Adverse Events for Overactive Bladder Medications From a Public Federal Database
Bibliographic record
Abstract
IMPORTANCE: Clinical data on the use of overactive bladder (OAB) medications are limited by the physician interpretation of adverse effects rather than those that are patient reported. OBJECTIVE: The aim of the study was to evaluate the association between OAB medications and adverse drug events (ADEs) through the self-reporting U.S. Food and Drug Administration Adverse Event Report System database. STUDY DESIGN: The U.S. Food and Drug Administration Adverse Event Report System (FAERS) database was queried from 2004 to 2019. Adverse drug events were recategorized. Disproportionality analysis was used to detect the risk signals for each OAB medication and ADEs. χ 2 values were calculated to assess the association between ADEs and dosage. RESULTS: A total number of 14,102 reports were identified. The most frequently reported OAB medications were mirabegron (35%), transdermal oxybutynin (27%), and solifenacin (25%). Neuropsychiatric (NP) ADEs were highest with tolterodine and fesoterodine usage (16% and 15.6%, respectively) and transdermal oxybutynin had the lowest (6.5%). Increasing the dose of tolterodine or fesoterodine was not associated with increased NP ADEs. Oxybutynin had the highest risk of affect/mood disorder, agitation, and balance/movement disorder; however, it had the lowest risk of headache/migraine compared with all OAB medications. Mirabegron compared with all other OAB medications had the lowest risk of affect/mood disorder and agitation; however, it had the highest risk of headache and migraines. CONCLUSIONS: The FAERS database not only is a repository of ADEs but also may represent evolving prescribing habits for OAB medications. Transdermal oxybutynin had the lowest NP ADEs and may be appropriate for selected individuals.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".