P11-4 + fluoride varnish vs CPP-ACP: Remineralization / reduction in lesion severity (change in QLF, laser fluorescence, or lesion area)
- Population
- Patients with white spot lesions / early caries lesions (WSLs/ECLs), including orthodontic and non-orthodontic lesions, active caries lesions, MIH, and artificial in vitro lesions; predominantly children and adolescents
- Intervention
- P11-4 + fluoride varnish
- Comparison
- CPP-ACP
- Outcome
- Remineralization / reduction in lesion severity (change in QLF, laser fluorescence, or lesion area)
P11-4 plus fluoride varnish was significantly better than CPP-ACP (SMD -0.78, 95% CI -1.33 to -0.24; indirect evidence only); certainty of evidence not assessed (no GRADE).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
P11-4 + fluoride varnish vs CPP-ACP Remineralization / reduction in lesion severity (change in QLF, laser fluorescence, or lesion area) · Patients with white spot lesions / early caries lesions (WSLs/ECLs), including orthodontic and non-orthodontic… · RCTs (clinical and in vitro); predominantly children and adolescents; orthodontic and non-orthodontic white spot lesions, early caries lesions, and artificial lesions P11-4 plus fluoride varnish was significantly better than CPP-ACP (SMD -0.78, 95% CI -1.33 to -0.24; indirect evidence only); certainty of evidence not assessed (no GRADE). Comparison of therapies of white spot lesions: a systematic review and network meta-analysis. (2023) All outcomes | SMD -0.78 [-1.33, -0.24] | 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 |
|---|---|---|---|
| Comparison of therapies of white spot lesions: a systematic review and network meta-analysis. 2023 All outcomes | July 2022 (last search reported in abstract); search window January 2007 to June 2022 (Methods) | 81 | Low / Critically Low |
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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.