Clinical orthodontic management during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: To provide a comprehensive summary of the implications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) on orthodontic treatment, contingency management, and provision of emergency orthodontic treatment, using currently available data and literature. MATERIALS AND METHODS: Orthodontically relevant sources of information were searched using electronic databases including PubMed and Google Scholar and current reports from major health bodies such as Centers for Disease Control and Prevention, World Health Organization, National Institutes of Health, and major national orthodontic associations. RESULTS: Where available, peer-reviewed and more recent publications were given priority. Due to the rapidly evolving nature of COVID-19 and limitations in quality of evidence, a narrative synthesis was undertaken. Relevant to orthodontics, human-to human transmission of SARS-CoV-2 occurs predominantly through the respiratory tract via droplets, secretions (cough, sneeze), and or direct contact, where the virus enters the mucous membrane of the mouth, nose, and eyes. The virus can remain stable for days on plastic and stainless steel. Most infected persons experience a mild form of disease, but those with advanced age or underlying comorbidities may suffer severe respiratory and multiorgan complications. CONCLUSIONS: During the spread of the COVID-19 pandemic, elective orthodontic treatment should be suspended and resumed only when permitted by federal, provincial, and local health regulatory authorities. Emergency orthodontic treatment can be provided by following a contingency plan founded on effective communication and triage. Treatment advice should be delivered remotely first when possible, and where necessary, in-person treatment can be performed in a well-prepared operatory following the necessary precautions and infection prevention and control (IPAC) protocol.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".