Question · asked by 1 review
G‐CSF vs placebo or no treatment: Oral mucositis (any severity)
- Population
- Adults with cancer receiving treatment
- Intervention
- G‐CSF
- Comparison
- placebo or no treatment
- Outcome
- Oral mucositis (any severity)
Granulocyte-colony stimulating factor (G-CSF) led to lower oral mucositis (any severity) compared with placebo or no treatment; GRADE not reported.
What each review found
Include earlier review versionsNo 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 |
|---|---|---|---|
Granulocyte-colony stimulating factor (G-CSF) vs Placebo or no treatment Oral mucositis (any severity) · Adults with cancer receiving treatment · Chemotherapy alone for lung cancer Granulocyte-colony stimulating factor (G-CSF) led to lower oral mucositis (any severity) compared with placebo or no treatment; GRADE not reported. Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and… (2017) · Cochrane All outcomes | RR 0.59 [0.40, 0.87] | 1 study · 195 participants | GRADE not reported |
Granulocyte-colony stimulating factor (G-CSF) vs Placebo or no treatment Oral mucositis (any severity) · Adults with cancer receiving treatment · RT to head and neck Granulocyte-colony stimulating factor (G-CSF) showed no clear difference in oral mucositis (any severity) compared with placebo or no treatment; GRADE not reported. Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and… (2017) · Cochrane All outcomes | RR 1.02 [0.86, 1.22] | 2 studies · 54 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 in patients with cancer receiving treatment: cytokines and growth factors 2017 · Cochrane All outcomes | 2017-05 | 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.