Question · asked by 1 review

Amifostine vs no treatment/placebo: Salivary flow rate (unstimulated)

Population
Patients receiving radiotherapy on its own or in addition to chemotherapy to the head and neck region
Intervention
Amifostine
Comparison
no treatment/placebo
Outcome
Salivary flow rate (unstimulated)

It is very uncertain whether amifostine changes salivary flow rate (mg/5 min, unstimulated) compared with no treatment/placebo (very low certainty).

What each review found

Current versions only
No studies found 0 · 0%GRADE not reported 1 · 50%Very low 1 · 50%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Amifostine vs No treatment/placebo
Salivary flow rate (mg/5 min, unstimulated) · Patients receiving radiotherapy on its own or in addition to chemotherapy to the head and neck region · 12 months postRT
It is very uncertain whether amifostine changes salivary flow rate (mg/5 min, unstimulated) compared with no treatment/placebo (very low certainty).
Pharmacological interventions for preventing dry mouth and salivary gland dysfunction following radiotherapy (2017) · Cochrane All outcomes
MD 0.32 [0.09, 0.55]1 study · 27 participantsVery low
Amifostine vs No treatment/placebo
Salivary flow rate (unstimulated) · Patients receiving radiotherapy on its own or in addition to chemotherapy to the head and neck region · End of RT
Amifostine led to higher salivary flow rate (unstimulated) compared with no treatment/placebo; GRADE not reported.
Pharmacological interventions for preventing dry mouth and salivary gland dysfunction following radiotherapy (2017) · Cochrane All outcomes
MD 0.34 [0.07, 0.61]83 participantsGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Pharmacological interventions for preventing dry mouth and salivary gland dysfunction following radiotherapy
2017 · Cochrane All outcomes
2016-09Not 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.