Ciprofloxacin vs placebo added to cycled and culture‐based inhaled tobramycin (TNS) therapy: Adverse effects of antibiotics: proportion of participants with one or more serious adverse event
- Population
- adults and children with cystic fibrosis and a positive microbiological isolate of P aeruginosa from a respiratory tract specimen
- Intervention
- Ciprofloxacin
- Comparison
- placebo added to cycled and culture‐based inhaled tobramycin (TNS) therapy
- Outcome
- Adverse effects of antibiotics: proportion of participants with one or more serious adverse event
Ciprofloxacin showed no clear difference in adverse effects of antibiotics: proportion of participants with one… compared with placebo added to cycled and culture‐based inhaled tobramycin (TNS)… (moderate certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Ciprofloxacin vs placebo added to cycled and culture‐based inhaled tobramycin (TNS) therapy Adverse effects of antibiotics: proportion of participants with one or more serious adverse event · adults and children with cystic fibrosis and a positive microbiological isolate of P aeruginosa from a respiratory… Ciprofloxacin showed no clear difference in adverse effects of antibiotics: proportion of participants with one… compared with placebo added to cycled and culture‐based inhaled tobramycin (TNS)… (moderate certainty). Antibiotic strategies for eradicating Pseudomonas aeruginosa in people with cystic fibrosis (2023) · Cochrane Headline outcomes only | OR 1.54 [0.93, 2.57] | 1 study · 304 participants | Moderate |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Antibiotic strategies for eradicating Pseudomonas aeruginosa in people with cystic fibrosis 2023 · Cochrane Headline outcomes only | Not reported | Not extracted | Not 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.