Publicly funded overactive bladder drug treatment patterns in Ontario over 15 years: An ecological study
Bibliographic record
Abstract
INTRODUCTION: Medication is an important option for patients with overactive bladder (OAB), with four different drugs approved over the last 10 years, including the first non-anticholinergic treatment, mirabegron. We set out to describe the number and rate of users of medication for the management of OAB over the last 15 years among residents of Ontario, Canada covered by the public drug programs. METHODS: We conducted a population-based, repeated cross-sectional study examining quarterly publically funded prescription claims for OAB medications from January 2000 to June 2016 in Ontario, Canada. RESULTS: We report two major changes in prescription patterns for OAB. The first was the rise of newer, more selective anticholinergics (tolterodine, solifenacin, and darifenacin) replacing oxybutynin. This led to a 54.8% reduction in the rate of users of oxybutynin over the study period from 10.4 users/1000 beneficiaries in 2000 to 4.7 users/1000 beneficiaries in 2016. Recently, we saw the emergence of mirabegron as the most commonly prescribed treatment for OAB. By the final quarter of the observation period, mirabegron was the most commonly used OAB treatment with 25.0% (n=19 411) of all OAB medication users in Ontario (n=77 660). CONCLUSIONS: Our findings highlight the rapid uptake of novel agents and a major shift in the treatment of OAB over the last three years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".