Question · asked by 1 review

Epidermal growth factor (EGF), oral spray vs placebo: Oral mucositis (moderate to severe)

Population
Adults with cancer receiving treatment
Intervention
Epidermal growth factor (EGF), oral spray
Comparison
placebo
Outcome
Oral mucositis (moderate to severe)

Epidermal growth factor (EGF), oral spray showed no clear difference in oral mucositis (moderate to severe) compared with placebo; GRADE not reported.

What each review found

Include earlier review versions
No studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Epidermal growth factor (EGF), oral spray vs Placebo
Oral mucositis (moderate to severe) · Adults with cancer receiving treatment · Chemoradiotherapy to the head and neck
Epidermal growth factor (EGF), oral spray showed no clear difference in oral mucositis (moderate to severe) compared with placebo; GRADE not reported.
Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and… (2017) · Cochrane All outcomes
RR 1.06 [0.78, 1.43]1 study · 136 participantsGRADE not reported
Epidermal growth factor (EGF), oral spray vs Placebo
Oral mucositis (moderate to severe) · Adults with cancer receiving treatment · Radiotherapy to the head and neck
Epidermal growth factor (EGF), oral spray led to lower oral mucositis (moderate to severe) compared with placebo; GRADE not reported.
Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and… (2017) · Cochrane All outcomes
RR 0.67 [0.45, 0.99]1 study · 103 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Interventions for preventing oral mucositis in patients with cancer receiving treatment: cytokines and growth factors
2017 · Cochrane All outcomes
2017-05Not listedNot 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.