1450 ppm F toothpaste vs 440 ppm F toothpaste: Caries increment, tooth index D(M)FT
- Population
- Young children with primary (deciduous) dentition (aged 1 to 6 years at study start)
- Intervention
- 1450 ppm F toothpaste
- Comparison
- 440 ppm F toothpaste
- Outcome
- Caries increment, tooth index D(M)FT
1450 ppm F toothpaste probably leads to lower caries increment, tooth index d(m)ft compared with 440 ppm F toothpaste (moderate certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
1450 ppm F toothpaste vs 440 ppm F toothpaste Caries increment, tooth index d(m)ft · Young children with primary (deciduous) dentition (aged 1 to 6 years at study start) · Follow-up: 60 months 1450 ppm F toothpaste probably leads to lower caries increment, tooth index d(m)ft compared with 440 ppm F toothpaste (moderate certainty). Fluoride toothpastes of different concentrations for preventing dental caries (2019) · Cochrane All outcomes | MD -0.34 [-0.59, -0.09] | 1 study · 2362 participants | Moderate |
1450 ppm F toothpaste vs 440 ppm F toothpaste Caries increment, tooth index d(m)ft · Young children with primary (deciduous) dentition (aged 1 to 6 years at study start) · 60 months Fluoride toothpastes of different concentrations for preventing dental caries (2010) · Cochrane All outcomesearlier version CD007868.PUB2, superseded | PF 11 [3, 19] | 1 study | GRADE not reported |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 |
|---|---|---|---|
| Fluoride toothpastes of different concentrations for preventing dental caries 2019 · Cochrane All outcomes | 2018-08 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- 1450 ppm F toothpaste vs 250 ppm F toothpaste · Young children with primary (deciduous) dentition (aged 1 to 6 years at study start) · 1 review
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.