Assessing the neurophysiological effects of botulinum toxin treatment for adults with focal limb spasticity: a systematic review
Bibliographic record
Abstract
UNLABELLED: To examine effectiveness of botulinum toxin therapy (BoNT), accurate quantification of the neurological component of underlying spasticity is needed. PURPOSE: Systematically identify and summarize literature on neurophysiological methods to test the effectiveness of BoNT, discuss methodological aspects, and recommend best techniques for clinical and research settings. METHODS: Electronic databases were searched using specific keywords. Reviews and reference lists were hand-checked to find relevant studies. Studies in adults with focal limb spasticity were included. RESULTS: Over 300 articles were scanned for relevance, and a list of 90 relevant articles was made. After excluding reviews and studies in healthy controls or non-spasticity related BoNT, 22 articles were reviewed. Neurophysiological techniques included electromyography (EMG; n = 11), electrical nerve stimulation (n = 13), transcranial magnetic stimulation (TMS; n = 1), functional magnetic resonance imaging (fMRI; n = 1), reflex threshold (n = 4), Achilles tendon reflex (n = 1), reflex latency (n = 1). DISCUSSION: Maximum M-wave amplitude (M-max) and stretch reflex threshold techniques were successfully used to measure the neurophysiological effects of BoNT and have high test-retest reliability. EMG techniques require effective magnitude normalization techniques and establishing test-retest reliability. CONCLUSION: Serial electrical nerve stimulation can be incorporated into clinical practice to assist in follow-up. We recommend using M-max and stretch reflex threshold techniques to assess the neurophysiological effects.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".