1000 mg champignon extract vs 50 mg champignon extract: Patient‐reported pain intensity or pain relief (VAS, NRS or categorical scale)
- Population
- Adolescents and adults aged over 16 years with clinical or self-assessed halitosis of oral origin; excluding induced or physiological halitosis, non-oral halitosis, and severe chronic periodontitis (clinical attachment level 5 mm or more)
- Intervention
- 1000 mg champignon extract
- Comparison
- 50 mg champignon extract
- Outcome
- Patient‐reported pain intensity or pain relief (VAS, NRS or categorical scale)
It is very uncertain whether 1000 mg champignon extract changes patient-reported visual analogue scale (VAS) score (scale 0 to 100) compared with 50 mg champignon extract (very low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
1000 mg champignon extract vs 50 mg champignon extract Patient-reported visual analogue scale (VAS) score (scale 0 to 100) · Adolescents and adults aged over 16 years with clinical or self-assessed halitosis of oral origin; excluding induced… · mean 2 weeks It is very uncertain whether 1000 mg champignon extract changes patient-reported visual analogue scale (VAS) score (scale 0 to 100) compared with 50 mg champignon extract (very low certainty). Interventions for managing halitosis (2019) · Cochrane All outcomes | MD -5.32 [-18.14, 7.5] | 1 study · 40 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Interventions for managing halitosis 2019 · Cochrane All outcomes | 2019-04 | Not listed | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- 1000 mg champignon extract vs placebo · Adolescents and adults aged over 16 years with clinical or self-assessed halitosis of… · 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.