Anticholinergics for overactive bladder: Temporal trends in prescription and treatment persistence
Bibliographic record
Abstract
INTRODUCTION: We sought to understand the contemporary pharmacologic management of overactive bladder (OAB) in a single-payer system. We examined temporal trends in the use of anticholinergic medications and assessed whether the likelihood of patients changing their anticholinergic therapy was predicted by their current therapy. METHODS: We conducted a retrospective, population-based analysis of prescription records from the PharmaNet database in BC, Canada. We identified patients treated with one or more anticholinergic prescriptions between 2001 and 2009. We characterized temporal trends in the use of anticholinergic medications. We used generalized estimating equations with a logit wing to assess the relationship between the type of anticholinergic medication and the change in prescription. RESULTS: The 114 325 included patients filled 1 140 296 anti-cholinergic prescriptions. The number of prescriptions each year increased over the study, both in aggregate and for each individual medication. While oxybutynin was the most commonly prescribed medication (68% of all prescriptions), the proportion of newer anticholinergics (solifenacin, darifenacin, and trospium) prescribed increased over time (p<0.0001). Patients taking tolterodine (odds ratio [OR] 1.03; p=0.01) and darifenacin (OR 1.12; p=0.0006) were significantly more likely to change their prescription than those taking oxybutynin. There was no association seen for patients taking solifenacin (p=0.6) and trospium (p=0.9). CONCLUSIONS: There are an increasing number of anticholinergic prescriptions being filled annually. Patients taking newer anticholinergics are at least as likely to change therapy as those taking oxybutynin. The reimbursement environment in BC likely affects these results. Restrictions in the available data limit assessment of other relevant predictors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".