Question · asked by 1 review

Pilocarpine vs Artificial saliva: Adverse effects (side effects; treatment withdrawals)

Population
Patients with radiation-induced salivary gland dysfunction
Intervention
Pilocarpine
Comparison
Artificial saliva
Outcome
Adverse effects (side effects; treatment withdrawals)

What each review found

Current versions only
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
Pilocarpine hydrochloride (parasympathomimetic) vs Artificial saliva (mucin-based)
Adverse effects (side effects; treatment withdrawals) · Patients with radiation-induced salivary gland dysfunction
Parasympathomimetic drugs for the treatment of salivary gland dysfunction due to radiotherapy (2015) · Cochrane All outcomes
Davies 1994: main side effects with pilocarpine were nausea 20%, sweating 15%, lacrimation 10%. One participant definitely withdrew because of side effects; another may have.1 study · 20 participantsGRADE not reported
Pilocarpine hydrochloride (parasympathomimetic) vs Artificial saliva (mucin-based)
Adverse effects (side effects; treatment withdrawals) · Patients with radiation-induced salivary gland dysfunction
Parasympathomimetic drugs for the treatment of salivary gland dysfunction due to radiotherapy (2007) · Cochrane All outcomesearlier version CD003782.PUB2, superseded
Davies 1994: main side effects with pilocarpine were nausea 20%, sweating 15%, lacrimation 10%. One participant definitely withdrew because of side effects; another may have.1 study · 20 participantsGRADE not reported

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Parasympathomimetic drugs for the treatment of salivary gland dysfunction due to radiotherapy
2015 · Cochrane All outcomes
2015-07Not listedNot assessed

The same intervention and outcome, asked in another population or against another comparator.

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.