Disinfection methods of dental unit waterlines contamination: a systematic review
Bibliographic record
Abstract
Background. Severe contamination of dental unit waterlines was found in healthcare settings. The benefits of decontamination methods are controversial. The aim of this review was to systematically evaluate disinfection methods in contamination control of dental unit waterlines. Methods. The terms ‘dental unit waterline(s) or DUWL(s) or dental unit water line(s)’ were searched through PubMed, Cochrane Library, Embase, Web of Science and Scopusup to 31 May 2021. The DUWLs’ output water was incubated on R2A agar at 20–28 °C for 5–7 days to evaluate heterotrophic mesophilic bacteria. The risk of bias was evaluated by a modified Newcastle-Ottawa quality assessment scale. Results. Eighteen papers from the literature were included. One study indicated that water supply played a crucial role in disinfecting DUWLs. Three studies indicated that flushing decreased bacteria counts but did not meet the American CDC standard (500 c.f.u. ml −1 ). All chlorine- and peroxide-containing disinfectants except sodium hypochlorite in one of 15 studies as well as three mouthrinses and citrus botanical extract achieved the standard (≤500 c.f.u. ml −1 ). The included studies were of low (1/18), moderate (6/18) and high (11/18) quality. Conclusion. Independent water reservoirs are recommended for disinfecting DUWLs using distilled water. Flushing DUWLs should be combined with disinfections. Nearly all the chlorine-, chlorhexidine- and peroxide-containing disinfectants, mouthrinses and citrus botanical extract meet the standard for disinfecting DUWLs. Alkaline peroxide would lead to tube blockage in the DUWLs. Regularly changing disinfectants can reduce the risk of occurrence of disinfectant-resistant strains of microbes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".