Management of urinary incontinence in females by primary care providers: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To describe primary care provider (PCP) practices for the assessment and management of females with urinary incontinence (UI), and appraise these practices relative to recommendations made in high-quality clinical guidelines. METHODS: Studies were searched in four databases (MEDLINE, EMBASE, CINAHL, Web of Science) from their respective inception dates to 6 March 2023. All studies describing UI evaluation and management practices used by PCPs for female patients were eligible. Two reviewers independently selected studies assessed their quality and extracted data. A narrative synthesis of included studies was performed to describe practices. Relevant evaluation and management practices were then compared to recommendations that were consistent across current high-quality UI guidelines. Pharmacotherapy, referrals, and follow-ups were reported descriptively only. RESULTS: A total of 3475 articles were retrieved and, among those, 31 were included in the review. The majority reported a poor-moderate adherence to performing a pelvic examination (reported adherence range: 23-76%; based on eight studies), abdominal examination (0-87%; three studies), pelvic floor muscle assessment (9-36%; two studies), and bladder diary (0-92%; nine studies), while there was high adherence to urine analysis (40-97%; nine studies). For the conservative management of UI, studies revealed a poor-moderate adherence to recommendations for pelvic floor muscle training (5-82%; nine studies), bladder training (2-53%; eight studies) and lifestyle interventions (1-71%; six studies). Regarding pharmacotherapy, PCPs predominantly prescribed antimuscarinics (2-46%; nine studies) and oestrogen (2-77%; seven studies). Lastly, PCPs referred those reporting UI to medical specialists (5-37%; 14 studies). Referrals were generally made <30 days after diagnosis with urologists being the most sought out professional to assess and treat UI. CONCLUSION: This review revealed poor-moderate adherence to clinical practice guideline recommendations. While these findings reflect high variability in reporting, the key message is that most aspects of patient care for female UI provided by PCPs needs to improve.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".