Evidence to Practice: Botulinum Toxin in the Treatment of Spasticity Post Stroke
Bibliographic record
Abstract
INTRODUCTION: Spasticity is a significant problem following stroke. Although there is extensive research examining the efficacy of botulinum toxin as a treatment, there are challenges in implementing its use. METHODS: The results from previously published randomized controlled trials and systematic reviews examining the use of botulinum toxin as a treatment for poststroke spasticity of the upper and lower limb and the shoulder are summarized. Several barriers to implementation are discussed. RESULTS: There is strong evidence that denervation of muscles, in the lower extremity and upper extremity post stroke, with botulinum toxin reduces focal spasticity. There is also strong evidence that it is associated with a small but significant improvement in gait velocity based on a recent meta-analysis. However, evidence that botulinum toxin injections are associated with improved function and improved quality of life is not as compelling. There is evidence that botulinum toxin injected into the subscapularis muscle can reduce spastic shoulder pain and improve passive range of motion of the hemiplegic shoulder. There are a number of challenges with botulinum toxin, including uncertainty over its role in improving motor dysfunction following stroke, the determination of which subsets of patients may benefit, the cost of treatment, and the identification of meaningful outcome measures. CONCLUSIONS: Botulinum toxin has been shown to be an effective treatment in reducing tone and managing spasticity post stroke. However, its effectiveness in improving function has been more controversial.
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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 | 0.021 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".