The Treatment of Provoked Vestibulodynia
Bibliographic record
Abstract
OBJECTIVE: To carry out a critical review of published studies concerning the treatment of provoked vestibulodynia. METHODS: MEDLINE, PsycINFO, and Cochrane were used to identify treatment studies published between January 1996 and December 2006. All studies published in English that dealt specifically with the treatment of provoked vestibulodynia were included in the review regardless of their methodological quality. Thirty-eight treatment studies were thus examined in the present paper. RESULTS: Since 1996, surgical treatment has received somewhat less empirical attention. Nevertheless, it still boasts the best success rates, which range from 61% to 94%. More studies have focused on medical treatments, yielding success rates varying between 13% and 67%. Behavioral treatments have been the least studied, although 35% to 83% of patients benefit from them. Despite these interesting results, only 5 of the 38 treatment studies reviewed are randomized clinical trials. Furthermore, the majority of studies have several methodological weaknesses, such as the absence of (1) control or placebo group, (2) double-blind evaluation, (3) pretreatment pain evaluation, and (4) validated measures of pain and sexual functioning. DISCUSSION: On the basis of the results of the reviewed prospective studies and the randomized clinical trials, vestibulectomy is the most efficacious treatment to date. Though some medical treatments seem little effective, others appear promising and should be investigated further, as is the case with behavioral treatments. Additional randomized clinical trials are necessary to confirm the efficacy of surgery and validate nonsurgical treatments for provoked vestibulodynia.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".