Fluoride varnish vs Fluoride in dentifrice: Caries increment, surface index D(M)FS
- Population
- Children or adolescents
- Intervention
- Fluoride varnish
- Comparison
- Fluoride in dentifrice
- Outcome
- Caries increment, surface index D(M)FS
The illustrative network meta-regression estimated an SMD for caries increment for fluoride in varnish versus fluoride in dentifrice across randomization year; the review did not state whether the difference was significant and certainty was not graded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fluoride in varnish vs Fluoride in dentifrice Caries increment in permanent teeth (DMFS change from baseline) · Children or adolescents · At least 1 year or school year, Network meta-regression covariate: randomization year The illustrative network meta-regression estimated an SMD for caries increment for fluoride in varnish versus fluoride in dentifrice across randomization year; the review did not state whether the difference was significant and certainty was not graded. Graphs of study contributions and covariate distributions for network meta-regression. (2018) All outcomes | no estimate extracted | Not reported | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Graphs of study contributions and covariate distributions for network meta-regression. 2018 All outcomes | Not specified | Not listed | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Fluoride varnish vs placebo · Children or adolescents · 1 review
- Fluoride varnish vs Fluoride gel · Children or adolescents · 1 review
- Fluoride varnish vs Fluoride in rinse · Children or adolescents · 1 review
- Fluoride varnish vs no treatment · Children or adolescents · 1 review
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.