Pain Characteristics, Fear-avoidance Variables, and Pelvic Floor Function as Predictors of Treatment Response to Physical Therapy in Women With Provoked Vestibulodynia
Bibliographic record
Abstract
OBJECTIVE: The aim was to investigate whether pretreatment pain characteristics, psychological variables, and pelvic floor muscle (PFM) function predict the response to physical therapy (PT) in women with provoked vestibulodynia (PVD). MATERIALS AND METHODS: One hundred-five women diagnosed with PVD underwent 10 weekly sessions of individual PT comprising education, PFM exercises with biofeedback, manual therapy, and dilators. Treatment outcomes were evaluated at pretreatment, post-treatment, and 6-month follow-up and included pain intensity (numerical rating scale 0 to 10) and sexual function (Female Sexual Function Scale). Multilevel analyses were used to examine the potential predictors of response over time including pain characteristics (PVD subtype, pain duration), psychological variables (fear of pain, pain catastrophizing), and PFM function assessed with a dynamometric speculum (tone, flexibility, and strength). RESULTS: PVD subtype and PFM tone were significant predictors of greater treatment response for pain intensity reduction. Secondary PVD (ie, pain developed after a period of pain-free intercourse) and lower PFM tone at baseline were both associated with greater reduction in pain intensity after PT and at follow-up. Among the psychological variables, fear of pain was the only significant predictor of better treatment response when assessed through improvement in sexual function, where higher fear of pain at baseline was associated with greater improvement after PT. DISCUSSION: This study identified PVD secondary subtype, lower PFM tone, and higher fear of pain as significant predictors of better treatment response to PT in women with PVD.
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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 | 0.003 |
| 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".