude that indoor purifiers help reduce the transmission of COVID-19. Dental offices still need other methods to reduce the transmission of viruses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".