Question · asked by 1 review
BoNT‐A vs usual care or physiotherapy: Adverse events
- Population
- children with CP
- Intervention
- BoNT‐A
- Comparison
- usual care or physiotherapy
- Outcome
- Adverse events
The review found no eligible studies for this question.
What each review found
Current versions onlyNo studies found 2 · 66.7%GRADE not reported 0 · 0%Very low 1 · 33.3%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
BoNT‐A vs usual care or physiotherapy Adverse events · children with CP The review found no eligible studies for this question. Botulinum toxin type A in the treatment of lower limb spasticity in children with cerebral palsy (2019) · Cochrane Headline outcomes only | no estimate extracted | Not reported | No studies found |
BoNT‐A vs usual care or physiotherapy Adverse events · children with CP The review found no eligible studies for this question. Botulinum toxin type A in the treatment of lower limb spasticity in children with cerebral palsy (2019) · Cochrane Headline outcomes only | no estimate extracted | Not reported | No studies found |
BoNT‐A vs usual care or physiotherapy Adverse events · children with CP Botulinum toxin type A in the treatment of lower limb spasticity in children with cerebral palsy (2019) · Cochrane Headline outcomes only | no estimate extracted | 5 studies · 206 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Botulinum toxin type A in the treatment of lower limb spasticity in children with cerebral palsy 2019 · 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.
- BoNT‐A vs sham · children with CP · 1 review
- BoNT‐A vs serial casting · children with CP · 1 review
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.