Recommendations for the re-opening of dental services : a rapid review of international sources
Bibliographic record
Abstract
The COVID-19 Dental Services Evidence Review Working Group would like to thank and acknowledge the contribution of the following individuals for providing the advice and access to the international guidance documents necessary for this rapid review: Colette Bridgman, Chief Dental Officer, Wales; Alonso Carrasco-Labra, Director, ADA Science & Research Institute; Riana Clarke, National Clinical Director Oral Health, New Zealand; Michael Donaldson, Chief Dental Officer, Northern Ireland; Tom Ferris, Chief Dental Officer, Scotland; Sara Hurley, Chief Dental Officer, England; Marco Landi, Council of European Dentists; Timothy Ricks, Chief Dental Officer, US Public Health Service; James Taylor, Chief Dental Officer, Canada; Benoit Varenne, Dental Officer, World Health Organization. The COVID-19 Dental Services Evidence Review Working Group are grateful for the help and support provided by Shona Floate, University of Glasgow; Anne Littlewood, Laura MacDonald and Helen Worthington from Cochrane Oral Health; David Felix, Postgraduate Dental Dean, NES and colleagues from NES’s Clinical Effectiveness workstream: Samantha Rutherford; Douglas Stirling; Michele West; Linda Young.
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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 | 0.251 | 0.437 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".