Systematic review of adjunct therapies to improve outcomes following botulinum toxin injection for treatment of limb spasticity
Bibliographic record
Abstract
OBJECTIVE: To determine the quality of evidence from randomized controlled trials on the efficacy of adjunct therapies following botulinum toxin injections for limb spasticity. DATA SOURCES: MEDLINE, EMBASE, CINAHL, and Cochrane Central Register of Controlled Trials electronic databases were searched for English language human studies from 1980 to 21 May 2015. STUDY SELECTION: Randomized controlled trials assessing adjunct therapies postbotulinum toxin injection for treatment of spasticity were included. Of the 268 studies screened, 17 met selection criteria. DATA EXTRACTION: Two reviewers independently assessed risk of bias using the Physiotherapy Evidence Database (PEDro) scale and graded according to Sackett's levels of evidence. DATA SYNTHESIS: Ten adjunct therapies were identified. Evidence suggests that adjunct use of electrical stimulation, modified constraint-induced movement therapy, physiotherapy (all Level 1), casting and dynamic splinting (both Level 2) result in improved Modified Ashworth Scale scores by at least 1 grade. There is Level 1 and 2 evidence that adjunct taping, segmental muscle vibration, cyclic functional electrical stimulation, and motorized arm ergometer may not improve outcomes compared with botulinum toxin injections alone. There is Level 1 evidence that casting is better than taping, taping is better than electrical stimulation and stretching, and extracorporeal shock wave therapy is better than electrical stimulation for outcomes including the Modified Ashworth Scale, range of motion and gait. All results are based on single studies. CONCLUSION: There is high level evidence to suggest that adjunct therapies may improve outcomes following botulinum toxin injection. No results have been confirmed by independent replication. All interventions would benefit from further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".