Urinary incontinence. Non-surgical management by family physicians.
Bibliographic record
Abstract
OBJECTIVE: To review current evidence on conservative management of urinary incontinence (UI) by family physicians. QUALITY OF EVIDENCE: Articles were sought through MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, CINAHL, PsycLit, ERIC, two consensus meetings, and review of abstracts presented at urology meetings. References of these articles were searched for relevant trials. Strong evidence supports bladder training, pelvic floor exercises, and some medications, but only fair evidence supports fluid adjustment, caffeine reduction, and stopping smoking. Weight loss and exercise are supported by expert opinion only. Consensus opinion is that, whenever possible, conservative management should be considered first. MAIN MESSAGE: Good evidence shows that initial management by primary care physicians is effective. After basic assessment and tests, strategies such as bladder retraining, pelvic floor exercises, and lifestyle modifications, augmented by appropriate medications, can be successful. If initial strategies are unsuccessful, patients can be referred. CONCLUSION: More than a million Canadians suffer from UI. In almost all cases, family physicians are the first health professionals contacted by patients. Basic assessment and conservative management can go far to ameliorate the problem.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".