Fluoride varnish vs Placebo varnish, resin sealant, or standard care (oral hygiene instructions): Prevalence of white spot lesions (WSL) at debonding, subject level
- Population
- Healthy adolescents and adults undergoing orthodontic treatment with fixed multi-bracket appliances
- Intervention
- Fluoride varnish
- Comparison
- Placebo varnish, resin sealant, or standard care (oral hygiene instructions)
- Outcome
- Prevalence of white spot lesions (WSL) at debonding, subject level
Fluoride varnish applied regularly during fixed orthodontic treatment may reduce the prevalence of white spot lesions at debonding (pooled RR 0.64, 95% CI 0.42 to 0.98), but the certainty of evidence is very low.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fluoride varnish applied periodically (at least quarterly) around the bracket bases during treatment vs Placebo varnish, resin sealant, or standard care (oral hygiene instructions) Prevalence of white spot lesions (WSL) at debonding, subject level · Healthy adolescents and adults undergoing orthodontic treatment with fixed multi-bracket appliances · At debonding, Orthodontic care (fixed multi-bracket appliance treatment) Fluoride varnish applied regularly during fixed orthodontic treatment may reduce the prevalence of white spot lesions at debonding (pooled RR 0.64, 95% CI 0.42 to 0.98), but the certainty of evidence is very low. Prevention of white spot lesions with fluoride varnish during orthodontic treatment with fixed appliances: a… (2023) All outcomes | RR 0.64 [0.42, 0.98] | 5 studies · 558 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Prevention of white spot lesions with fluoride varnish during orthodontic treatment with fixed appliances: a systematic review. 2023 All outcomes | 7 October 2022 | 12 | Low / Low |
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.