Question · asked by 1 review
Xylitol-only sugar-free chewing gum vs Pooled controls: no gum, placebo/control gum, probiotic, or fluoride varnish: Streptococcus mutans load in the oral cavity (saliva and/or dental plaque)
- Population
- Adults and children (general population)
- Intervention
- Xylitol-only sugar-free chewing gum
- Comparison
- Pooled controls: no gum, placebo/control gum, probiotic, or fluoride varnish
- Outcome
- Streptococcus mutans load in the oral cavity (saliva and/or dental plaque)
Xylitol-only sugar-free gum significantly reduced Streptococcus mutans load versus pooled controls (SMD -0.46, 95% CI -0.64 to -0.28; 12 trials, I2 43%); certainty not formally graded.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Xylitol-only sugar-free chewing gum vs Pooled controls: no gum, placebo/control gum, probiotic, or fluoride varnish Streptococcus mutans load in the oral cavity (saliva and/or dental plaque) · Adults and children (general population) · Final/end-of-study time point (follow-up 2 weeks to 18 months), Schools, kindergartens, universities and community settings; adults and children; multiple countries, Overall (vs all controls) Xylitol-only sugar-free gum significantly reduced Streptococcus mutans load versus pooled controls (SMD -0.46, 95% CI -0.64 to -0.28; 12 trials, I2 43%); certainty not formally graded. A systematic review and meta-analysis of the role of sugar-free chewing gum on Streptococcus mutans. (2021) All outcomes | SMD -0.46 [-0.64, -0.28] | 12 studies · 1295 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| A systematic review and meta-analysis of the role of sugar-free chewing gum on Streptococcus mutans. 2021 All outcomes | 1 January 1946 to 31 August 2020 | 14 | Critically Low / Critically Low |
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.