COVID-19 Pandemic and Dental Practice
Bibliographic record
Abstract
SARS-CoV-2, a virus causing severe acute respiratory syndrome, has inundated the whole world, generating global health concerns. There is a wildfire-like effect, despite the extensive range of efforts exercised by the affected countries to restrain the expanse of this pandemic, owing to its community spread pattern. Dental specialists in the upcoming days will likely come across patients with presumed or confirmed COVID-19 and will have to ensure stringent infection prevention and control to prevent its nosocomial spread. This paper strives to provide a brief overview of the etiology, incubation, symptoms, and transmission paradigms of this novel infection and how to minimize the spread in a dental healthcare setting. This review presents evidence-based patient management practice and protocols from the available literature to help formulate a contingency plan with recommendations, for the dental practices prior to patients' visit, during in-office dental treatment, and post-treatment, during the pandemic and after.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".