Aerosolization of mycobacteria and legionellae during dental treatment: low exposure despite dental unit contamination
Bibliographic record
Abstract
Dental unit waterlines (DUWL) support growth of a dense microbial population that includes pathogens and hypersensitivity-inducing bacteria, such as Legionella spp. and non-tuberculous mycobacteria (NTM). Dynamic dental instruments connected to DUWL generate aerosols in the work environment, which could allow waterborne pathogens to be aerosolized. The use of the real-time quantitative polymerase chain reaction (qPCR) provides a more accurate estimation of exposure levels compared with the traditional culture approach. Bioaerosol sampling was performed 13 times in an isolated dental treatment room according to a standardized protocol that included four dental prophylaxis treatments. Inhalable dust samples were taken at the breathing zone of both the hygienist and patient and outside the treatment room (control). Total bacteria as well as Legionella spp. and NTM were quantified by qPCR in bioaerosol and DUWL water samples. Dental staff and patients are exposed to bacteria generated during dental treatments (up to 4.3 E + 05 bacteria per m(3) of air). Because DUWL water studied was weakly contaminated by Legionella spp. and NTM, their aerosolization during dental treatment was not significant. As a result, infectious and sensitization risks associated with legionellae and NTM should be minimal.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".