A pooled analysis of the efficacy of fesoterodine for the treatment of overactive bladder, and the relationship between safety, co-morbidity and polypharmacy in patients aged 65 years or older
Bibliographic record
Abstract
Background: overactive bladder (OAB) is a common condition in older persons. Antimuscarinic treatment remains the mainstay of treatment of OAB but clinicians have been reluctant to prescribe this to older patients. This study examined efficacy and safety information from patients >65 in fesoterodine trials to reaffirm efficacy and to explore the relationships between treatment emergent adverse events (TEAEs), coexisting medication and co-morbidity. Methods: data from 10 double-blind, placebo-controlled studies were analysed. A logistic regression analysis, where TEAE incidence was predicted by treatment, prior antimuscarinic treatment, number of coexisting medications, number of concomitant diseases and all possible combinations of two-way interaction terms with treatment was conducted. Results: of 4,040 patients who participated in trials; fesoterodine treatment was associated with statistically significant reductions in all disease-related and patient-reported outcomes compared to placebo. There was a significant increase in the likelihood of reporting a TEAE in association with the number of coexistent medications (odds ratio (OR) = 1.028, 95% CI: 1.0143-1.044, P < 0.003). The OR of having a TEAE with increase in the number of concomitant diseases was 1.058 (95% CI: 1.044-1.072, P < 0.0001). Central nervous system (CNS) events were few. Discussion: fesoterodine treatment led to clinically meaningful improvements across all included patient reported outcomes. The number of concomitant conditions had the greatest influence on the likelihood of an adverse event being reported. CNS TEAE were not associated with fesoterodine dose and were low across all categories of concomitant disease and coexisting medication.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.026 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".