Question · asked by 1 review

Silver diamine fluoride (SDF) vs Nano-silver fluoride (NSF; with or without green tea extract): Caries arrestment at the individual (patient) level

Population
Children and adults with active dental caries (aged 1-50; mostly preschool/school children with primary dentition)
Intervention
Silver diamine fluoride (SDF)
Comparison
Nano-silver fluoride (NSF; with or without green tea extract)
Outcome
Caries arrestment at the individual (patient) level

Pooling two trials, nano-silver fluoride achieved higher individual-level caries arrestment than SDF (OR 0.39 favoring the comparator; ungraded subgroup).

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Silver diamine fluoride (SDF; 38% in most trials, 30% in three) vs Nano-silver fluoride (NSF; with or without green tea extract)
Caries arrestment at the individual (patient) level · Children and adults with active dental caries (aged 1-50; mostly preschool/school children with primary dentition) · 3 weeks to 12 months, Children/adults with dental caries; multiple countries; community/clinic care setting not clearly specified, SDF vs NSF
Pooling two trials, nano-silver fluoride achieved higher individual-level caries arrestment than SDF (OR 0.39 favoring the comparator; ungraded subgroup).
The role of silver diamine fluoride as dental caries preventive and arresting agent: a systematic review and… (2024) All outcomes
OR 0.39 [0.26, 0.60]2 studies · 384 participantsGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
The role of silver diamine fluoride as dental caries preventive and arresting agent: a systematic review and meta-analysis.
2024 All outcomes
Not specified (publication window 2014-2024; references accessed July 2024)20Critically Low / Critically Low

The same intervention and outcome, asked in another population or against another comparator.

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.