Question · asked by 1 review
Keratinocyte GF 50 mg vs Keratinocyte GF 25 mg: Oral mucositis (severe)
- Population
- Patients with cancer receiving radiotherapy, chemotherapy or targeted therapies
- Intervention
- Keratinocyte GF 50 mg
- Comparison
- Keratinocyte GF 25 mg
- Outcome
- Oral mucositis (severe)
Keratinocyte GF 50 mg showed no clear difference in oral mucositis (severe) compared with keratinocyte GF 25 mg; GRADE not reported.
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Keratinocyte GF 50 mg vs Keratinocyte GF 25 mg Oral mucositis (severe) · Patients with cancer receiving radiotherapy, chemotherapy or targeted therapies · Median 28 days Keratinocyte GF 50 mg showed no clear difference in oral mucositis (severe) compared with keratinocyte GF 25 mg; GRADE not reported. Interventions for preventing oral mucositis for patients with cancer receiving treatment (2011) · Cochrane All outcomes | RR 0.33 [0.04, 2.83] | 1 study · 28 participants | GRADE not reported |
Keratinocyte GF 50 mg vs Keratinocyte GF 25 mg Oral mucositis (severe) · Patients with cancer receiving radiotherapy, chemotherapy or targeted therapies · Median 28 days Keratinocyte GF 50 mg showed no clear difference in oral mucositis (severe) compared with keratinocyte GF 25 mg; GRADE not reported. Interventions for preventing oral mucositis for patients with cancer receiving treatment (2010) · Cochrane All outcomesearlier version CD000978.PUB4, superseded | RR 0.33 [0.04, 2.83] | 1 study · 28 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Interventions for preventing oral mucositis for patients with cancer receiving treatment 2011 · Cochrane All outcomes | 2011-02 | Not listed | Not assessed |
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.