Postoperative Physiotherapy in Women Undergoing Pelvic Floor Reconstructive Surgery: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
Purpose: Postoperative physiotherapy in conjunction with pelvic organ prolapse (POP) surgery is still under-investigated and controversial. In this randomized controlled trial, pelvic floor muscle training (PFMT) and abdominal training were compared with a control condition (standard in-hospital treatment). Method: Eighty-one women were randomized to one of three groups. The Prolapse Quality of Life questionnaire, two-dimensional ultrasound, Pelvic Organ Prolapse Quantification System scale, the PERFECT (power, endurance, repetitions, fast contractions, every contraction timed) scheme, electromyography, Sahrmann scale, and pressure biofeedback unit (PBU) were used to measure quality of life (QOL), POP, and pelvic floor and abdominal muscle function. A mixed-model analysis of variance and the Kruskal–Wallis test was used for analysis. Results: Beneficial effects ( p < 0.05) were found for the PFMT group – increased power, number of fast contractions, amount of movement, endurance, and Sahrmann and PBU measures – compared with the control group. Abdominal training led to a significant ( p < 0.05) increase in bulging and discomfort, number of pelvic floor muscle contractions, and Sahrmann and PBU measures compared with the control condition; both groups showed significantly increased urinary frequency ( p < 0.05). Conclusions: Postoperative physiotherapy did not have a beneficial effect on QOL or POP symptoms. PFMT and abdominal training had beneficial effects on pelvic floor muscle function and abdominal muscle measures. Additional abdominal training led to increased symptoms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".