Question · asked by 1 review
Micro-invasive treatment (proximal sealing or resin infiltration): resin sealant, glass ionomer sealant, polyurethane/adhesive patch, or resin infiltration vs Non-invasive / control treatment: Material deficiency (retention/adaptation loss; re-treatment)
- Population
- Dentate children and adults with non-cavitated proximal caries lesions extending into enamel or dentine (not pulp) in primary or permanent teeth (trial ages 4 to 39 years)
- Intervention
- Micro-invasive treatment (proximal sealing or resin infiltration): resin sealant, glass ionomer sealant, polyurethane/adhesive patch, or resin infiltration
- Comparison
- Non-invasive / control treatment
- Outcome
- Material deficiency (retention/adaptation loss; re-treatment)
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Micro-invasive treatment (proximal sealing or resin infiltration): resin sealant, glass ionomer sealant, polyurethane/adhesive patch, or resin infiltration vs Non-invasive measures (e.g. fluoride varnish, flossing/oral hygiene advice), no treatment, or placebo Material deficiency (retention/adaptation loss; re-treatment) · Dentate children and adults with non-cavitated proximal caries lesions extending into enamel or dentine (not pulp) in… · 3 years Micro‐invasive interventions for managing proximal dental decay in primary and permanent teeth (2015) · Cochrane All outcomes | One study (Alkilzy 2011, sealant patch) reported material deficiency: 10% total and 23% partial loss of proximal sealants after three years; 53% maintained perfect margins and none had open margins. No studies reported… | 1 study | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Micro‐invasive interventions for managing proximal dental decay in primary and permanent teeth 2015 · Cochrane All outcomes | 2014-12-31 | 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.