The status of pelvic floor muscle training for women
Bibliographic record
Abstract
There is no consensus on the amount of exercise necessary toimprove pelvic floor muscle (PFM) function. We reviewed thepathophysiology of PFM dysfunction and the evolution of PFMtraining regimens since Kegel introduced the concept of pelvic floorawareness and the benefits of strength. This paper also describesthe similarities and differences between PFM and other musculargroups, reviews the physiology of muscle contraction and principlesof muscle fitness and exercise benefits and presents the rangeof protocols designed to strengthen the PFM and improve function.We also discuss the potential application of new technologyand methodologies. The design of PFM training logically requiresmultiple factors to be considered in each patient. Research thatdefines measures to objectively quantify the degree of dysfunctionand the efficacy of training would be beneficial. The applicationof new technologies may help this process.Il n’existe aucun consensus concernant la quantité d’exercicerequis pour améliorer la fonction du muscle du plancher pelvien(MPP). Nous avons examiné la physiopathologie d’un mauvaisfonctionnement du MPP et l’évolution des plans d’entraînement dece muscle depuis que Kegel a introduit le concept de la prise deconscience du MPP et les avantages de son renforcement. L’articledécrit également les similitudes et les différences entre le MPP etd’autres groupes musculaires, passe en revue la physiologie descontractions musculaires et les principes de bon fonctionnementmusculaire et les avantages liés à l’exercice. Nous présentons aussidivers protocoles visant à renforcer le MPP et à en améliorer lefonctionnement, ainsi que l’application potentielle de nouvellestechnologies et méthodologies. Le plan d’entraînement du MPPnécessite en toute logique la prise en compte de multiples facteursselon les patients. Des études cherchant à définir les mesures àutiliser pour quantifier de manière objective le niveau de dysfonctionet l’efficacité de l’entraînement seraient utiles. L’application denouvelles technologies pourrait contribuer à cet objectif.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".