Question · asked by 1 review
Summary of findings: continuous cycles alternating aztreonam lysine for inhalation with tobramycin for inhalation solution vs continuous cycles alternating placebo with tobramycin: FEV1 (% predicted): mean change from baseline
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Summary of findings: continuous cycles alternating aztreonam lysine for inhalation with tobramycin for inhalation solution vs continuous cycles alternating placebo with tobramycin FEV1 (% predicted): mean change from baseline (average values across the end of the 3 treatment cycles) · children and adults with CF and P aeruginosa Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis (2022) · Cochrane Headline outcomes only | no estimate extracted | 1 study · 90 participants | Low |
Summary of findings: continuous cycles alternating aztreonam lysine for inhalation with tobramycin for inhalation solution vs continuous cycles alternating placebo with tobramycin FEV1 (% predicted): mean change from baseline (average values across the end of the three treatment cycles) · children and adults with CF and P aeruginosa Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis (2018) · Cochrane Headline outcomes onlyearlier version CD001021.PUB3, superseded | no estimate extracted | 1 study · 90 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Inhaled anti‐pseudomonal antibiotics for long‐term therapy in cystic fibrosis 2022 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not 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.