Prevalence and Correlative Factors of Poststroke Urinary Incontinence
Bibliographic record
Abstract
Objective: To determine the prevalence and pattern changes of urinary incontinence in the first 3 months after stroke. The correlation between urinary incontinence and cognitive impairment, physical impairment and functional disability were also explored. Methods: One hundred acute stroke patients who had their first ever stroke without urinary incontinence were recruited. Canadian Neurological Scale, Barthel Index, Thai Mental State Examination, and Urogenital Distress Inventory Short Form were administered 3 times, within 7 days, at 1 and 3 months after stroke. Results: The prevalence of urinary incontinence within 7 days, at 1 month and 3 months after stroke were 34%, 22.1% and 17%, respectively. The incontinence pattern improved 18% at 1 month and 21 % at 3 months. The main type of incontinence found was urge incontinence. The initially incontinent group at 7 days was significantly more aphasic and dysphagic. Urinary incontinence correlated moderately with physical impairment and functional disability across all the three times of evaluations. There was no correlation between urinary incontinence and cognitive impairment. Conclusion: Urinary incontinence in acute stroke patients improves over time. Urge incontinence is the major problem in the incontinence group. The stroke patients with urinary incontinence have more physical impairments and disability than those with continence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".