Aerosolization of mycobacteria and legionellae during dental treatment: low exposure despite dental unit contamination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it