Effects of botulinum toxin and functional electrical stimulation in individuals with spasticity: a systematic review
Bibliographic record
Abstract
Background/Aims Botulinum toxin is a globally recognised method for managing spasticity in people with cerebral palsy, stroke and spinal cord injury. Despite its efficacy, enhancing functionality in these conditions requires complementary therapeutic strategies. Functional electrical stimulation is widely used to support mobility by providing targeted electrical stimulation during functional motor tasks. The aim of this systematic review was to investigate the impact of combining Botulinum toxin A and functional electrical stimulation on functionality in individuals with spasticity. Methods The search, spanning from inception to July 2023, was conducted across PubMed, ScienceDirect, Scopus, and the Cochrane Library. The Downs and Black Checklist was used to assess the methodological quality of the studies. Results The search yielded seven articles out of 605 studies. The included studies exhibited heterogeneity in intervention parameters and, on average, demonstrated fair methodological quality. Conclusions The synthesis indicates that applying functional electrical stimulation after Botulinum Toxin A seems to improve functionality and muscle tone control in upper and lower limbs for conditions such as cerebral palsy, stroke and spinal cord injury. However, uncertainties persist regarding optimal intervention parameters, including the time interval between interventions and treatment frequency and volume. Implications for practice The findings of this review suggest that functional electrical stimulation, when used as an adjuvant therapy to BoNT-A, is effective in achieving clinical rehabilitation goals. These goals include reducing muscle tone in adults, improving gait spatiotemporal parameters in both children and adults, and enhancing manual dexterity in daily activities in adults.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".