Vulvodynia is not created equally: empirical classification of women with vulvodynia
Bibliographic record
Abstract
BACKGROUND: Vulvodynia classification is based on the sensory dimensions of pain and does not include psychological factors associated with the pain experience and treatment outcomes. Previous work has shown that individuals with chronic pain can be classified into subgroups based on pain sensitivity, psychological distress, mood, and symptom severity. OBJECTIVE: The aim of this study was to identify distinct subgroups of women with vulvodynia enrolled in the National Vulvodynia Registry. We hypothesized that women with vulvodynia can be clustered into subgroups based on distress and pain sensitivity. DESIGN: A cross-sectional study. METHODS: We conducted an exploratory hierarchical agglomerative cluster analysis using Ward's cluster method and squared Euclidean distances to identify unique subgroups based on baseline psychological distress and pain sensitivity. The variables included the catastrophizing subscale of the Coping Strategies Questionnaire, the Beck Depression Inventory, the State Trait Anxiety Index-Trait scale, McGill Pain Questionnaire-Affective subscale, and vulvar and pelvic muscle pressure pain sensitivity. SUBJECTS: Eight sites enrolled women who presented with vaginal or vulval pain of at least 3-month duration. RESULTS: Two distinct subgroups, high pain sensitivity with high distress (n=27) and low pain sensitivity with low distress (n=100), emerged from the cluster analysis. Validation indicated that subgroups differed in terms of clinical pain intensity, sensory aspects of pain, and intercourse pain. CONCLUSION: Empirical classification indicates that unique subgroups exist in women with vulvodynia. Providers should be aware of the heterogeneity of this condition with respect to pain-related distress and pain sensitivity.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".