Coronavirus outbreaks and infection prevention in dentistry: a narrative review.
Bibliographic record
Abstract
Objective: This narrative review aims to compile and analyse infection prevention and control (IPAC) practices followed by dental clinics during 3 coronavirus outbreaks: SARS (2002-2004), MERS (2012-2014), and COVID-19 (2019-); and to draw parallels from them for future epidemics. Methods: Data were collected from 3 databases: Google Scholar, PubMed, and Embase using search terms "SARS," "MERS," "COVID-19," "infection control," "disinfection," and "sterilization". Results: Careful examination of 108 peer-reviewed articles on the 3 outbreaks revealed the following commonalities in the IPAC practices of dental clinics: use of sodium hypochlorite (surface disinfectant), ethanol and 1-propanol (hand hygiene), povidone-iodine (oral rinse), high-volume evacuation (HVE), rubber dam isolation, anti-retraction handpieces, and fogging. Discussion & Conclusion: Ethanol, 1-propanol, sodium hypochlorite, povidone-iodine, photocatalysis, and fogging have been shown to be effective against various coronaviruses. However, more studies are required to validate the effectiveness of anti-retraction handpieces, rubber dam isolation, HVE, and cold atmospheric plasma specifically in infection control of the current coronavirus strain, SARS-CoV-2.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".