Acupuncture for the Treatment of Vulvodynia: A Randomized Wait-List Controlled Pilot Study
Bibliographic record
Abstract
INTRODUCTION: The incidence of vulvodynia in American women has been reported to be between 8.3% and 16%. However, there is no consistently effective standardized treatment for vulvodynia. AIM: To determine the feasibility and potential effects of using a standardized acupuncture protocol for the treatment of women with vulvodynia. MAIN OUTCOME MEASURES: The primary outcome was vulvar pain, and sexual function was the secondary outcome. Pain was assessed by the Short-Form McGill Pain Questionnaire, and function was measured by the Female Sexual Function Index (FSFI). METHODS: Thirty-six women with vulvodynia met inclusion criteria. The women were randomly assigned either to the acupuncture group or to the wait-list control group. The 18 subjects assigned to the acupuncture group received acupuncture two times per week for 5 weeks for a total of 10 sessions. RESULTS: Reports of vulvar pain and dyspareunia were significantly reduced, whereas changes in the aggregate FSFI scores suggest significant improvement in sexual functioning in those receiving acupuncture vs. those who did not. Acupuncture did not significantly increase sexual desire, sexual arousal, lubrication, ability to orgasm or sexual satisfaction in women with vulvodynia. CONCLUSION: This was the first randomized controlled pilot study to examine the use of acupuncture for the treatment of vulvodynia. The acupuncture protocol was feasible and in this small sample appeared to reduce vulvar pain and dyspareunia with an increase in overall sexual function for women with vulvodynia. This study should be replicated in a larger double-blinded randomized controlled trial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".