Are hypopressive and other exercise programs effective for the treatment of pelvic organ prolapse?
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: Pelvic floor muscle training (PFMT) is effective for the treatment of pelvic organ prolapse (POP), but other exercise programs have also been promoted and used. The aim of this review was to evaluate the effect of hypopressive and other exercise programs besides PFMT for POP. METHODS: A literature search was conducted on Ovid Medline, EMBASE, CINAHL, Cochrane, PEDro, and Scopus databases from January 1996 to 30 December 2021. Only randomized controlled trials (RCTs) were included. The keywords were combinations of "pelvic organ prolapse" or "urogenital prolapse," and "exercise therapy," "hypopressive exercise," "Kegel," "pelvic floor muscle training," "pelvic floor muscle exercises," "Pilates," "treatment," "yoga," "Tai Chi." Methodological quality was assessed using the PEDro rating scale (0-10). RESULTS: Seven RCTs containing hypopressive exercise, yoga or breathing and hip muscle exercises in an inverted position were retrieved and analyzed. PEDro score ranged from 4 to 7. There was no additional effect of adding hypopressive exercise to PFMT, and PFMT was more effective than hypopressive exercise alone. The studies that included the term "yoga" included regular PFMT and thus can be classified as PFMT. Hip exercises in an inverted position added to PFMT vs PFMT alone showed better improvement in some secondary outcomes but not in the primary outcome, POP stage. CONCLUSIONS: There are few RCTs assessing the effects of other exercise programs besides PFMT in the treatment of POP. To date, there is no evidence that other exercise programs are more effective than PFMT for POP.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".