Granulocyte-colony stimulating factor (G-CSF; pegfilgrastim, single dose) vs Granulocyte-colony stimulating factor (G-CSF; filgrastim, multiple doses): Oral mucositis (moderate to severe)
- Population
- Children receiving bone marrow/stem cell transplantation after conditioning therapy for mixed cancers
- Intervention
- Granulocyte-colony stimulating factor (G-CSF; pegfilgrastim, single dose)
- Comparison
- Granulocyte-colony stimulating factor (G-CSF; filgrastim, multiple doses)
- Outcome
- Oral mucositis (moderate to severe)
Granulocyte-colony stimulating factor (G-CSF; pegfilgrastim, single… showed no clear difference in oral mucositis (moderate to severe) compared with granulocyte-colony stimulating factor (G-CSF; filgrastim, multiple…; GRADE not reported.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Granulocyte-colony stimulating factor (G-CSF; pegfilgrastim, single dose) vs Granulocyte-colony stimulating factor (G-CSF; filgrastim, multiple doses) Oral mucositis (moderate to severe) · Children receiving bone marrow/stem cell transplantation after conditioning therapy for mixed cancers · Children Granulocyte-colony stimulating factor (G-CSF; pegfilgrastim, single… showed no clear difference in oral mucositis (moderate to severe) compared with granulocyte-colony stimulating factor (G-CSF; filgrastim, multiple…; GRADE not reported. Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and… (2017) · Cochrane All outcomes | RR 0.78 [0.55, 1.11] | 1 study · 61 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.