Question · asked by 1 review
Fluoride mouthwash vs Blank control: Cariogenic bacteria (Mutans streptococci)
- Population
- People undergoing orthodontic treatment (any age; 9 to 34 years in included studies)
- Intervention
- Fluoride mouthwash
- Comparison
- Blank control
- Outcome
- Cariogenic bacteria (Mutans streptococci)
Short-term fluoride mouthwash reduced Mutans streptococci, but certainty is very low.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 0 · 0%Very low 2 · 100%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Fluoride mouthwash vs Blank control Cariogenic bacteria (Mutans streptococci) · Orthodontic patients · ≤ 3 months Short-term fluoride mouthwash reduced Mutans streptococci, but certainty is very low. The efficacy of mouthwashes on oral microorganisms and gingivitis in patients undergoing orthodontic… (2023) All outcomes | Colony count of MS was significantly reduced compared to baseline according to two studies [33; 34]. | 2 studies | Very low |
Fluoride mouthwash vs Blank control Cariogenic bacteria (Mutans streptococci) · Orthodontic patients · ≥ 12 months Long-term fluoride mouthwash did not change Mutans streptococci versus blank control, with very low-certainty evidence. The efficacy of mouthwashes on oral microorganisms and gingivitis in patients undergoing orthodontic… (2023) All outcomes | Colony count of MS was similar to blank control according to two studies [37; 59]. | 2 studies | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| The efficacy of mouthwashes on oral microorganisms and gingivitis in patients undergoing orthodontic treatment: a systematic review and… 2023 All outcomes | 16 April 2022 (databases and grey literature searched by 16 April 2022; no publication-year limit) | 47 | Low / Moderate |
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.