Relationship of Pain Quality Descriptors and Quantitative Sensory Testing
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Chronic pain in adults with sickle cell disease (SCD) may be the result of altered processing in the central nervous system, as indicated by quantitative sensory testing (QST). Sensory pain quality descriptors on the McGill Pain Questionnaire (MPQ) are indicators of typical or altered pain mechanisms but have not been validated with QST-derived classifications. OBJECTIVES: The specific aim of this study was to identify the sensory pain quality descriptors that are associated with the QST-derived normal or sensitized classifications. We expected to find that sets of sensory pain quality descriptors would discriminate the classifications. METHODS: A cross-sectional quantitative study of existing data from 186 adults of African ancestry with SCD. Variables included MPQ descriptors, patient demographic data, and QST-derived classifications. RESULTS: The participants were classified as central sensitization (n = 33), mixed sensitization (n = 23), and normal sensation. Sensory pain quality descriptors that differed statistically between mixed sensitization and central sensation compared to normal sensitization included cold (p = .01) and spreading (p = .01). Aching (p = .01) and throbbing (p = .01) differed statistically between central sensitization compared with mixed sensitization and normal sensation. Beating (p = .01) differed statistically between mixed sensitization compared with central sensitization and normal sensation. No set of sensory pain quality descriptors differed statistically between QST classifications. DISCUSSION: Our study is the first to examine the association between MPQ sensory pain quality descriptors and QST-derived classifications in adults with SCD. Our findings provide the basis for the development of a MPQ subscale with potential as a mechanism-based screening tool for neuropathic pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it