Cost effectiveness of a fluoride varnish daycare program versus usual care in central Winnipeg, Canada.
Bibliographic record
Abstract
Objective: This project compares the cost effectiveness of a preventive fluoride varnish (FV) program with usual dental care (surgery under general anesthesia [GA]) for preschool children in 2 low-income communities in Winnipeg, Canada. Methods: Program impact is described in terms of cost, cavities avoided, and reductions in surgery volume. Aggregate data for 873 children ages 1 to 6 years old enrolled in the Winnipeg Regional Health Authority Daycare Fluoride Varnish Program in January 2018 were analysed using a Markov model. Results: The program was found to save approximately $822.98 per child over 5 years versus usual dental care. There were 4.38 cavities avoided per child and a savings of $187.71/cavity for the FV group. Participants' need for dental surgery under GA was reduced from 19.1% in the usual care group to 1.6% in the FV group (92% reduction) over 5 years. Sensitivity analyses using a Monte Carlo simulation showed that the program was cost effective over usual care 100% of the time. Finally, it was estimated that the program had saved $753,000 since its inception, or approximately $41.15 per FV application. Conclusion: The FV intervention had better health outcomes, lower costs, and was less invasive than usual care involving dental surgery under GA for children enrolled in the program.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".