Prevalence of commonly prescribed medications potentially contributing to urinary symptoms in a cohort of older patients seeking care for incontinence
Bibliographic record
Abstract
BACKGROUND: Several medication classes may contribute to urinary symptoms in older adults. The purpose of this study was to determine the prevalence of use of these medications in a clinical cohort of incontinent patients. METHODS: A cross-sectional study was conducted among 390 new patients aged 60 years and older seeking care for incontinence in specialized outpatient geriatric incontinence clinics in Quebec, Canada. The use of oral estrogens, alpha-blocking agents, benzodiazepines, antidepressants, antipsychotics, ACE inhibitors, loop diuretics, NSAIDs, narcotics and calcium channel blockers was recorded from each patient's medication profile. Lower urinary tract symptoms and the severity of incontinence were measured using standardized questionnaires including the International Consultation on Incontinence Questionnaire. The type of incontinence was determined clinically by a physician specialized in incontinence. Co-morbidities were ascertained by self-report. Logistic regression analyses were used to detect factors associated with medication use, as well as relationships between specific medication classes and the type and severity of urinary symptoms. RESULTS: The prevalence of medications potentially contributing to lower urinary tract symptoms was 60.5%. Calcium channel blockers (21.8%), benzodiazepines (17.4%), other centrally active agents (16.4%), ACE inhibitors (14.4%) and estrogens (12.8%) were most frequently consumed. Only polypharmacy (OR = 4.9, 95% CI = 3.1-7.9), was associated with medication use contributing to incontinence in analyses adjusted for age, sex, and multimorbidity. No associations were detected between specific medication classes and the type or severity of urinary symptoms in this cohort. CONCLUSION: The prevalence of use of medications potentially causing urinary symptoms is high among incontinent older adults. More research is needed to determine whether de-prescribing these medications results in improved urinary symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".