P.099 Spasticity treatment patterns in long-term care using Ontario real-world evidence
Bibliographic record
Abstract
Background: Focal spasticity affects up to 1 in 3 residents in long-term care (LTC), with potentially disabling consequences. Data are limited on access to care for patients requiring botulinum toxin (BoNT) treatment in LTC. Methods: This retrospective, observational, real-world study was conducted using the Ontario Drug Benefit claims database. Patients with ≥1 medical claim for BoNT for focal spasticity treatment were selected, and those residing in LTC were further identified. Data were analyzed for the utilization (2000–2019), treatment rate, and time-to-treatment with BoNT in LTC residents (2015–2019). Results: Over a 10-year period, the number of patients receiving BoNT for spasticity increased 7-fold and the proportion of patients residing in LTC versus community increased from 43% (2010) to 52% (2019). Of the LTC residents eligible for BoNT treatment, 33% received BoNT in 2015 compared with 63% in 2019. Injections/patient/year increased from 1.9 (2010) to 3.1 (2017). Following LTC admission, median time to first injection was 2.9 years. Conclusions: In this study, approximately 40% of eligible LTC residents in Ontario were not receiving BoNT treatment, and of those who were, median time to first injection was 2.9 years. Future policy considerations should prioritize uniform access to spasticity standards of care for LTC residents.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".