Question · asked by 1 review

Summary of findings: anti‐pseudomonal antibiotics vs placebo: Frequency of hospital admissions for pulmonary exacerbations ‐ not measured

Summary of findings: anti‐pseudomonal antibiotics may leads to lower pulmonary exacerbations: frequency of one or more hospital admissions compared with placebo (low certainty).

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Summary of findings: anti‐pseudomonal antibiotics vs placebo
Pulmonary exacerbations: frequency of one or more hospital admissions · adults and children with CF and P aeruginosa
Summary of findings: anti‐pseudomonal antibiotics may leads to lower pulmonary exacerbations: frequency of one or more hospital admissions compared with placebo (low certainty).
Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis (2022) · Cochrane Headline outcomes only
RR 0.66 [0.47, 0.93]3 studies · 946 participantsLow
Summary of findings: anti‐pseudomonal antibiotics vs placebo
Pulmonary exacerbations: frequency of one or more hospital admissions · adults and children with CF and P aeruginosa
Summary of findings: anti‐pseudomonal antibiotics may leads to lower pulmonary exacerbations: frequency of one or more hospital admissions compared with placebo (low certainty).
Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis (2018) · Cochrane Headline outcomes onlyearlier version CD001021.PUB3, superseded
RR 0.66 [0.47, 0.93]3 studies · 946 participantsLow

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

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis
2022 · Cochrane Headline outcomes only
Not reportedlist not extracted yetNot assessed

Studies behind the findings

None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.

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.