Systematic review and meta-analysis of the effects of treatment modalities for vestibulodynia in women
Bibliographic record
Abstract
Objective: To quantify the effects of available treatments of vestibulodynia.Methods: Systematic review of randomised controlled trials (RCTs) in six search engines until December 2018, comparing any intervention vs. placebo or sham in women with vestibulodynia. Primary outcome was dyspareunia assessed with visual analogue (VAS) or numeric rating (NRS) scales. Secondary outcomes were daily vestibular symptoms (DVS), McGill Pain Questionnaire (MPQ) and Index of Sexual Satisfaction (ISS). Effects were described as mean differences (MDs) with their 95% confidence intervals (CIs). Traditional and frequentist network meta-analyses (NMA) were performed using random effect models.Results: Four RCTs (n = 275) were included evaluating vaginal cream of conjugated oestrogens, oral desipramine with or without topical lidocaine, topical lidocaine, laser therapy and transcranial direct current. In traditional MA, interventions did not reduce dyspareunia (MD = 0.08; 95%CI = −0.49 to 0.64), DVS (MD = −0.04; 95%CI = −0.31 to 0.24; 4 interventions), or MPQ (MD = −0.17; 95%CI = −2.16 to 1.81; 4 interventions). ISS was significantly improved (MD = −5.14; 95%CI = −9.52 to −0.75). In NMA, oral desipramine with or without lidocaine significantly improved ISS vs. other treatments.Conclusions: Several existing interventions were not associated with improvements in vestibulodynia. There only was improvement of sexual function with oral desipramine with or without lidocaine.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".