Improvements following multimodal pelvic floor physical therapy in gynecological cancer survivors suffering from pain during sexual intercourse: Results from a one-year follow-up mixed-method study
Bibliographic record
Abstract
BACKGROUND: A large proportion of gynecological cancer survivors suffer from pain during sexual intercourse, also known as dyspareunia. Following a multimodal pelvic floor physical therapy (PFPT) treatment, a reduction in pain and improvement in psychosexual outcomes were found in the short term, but no study thus far has examined whether these changes are sustained over time. PURPOSE: To examine the improvements in pain, sexual functioning, sexual distress, body image concerns, pain anxiety, pain catastrophizing, painful intercourse self-efficacy, depressive symptoms and pelvic floor disorder symptoms in gynecological cancer survivors with dyspareunia after PFPT, and to explore women's perceptions of treatment effects at one-year follow-up. METHODS: This mixed-method study included 31 gynecological cancer survivors affected by dyspareunia. The women completed a 12-week PFPT treatment comprising education, manual therapy and pelvic floor muscle exercises. Quantitative data were collected using validated questionnaires at baseline, post-treatment and one-year follow-up. As for qualitative data, semi-structured interviews were conducted at one-year follow-up to better understand women's perception and experience of treatment effects. RESULTS: Significant improvements were found from baseline to one-year follow-up on all quantitative outcomes (P ≤ 0.028). Moreover, no changes were found from post-treatment to one-year follow-up, supporting that the improvements were sustained at follow-up. Qualitative data highlighted that reduction in pain, improvement in sexual functioning and reduction in urinary symptoms were the most meaningful effects perceived by participants. Women expressed that these effects resulted from positive biological, psychological and social changes attributable to multimodal PFPT. Adherence was also perceived to influence treatment outcomes. CONCLUSIONS: Findings suggest that the short-term improvements following multimodal PFPT are sustained and meaningful for gynecological cancer survivors with dyspareunia one year after treatment.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".