Recommendations for Self-Report Outcome Measures in Vulvodynia Clinical Trials
Bibliographic record
Abstract
OBJECTIVES: Vulvodynia (idiopathic chronic vulvar pain) is a prevalent condition associated with significant and negative impacts in many areas of function. Despite the increased research interest in vulvodynia in recent years, recommendations for outcome measures for use in clinical trials are missing. The purpose of this paper, therefore, was to provide recommendations for outcome measures for vulvodynia clinical trials so that consistent measures are used across trials to facilitate between-study comparisons and the conduct of large multicenter trials, and to improve measurement of the multiple dimensions of vulvodynia. METHODS: Given that provoked vestibulodynia (PVD)-characterized by provoked pain localized to the vaginal opening-is the most common subtype of vulvodynia and the current main focus of clinical trials, this paper focused on recommended outcome measures in PVD clinical trials. The framework used to guide the selection of outcome measures was based on the one proposed by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT). RESULTS: The IMMPACT framework provided a well-suited guideline for outcome measure recommendations in PVD clinical trials. However, given the provoked presentation of PVD and the significant impact it has on sexuality, modifications to some of the IMMPACT recommendations were made and specific additional measures were suggested. DISCUSSION: Measures that are specific to vulvovaginal pain are ideal for adoption in PVD clinical trials, and many such measures currently exist that allow the relevant IMMPACT domains to be captured.
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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 | 0.630 | 0.763 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".