Optimal Muscle Selection for OnabotulinumtoxinA Injections in Poststroke Lower-Limb Spasticity
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to identify optimal muscle selection patterns for onabotulinumtoxinA treatment of poststroke lower-limb spasticity. DESIGN: Adults with poststroke lower-limb spasticity (ankle Modified Ashworth Scale ≥3) were randomized to onabotulinumtoxinA (300 U, mandatory ankle plantar flexors; ≤100 U, optional lower-limb muscles) or placebo. Post hoc analysis assessed the impact of muscle selection patterns on ankle Modified Ashworth Scale and physician-assessed Clinical Global Impression of Change based on change from baseline to average of weeks 4/6 versus placebo. RESULTS: Among 468 patients randomized, onabotulinumtoxinA improved ankle Modified Ashworth Scale (-0.81 vs -0.61, P = 0.01) and Clinical Global Impression of Change (0.86 vs 0.65, P = 0.012) versus placebo. Injection of mandatory muscles alone was not sufficient in improving ankle Modified Ashworth Scale (P = 0.255) or Clinical Global Impression of Change (P = 0.576) versus placebo but was adequate 24 mos or less after stroke (Modified Ashworth Scale, -1.13 vs -0.62, P = 0.019; Clinical Global Impression of Change, 1.24 vs 0.68, P = 0.006). Additional injections into toe muscles (flexor digitorum longus, flexor hallucis longus) improved ankle Modified Ashworth Scale (-0.98 vs -0.52, P = 0.002) and Clinical Global Impression of Change (0.80 vs 0.38, P = 0.023) versus placebo regardless of time since stroke. OnabotulinumtoxinA was well tolerated, with no new safety findings. CONCLUSIONS: Post hoc analyses suggested additional injections of onabotulinumtoxinA into toe flexors improved ankle Modified Ashworth Scale and Clinical Global Impression of Change scores versus mandatory muscles alone overall and with treatment initiation more than 24 mos after stroke.
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".