Thiele massage as a therapeutic option for women with chronic pelvic pain caused by tenderness of pelvic floor muscles
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Musculoskeletal system has been found to be involved in genesis and perpetuation of chronic pelvic pain (CPP) and has strong evidences that up to 80% of women with CPP present dysfunction of the musculoskeletal system. In this study, we report a series of women with CPP caused by tenderness of pelvic floor muscles successfully treated with Thiele massage. METHODS: Were included in this study six women with CPP caused by tenderness of the levator ani muscle that underwent transvaginal massage using the Thiele technique, over a period of 5 minutes repeated once a week for 4 weeks. After 1 month, the women returned for follow-up. RESULTS: The median tenderness score for the six women evaluated was 3 at the first evaluation and 0 after 1 month of follow-up (P < 0.01). The mean Visual Analogue Scale and McGill Pain Index scores were 8.1 and 34, respectively, at the first evaluation, and 1.5 and 16.6 at follow-up (P < 0.01). CONCLUSION: Thiele massage appears to be very helpful for women with CPP caused by tenderness of the levator ani muscle. However, these results are preliminary and a larger number of women are necessary to obtain more conclusive results.
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 | 0.000 | 0.001 |
| 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".