The management of mixed urinary incontinence in women
Bibliographic record
Abstract
Mixed urinary incontinence is a common diagnosis among women with urinary leakage and is often present in women who are unable to characterize their incontinence. Research and optimized clinical treatment of these patients is limited by the challenges in objectively defining and stratifying this population. The evaluation of these patients should follow the same general principles as any assessment of any women with incontinence; however, it is essential to define whether urge or stress incontinence is the predominant symptom. Urodynamics (UDS) may be helpful in this regard and may help predict surgical outcomes. Behavioural therapy, weight loss, and pelvic floor muscle therapy are usually appropriate initial management strategies. In postmenopausal women, vaginal estrogen can be considered, and in women with equal parts stress and urge incontinence or urge-predominant mixed incontinence, a trial of anticholinergics or beta-3 agonists is appropriate. In women with stress-predominant or equal parts stress and urge incontinence, stress incontinence surgery can be considered, with the caveat that outcomes are generally worse among women with more severe levels of urgency, success rates may not be as durable, and a significant proportion of women may need additional medical therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".