Treatment of adult spasticity with Botox (onabotulinumtoxinA): Development, insights, and impact
Bibliographic record
Abstract
Upper and lower limb spasticity (ULS, LLS) often occur following a stroke or in patients with other neurological disorders, leading to difficulties in mobility and daily living and decreased quality of life. Prior to the use of onabotulinumtoxinA, antispastic medications had limited efficacy and often caused sedation. Phenol injections were difficult for physicians to perform, painful, and led to tissue destruction. The success of onabotulinumtoxinA in treating cervical dystonia led to its use in spasticity. However, many challenges characterized the development of onabotulinumtoxinA for adult spasticity. The wide variability in the presentation of spasticity among patients rendered it difficult to determine which muscles to inject and how to measure improvement. Another challenge was the initial refusal of the Food and Drug Administration to accept the Ashworth Scale as a primary endpoint. Additional scales were designed to incorporate a goal-oriented, patient-centered approach that also accounted for the variability of spasticity presentations. Several randomized, double-blind, placebo-controlled trials of post-stroke spasticity of the elbow, wrist, and/or fingers showed significantly greater improvements in the modified Ashworth Scale and patient treatment goals and led to the approval of onabotulinumtoxinA for the treatment of ULS in adult patients. Lessons learned from the successful ULS trials were applied to design an LLS trial that led to approval for the latter indication. Additional observational trials mimicking real-world treatment have shown continued effectiveness and patient satisfaction. The use of onabotulinumtoxinA for spasticity has ushered in a more patient-centered treatment approach that has vastly improved patients' quality of life.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".