Question · asked by 1 review
Fluoride-releasing resin-based sealant vs Non-invasive measures (fluoride varnish; no intervention): Radiographic proximal caries lesion progression
- Population
- Children and adults with non-cavitated proximal (interproximal) caries lesions in primary and/or permanent teeth (ages 3-45)
- Intervention
- Fluoride-releasing resin-based sealant
- Comparison
- Non-invasive measures (fluoride varnish; no intervention)
- Outcome
- Radiographic proximal caries lesion progression
Pooling 2 trials, resin-based sealants reduced radiographic proximal caries progression versus non-invasive measures (OR 0.24; ungraded subgroup).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Resin-based sealant (proximal sealing / fluoride-releasing resin sealant) vs Non-invasive measures (fluoride varnish; no intervention) Radiographic progression of proximal caries lesions · Children and adults with non-cavitated proximal (interproximal) caries lesions in primary and/or permanent teeth (ages… · Final follow-up (1 to 3.5 years), Multiple countries (Brazil, Saudi Arabia, Netherlands, USA, Turkey, Switzerland, Germany, New Zealand, Chile, Thailand); dental clinic/community settings, Sealant vs non-invasive (Fig. 5, subgroup 2.1.2) Pooling 2 trials, resin-based sealants reduced radiographic proximal caries progression versus non-invasive measures (OR 0.24; ungraded subgroup). Microinvasive interventions in the management of proximal caries lesions in primary and permanent teeth-… (2025) All outcomes | OR 0.24 [0.10, 0.55] | 2 studies · 111 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Microinvasive interventions in the management of proximal caries lesions in primary and permanent teeth- systematic review and… 2025 All outcomes | May 2024 | 17 | Low / Critically 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.