Determining the optimal pelvic floor muscle training regimen for women with stress urinary incontinence
Bibliographic record
Abstract
Pelvic floor muscle (PFM) training has received Level-A evidence rating in the treatment of stress urinary incontinence (SUI) in women, based on meta-analysis of numerous randomized control trials (RCTs) and is recommended in many published guidelines. However, the actual regimen of PFM training used varies widely in these RCTs. Hence, to date, the optimal PFM training regimen for achieving continence remains unknown and the following questions persist: how often should women attend PFM training sessions and how many contractions should they perform for maximal effect? Is a regimen of strengthening exercises better than a motor control strategy or functional retraining? Is it better to administer a PFM training regimen to an individual or are group sessions equally effective, or better? Which is better, PFM training by itself or in combination with biofeedback, neuromuscular electrical stimulation, and/or vaginal cones? Should we use improvement or cure as the ultimate outcome to determine which regimen is the best? The questions are endless. As a starting point in our endeavour to identify optimal PFM training regimens, the aim of this study is (a) to review the present evidence in terms of the effectiveness of different PFM training regimens in women with SUI and (b) to discuss the current literature on PFM dysfunction in SUI women, including the up-to-date evidence on skeletal muscle training theory and other factors known to impact on women's participation in and adherence to PFM training.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".