Medical conditions, medications, and urinary incontinence. Analysis of a population-based survey.
Bibliographic record
Abstract
OBJECTIVE: To assess associations between various medical conditions and drug treatments and reports of urinary incontinence. DESIGN: Secondary analysis of responses to the second wave of the National Population Health Survey (NPHS). Odds ratios were calculated using survey-weighted multiple logistic regression; confidence intervals were calculated using bootstrap methods. SETTING: Canadian households in all 10 provinces, as assessed by Statistics Canada's NPHS. PARTICIPANTS: From among respondents to the NPHS, the 54,920 people aged 30 years or older. MAIN OUTCOME MEASURES: Responses to the question "Do you have urinary incontinence diagnosed by a health professional?" and analysis of variables related to medical conditions and medications. RESULTS: Urinary incontinence was associated with strokes, arthritis, and back problems in both sexes. Odds ratios for incontinence were elevated among men and women who reported having asthma. Narcotics and diuretics were strongly associated with incontinence in both sexes. Psychoactive medications were associated with incontinence in women; antidepressants were associated with incontinence in men. CONCLUSION: Physicians should consider the possibility that patients with common conditions, such as arthritis, back problems, or respiratory conditions associated with coughing, might also have urinary incontinence. Physicians should also be aware that urinary incontinence might be a side effect of therapies and make relevant inquiries. Medications associated with incontinence could be changed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".