Post-Stroke Spasticity Treatment: A Retrospective Cohort Study From Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Understanding post-stroke spasticity (PSS) treatment in everyday clinical practice may guide improvements in patient care. METHODS: This was a retrospective cohort study that used population-level administrative data. Adults (aged ≥18 years) who initiated PSS treatment (defined by the first PSS clinic visit, focal botulinum toxin injection, or anti-spasticity medication dispensation [baclofen, dantrolene and tizanidine] with none of these treatments occurring during the 2 years before the stroke) were identified between 2012 and 2019 in Alberta, Canada. Spasticity treatment use, time to treatment start and type of prescribing/treating physician were measured. Descriptive statistics were performed. RESULTS: Within the cohort (n = 1,079), the most common PSS treatment was oral baclofen (initial treatment: 60.9%; received on/after the initial treatment date up to March 31, 2020: 69.0%), largely prescribed by primary care physicians (77.6%) and started a median of 348 (IQR 741) days after the stroke. Focal botulinum toxin (23.3%; 37.7%) was largely prescribed by physiatrists (72.2%) and started 311 (IQR 446) days after the stroke; spasticity clinic visits (18.6%; 23.8%) were also common. CONCLUSIONS: We found evidence of gaps in provision of spasticity management in persons with PSS including overuse of systemic oral baclofen (that has common adverse side effects and lacks evidence of effectiveness in PSS) and potential underuse of focal botulinum toxin injections. Further investigation and strategies should be pursued to improve alignment of PSS treatment with guideline recommendations that in turn will support better outcomes for those with PSS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".