Perineal Massage Improves the Dyspareunia Caused by Tenderness of the Pelvic Floor Muscles
Bibliographic record
Abstract
ABSTRACT Aim: To evaluate the long-term effectiveness of perineal Thiele massage in the treatment of women with dyspareunia caused by tenderness of the pelvic floor muscles. Methods: A total of 18 women with diagnoses of dyspareunia caused by tenderness of the pelvic floor muscles were included in the study. The women were divided in two groups: the dyspareunia (D) group - 8 women with dyspareunia caused by tenderness of the pelvic floor muscles; and the chronic pelvic pain group (CPP) group - 10 women with dyspareunia caused by tenderness of the pelvic floor muscles associated with CPP. Each patient filled out the Visual Analogue Scale (VAS), the McGill Pain Index, the Female Sexual Function Index (FSFI) and the Hospital Anxiety and Depression Scale (HADS). After an evaluation, the women underwent transvaginal massage using the Thiele technique over a period of 5 minutes, once a week for 4 weeks. Results: All women had significant improvements in their dyspareunia according the VAS and the McGill Pain Index (p < 0,001), but the HADS scores did not show significant differences. Regarding sexual function, the D group showed improvements on all aspects of sexual function, while the CPP group showed differences only in the pain domain. Conclusion: Thiele massage is effective in the treatment of dyspareunia caused by tenderness of the pelvic floor muscles with a long-term pain relief.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".