Mindfulness-based body scan training in multimodal physiotherapy for vulvodynia – a randomized controlled feasibility study
Bibliographic record
Abstract
This randomized controlled trial explored the feasibility and preliminary effectiveness of integrating mindfulness-based body scan (MBBS) interventions into multimodal pelvic floor physiotherapy for vulvodynia treatment. Participants received ten sessions of standardized physiotherapy. The mindfulness intervention group was instructed to additionally perform home-based audio-file guided MBBS five times a week. The primary study endpoint was feasibility. The effect on pain intensity, pain characteristics and sexuality were assessed with Numeric Rating scales (NRS), the McGill-Melzack Pain Questionnaire (MPQ), the Female Sexual Distress Scale (FSDS) and Female Sexual Function Index (FSFI) and digital assessment of pelvic floor. Thirty-three women were randomized and completed the end-of-treatment assessments and 26 (79%) attended the follow-up. 15 of 17 participants of the intervention group (88%) performed the body scans more than ten times and the feasibility criteria were achieved. The intervention group showed significantly better improvements in NRS of average pain, MPQ subscales and FSDS total score. Pelvic floor assessment showed a significant improvement of myofascial pressure points over time with no difference between study groups. Integration of MBBS trainings into multimodal pelvic floor physiotherapy for vulvodynia is feasible and well accepted and may improve pain reduction and sexual function.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".