Question · asked by 1 review

Fluoride varnish vs Untreated lesions (single-arm before vs after proportion): Remineralisation of early enamel caries

Population
Patients (mainly children) with white spot lesions (WSL)
Intervention
Fluoride varnish
Comparison
Untreated lesions (single-arm before vs after proportion)
Outcome
Remineralisation of early enamel caries

Professionally applied 5% sodium fluoride varnish remineralised about two-thirds of early enamel caries lesions in children (pooled proportion 63.6%, 95% CI 36.0% to 91.2%; 4 studies); certainty was not GRADE-rated and heterogeneity was very high (I2=96%).

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Professionally applied 5% sodium fluoride (NaF) varnish vs Untreated lesions (single-arm before vs after proportion)
Remineralisation of early enamel caries (proportion of lesions remineralised) · Children with early enamel caries or white spot lesions · Children in clinical dental settings
Professionally applied 5% sodium fluoride varnish remineralised about two-thirds of early enamel caries lesions in children (pooled proportion 63.6%, 95% CI 36.0% to 91.2%; 4 studies); certainty was not GRADE-rated and heterogeneity was very high (I2=96%).
Caries remineralisation and arresting effect in children by professionally applied fluoride treatment - a… (2016) All outcomes
Proportion 0.636 [0.360, 0.912]4 studiesGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Caries remineralisation and arresting effect in children by professionally applied fluoride treatment - a systematic review.
2016 All outcomes
Publications from 1948 to 2014 (search date not reported)17Critically Low / Critically Low

Studies behind the findings

The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.

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.