Cycled inhaled tobramycin vs culture‐based TNS: 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
- Cycled inhaled tobramycin
- Comparison
- culture‐based TNS
- Outcome
- Adverse effects of antibiotics: proportion of participants with one or more serious adverse event
Cycled inhaled tobramycin (TNS) probably leads to lower adverse effects of antibiotics: proportion of participants with one… compared with culture‐based TNS (moderate certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Cycled inhaled tobramycin (TNS) vs culture‐based TNS Adverse effects of antibiotics: proportion of participants with one or more serious adverse events · adults and children with cystic fibrosis and a positive microbiological isolate of P aeruginosa from a respiratory… Cycled inhaled tobramycin (TNS) probably leads to lower adverse effects of antibiotics: proportion of participants with one… compared with culture‐based TNS (moderate certainty). Antibiotic strategies for eradicating Pseudomonas aeruginosa in people with cystic fibrosis (2023) · Cochrane Headline outcomes only | OR 0.85 | 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 |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Cycled inhaled tobramycin vs culture‐based inhaled tobramycin · adults and children with cystic fibrosis and a positive microbiological isolate of P… · 0 reviews
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.