Botulinum Toxin Combined with Robot-Assisted Therapy for Post-Stroke Spasticity: A Systematic Review
Bibliographic record
Abstract
(1) Background: Post-stroke spasticity limits motor recovery and independence. Combining botulinum toxin type-A (BoNT-A) injection with intensive, task-specific robot-assisted therapy (RAT) might enhance neuroplasticity and functional gains, but its additive effect and optimal timing are uncertain. (2) Methods: We systematically searched major medical databases and trial registries up to April 2025 for randomized controlled trials in adults with post-stroke spasticity comparing botulinum toxin type-A injection plus RAT with toxin injection plus conventional therapy, or RAT alone with RAT combined with toxin injection. Risk of bias was assessed using the RoB 2 tool, and findings were synthesized narratively. (3) Results: Seven trials (n = 229) were included. Across all studies, toxin treatment reduced spasticity within groups, whereas additional spasticity reduction with RAT versus conventional rehabilitation was inconsistent. In contrast, several lower-limb trials reported greater improvements in walking capacity and balance when RAT was added, while upper-limb trials showed comparable motor recovery across treatment arms with occasional advantages in strength and movement quality. A pilot four-arm study suggested that starting RAT around four weeks after injection may maximize upper-limb motor gains. (4) Conclusions: The combination of BoNT-A with RAT appears safe and is particularly promising for gait rehabilitation, but further research is needed to define optimal timing and protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".