Use of the McGill Pain Questionnaire to compare women with vulvar pain, pelvic pain and headaches.
Bibliographic record
Abstract
OBJECTIVE: To assess differences between women with three distinct types of chronic pain conditions using a modified McGill Pain Questionnaire. STUDY DESIGN: Data by self-administered questionnaire were collected on patients presenting to the University of Michigan Medical Center with chronic vulvar pain (144 patients), pelvic pain (198 patients) or headaches (130 patients). Data for analysis included: patient demographics, duration of pain and modified McGill Pain Questionnaire scores. Univariate and multivariate analyses were performed. RESULTS: Patients with vulvar pain had more formal education (P < .001), were more likely to be married (P < .001) and were less likely to be African American (P = .003) as compared to those with chronic pelvic pain and headaches. Chronic pelvic pain patients were younger than those in the other two groups (P = .002), and headache patients were likely to have had their chronic pain for a shorter duration than those with vulvar or pelvic pain (P < .001). Patients with vulvar pain had lower total scores on the McGill Pain Questionnaire as well as on the four subsets of variables: affective, sensory, cognitive and miscellaneous indexes (P < .001). They also chose fewer words to describe their symptoms from the 20-word lists (P < .001) and had lower average scores in each of the 20 categories as compared to the other two groups (P < .0001). Controlling for age, ethnicity and marital status did not alter this significance. CONCLUSION: Patients with vulvar pain were a unique groups when compared to other chronic pain populations. Evaluation of the demographics and McGill Pain Questionnaire scores confirmed the distinct qualities of women with vulvar pain.
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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) | 0.003 | 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".