Topical fluoride therapy (toothpaste, mouthrinse, gel or varnish; any fluoride agent/concentration; frequency at least once a year) vs placebo or no treatment: Unacceptability of treatment (dropouts/exclusions during the trial)
- Population
- Children and adolescents aged 16 years or less
- Intervention
- Topical fluoride therapy (toothpaste, mouthrinse, gel or varnish; any fluoride agent/concentration; frequency at least once a year)
- Comparison
- placebo or no treatment
- Outcome
- Unacceptability of treatment (dropouts/exclusions during the trial)
Topical fluoride therapy (toothpaste, mouthrinse, gel or varnish;… showed no clear difference in unacceptability of treatment (dropouts/exclusions during the trial) compared with placebo or no treatment; GRADE not reported.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Topical fluoride therapy (toothpaste, mouthrinse, gel or varnish; any fluoride agent/concentration; frequency at least once a year) vs Placebo or no treatment Unacceptability of treatment (dropouts/exclusions during the trial) · Children and adolescents aged 16 years or younger · During trial, No-treatment-controlled trials Topical fluoride therapy (toothpaste, mouthrinse, gel or varnish;… showed no clear difference in unacceptability of treatment (dropouts/exclusions during the trial) compared with placebo or no treatment; GRADE not reported. Topical fluoride (toothpastes, mouthrinses, gels or varnishes) for preventing dental caries in children and… (2003) · Cochrane All outcomes | RR 1.20 [0.85, 1.70] | 10 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Topical fluoride (toothpastes, mouthrinses, gels or varnishes) for preventing dental caries in children and adolescents 2003 · Cochrane All outcomes | 2001-10 | Not listed | Not assessed |
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.