Treatment Patterns for Patients on Overactive Bladder Therapy: A Retrospective Statistical Analysis Using Canadian Claims Data
Bibliographic record
Abstract
Background: Overactive bladder (OAB) is a chronic condition which may be associated with a significant negative impact on quality of life. Antimuscarinic drugs are currently the mainstay of medical therapy, but persistence and adherence are generally poor. Treatment switching may be considered in order to maximise benefits from pharmacological therapy, but there are relatively few data on OAB therapy switching to second and third-lines of medication. There are also few formal analyses on the impact of age, gender and choice of initial OAB drug on discontinuation rates. Objectives: To investigate discontinuation rates with antimuscarinics in patients newly starting OAB therapy, with regard to patterns of switching to alternative medication, and the potential impact of age, gender and choice of initial drug. Methods: Data on prescription drug use in Canada were retrieved from the IMS Brogan public and private prescription claims databases. Medication usage was tracked for four years following an index claim. The primary endpoint was the number of days from index claim to discontinuation of medication. Secondary endpoints were the number of days on first-line therapy before switching. Descriptive results were evaluated using univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) models. Results: Data were available for 31,754 patients. Approximately 91% discontinued OAB medication within the four-year follow-up period. The discontinuation rate was similar between men and women. The risk of discontinuation in patients ≥75 years was only slightly higher than that in patients aged 40−64 years (hazard ratio of 1.08) and was lower than in those aged 65−74 years. Retention when oxybutynin was the initial drug was lower than with most of the other antimuscarinics. Only 12.5% of patients changed OAB medication during the 4-year period. Women were more likely than men to switch from first-line or second-line treatment. Conclusions: Discontinuation of initial antimuscarinic therapy was high. Compared with oxybutynin, several alternative antimuscarinics offered lower risks of discontinuation. The majority of patients had no trial of second-line treatment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".