A Review of “Optimal Fallow Period” Guidance Across Canadian Jurisdictions
Bibliographic record
Abstract
INTRODUCTION: Understanding how different countries have responded to mitigate the risk of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) transmission in dental offices is important. This article describes the different approaches taken towards optimal fallow periods in Canadian jurisdictions. METHODS: We searched publicly available information from dentist and dental hygiene regulator websites across the 10 provinces and 3 territories in Canada. We also searched for guidance documents on dental associations' websites or through personal communication with government officials. We extracted and tabulated information on fallow period recommendations or guidance, when available. RESULTS: Nine jurisdictions (6 provinces and all 3 territories) acknowledge or provide guidance on fallow periods following aerosol-generating procedures. Among those who have provided guidance regarding a fallow period, recommendations follow the Centers for Disease Control and Prevention guidance if the air changes per hour (ACH) in the dental operatory is known. CONCLUSION: The evidence for deciding on optimal fallow period is limited and still being explored, resulting in substantial variation across Canadian jurisdictions. A focus on developing scientific evidence relevant to dentistry and assimilating existing science is crucial to establishing consistency and uniformity in information to deliver safe oral health care services.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".