Bibliographic record
Abstract
Urinary incontinence, the complaint of any involuntary loss of urine, is a troubling symptom experienced by men and women of all ages. Options for treatment include a range of behavioral, pharmacologic, and surgical therapies. Behavioral therapies, such as dietary modification, pelvic floor muscle training, and bladder training, are noninvasive, with little risk of side effects, and experts agree they should represent the first line of treatment whenever possible. These therapies can be initiated and monitored at the primary care level, thereby enhancing the accessibility of care for those affected. The purpose of this article is to methodically review what is and is not known about behavioral therapies, with attention to research needs. Although there is clear evidence for pelvic floor muscle training in women with urinary incontinence and modest evidence in men for a short time after radical prostatectomy, less is known about bladder training, prompted voiding, habit retraining, and timed voiding. Additional research is required to enhance our understanding of the comparative efficacy of behavioral interventions in specific populations. This research must take an increasingly long-term focus, given the potentially chronic nature of urinary incontinence.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".