Utilization of silver diamine fluoride by dentists in Canada: a review of the Non-Insured Health Benefits Dental Claims database
Bibliographic record
Abstract
INTRODUCTION: In August 2020, Indigenous Services Canada's Non-Insured Health Benefits (NIHB) program approved reimbursement for silver diamine fluoride (SDF), a dental caries-arresting agent, for NIHB-eligible clients of all ages. We investigated the utilization of SDF for NIHB-eligible children and youth and determined trends and regional differences. METHODS: The NIHB program provided data on SDF claims for children and youth (< 17 years) from 1 August 2020 to 31 July 2022. We derived descriptive statistics and calculated rates of SDF application by dividing the number of children and youth with SDF claims by the number of NIHB-eligible children and youth (n = 215 215). There were 4158 SDF claims for 3465 children and youth (1542 in 2020-2021 and 1923 in 2021-2022, a 24.7% increase). The mean (SD) age was 7.9 (4.0) years, and 52.9% were female. General dentists made the most claims (87.1%). Manitoba had the most initial claims (19.6%), but Alberta had the highest number of follow-up claims. Nunavut (37.0/1000; 95% CI: 33.8-40.4) and Northwest Territories (20.9/1000, 95% CI: 17.2-25.1) had the highest rates of SDF claims. The increase in the number of SDF claims over the 2 years may indicate that more dental care providers have become aware that the NIHB program covers SDF treatment and have incorporating it into their caries treatment approaches. Still, few children and youth received follow-up SDF applications, potentially reducing the effectiveness of caries arrest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.040 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".