A randomized comparison of group cognitive–behavioral therapy, surface electromyographic biofeedback, and vestibulectomy in the treatment of dyspareunia resulting from vulvar vestibulitis
Bibliographic record
Abstract
This study compared group cognitive-behavioral therapy (12-week trial), surface electromyographic biofeedback (12-week trial), and vestibulectomy in the treatment of dyspareunia resulting from vulvar vestibulitis. Subjects were 78 women randomly assigned to one of three treatment conditions and assessed at pretreatment, posttreatment and 6-month follow-up via gynecological examinations, structured interviews and standard questionnaires pertaining to pain (Pain Rating Index and Sensory scale of the McGill Pain Questionnaire, vestibular pain index, pain during intercourse), sexual function (Sexual History Form, frequency of intercourse, Information subscale of the Derogatis Sexual Functioning Inventory), and psychological adjustment (Brief Symptom Inventory). As compared with pretreatment, study completers of all treatment groups reported statistically significant reductions on pain measures at posttreatment and 6-month follow-up, although the vestibulectomy group was significantly more successful than the two other groups. However, the apparent superiority of vestibulectomy needs to be interpreted with caution since seven women who had been assigned to this condition did not go ahead with the intervention. All three groups significantly improved on measures of psychological adjustment and sexual function from pretreatment to 6-month follow-up. Intent-to-treat analysis supported the general pattern of results of analysis by-treatment-received. Findings suggest that women with dyspareunia can benefit from both medical and behavioral interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".