Fluoride gel (topically applied; operator- or self-applied; any fluoride agent/concentration; frequency at least once a year) vs placebo: Caries increment in permanent teeth
- Population
- Children or adolescents
- Intervention
- Fluoride gel (topically applied; operator- or self-applied; any fluoride agent/concentration; frequency at least once a year)
- Comparison
- placebo
- Outcome
- Caries increment in permanent teeth
The NMR reported a difference between fluoride in gel and placebo at randomisation years 1954 to 1980; no GRADE assessment was reported.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fluoride in gel vs Placebo Caries increment in permanent teeth · Children or adolescents · At least 1 year or school year, Randomisation year, NMR covariate The NMR reported a difference between fluoride in gel and placebo at randomisation years 1954 to 1980; no GRADE assessment was reported. 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 gel (topically applied; operator- or self-applied; any fluoride agent/concentration; frequency at least once a year) vs Fluoride mouthrinse · Children and adolescents aged 16 years or less · 1 review
- Fluoride gel (topically applied; operator- or self-applied; any fluoride agent/concentration; frequency at least once a year) 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.