Responses to the McGill Pain Questionnaire Predict Neuropathic Pain Medication Use in Women in With Vulvar Lichen Sclerosus
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to test the hypothesis that responses to the McGill Pain Questionnaire are predictive of adjunctive neuropathic pain medication use by women with lichen sclerosus (LS). MATERIALS AND METHODS: This is a retrospective chart review of 430 women with vulvar LS treated at a tertiary referral vulvar care clinic. Demographics, responses to the McGill Pain Questionnaire, and use of neuropathic pain medications were collected. Bivariate and multivariable logistic regression analyses were performed to identify factors significantly associated with use of neuropathic pain medications. RESULTS: Of the 430 subjects, 119 (27.7%) used neuropathic pain medications for vulvar pain. Factors significantly associated with use of these medications include lower body mass index (odds ratio [OR] = 0.96, p = .02), non-White race (OR = 2.97, p = .05), and total McGill Pain Questionnaire score (OR = 1.05, p < .001). CONCLUSIONS: Vulvar pain is a common presenting symptom in women with LS. Responses to the McGill Pain Questionnaire may be helpful in the long-term management of women with LS as a screen to identify those patients who might benefit from adjunctive neuropathic pain medication use.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".