Canadian Physicians’ Use of Intramuscular Botulinum Toxin Injections for Shoulder Spasticity: A National Cross-Sectional Survey
Bibliographic record
Abstract
Spasticity of the upper extremity can result in severe pain, along with many complications that can impair a patient's activities of daily living. Failure to treat patients with spasticity of the upper limb can result in a decrease in the range of motion of joints and contracture development, leading to further restriction in daily activities. We aimed to investigate the practice patterns of Canadian physicians who utilize Botulinum toxin type-A (BoNT-A) injections in the management of shoulder spasticity. 50 Canadian Physical Medicine and Rehabilitation (PM&R) physicians completed a survey with an estimated completion rate of (36.23%). The demographics of the survey participants came from a variety of provinces, clinical settings, and patient populations. The most common muscle injected for shoulder adduction and internal rotation spasticity was the pectoralis major, this was followed by latissimus dorsi, pectoralis minor, subscapularis and teres major. Injection of BoNT-A for problematic post-stroke shoulder spasticity was common, with (81.48%) of participants responding that it was always or often used in their management of post-stroke spasticity (PSS). Dosing of BoNT-A demonstrated variability for the muscle injected as well as the type of toxin used. The goals of the patients, caregivers, and practitioners were used to help guide the management of these patients. As a result, the practice patterns of Canadian physicians who treat shoulder spasticity are varied, due to numerous patient factors. Future studies are needed to analyze optimal treatment patterns, and the development of algorithms to standardize care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".