Measuring Pain in Systemic Sclerosis: Comparison of the Short-form McGill Pain Questionnaire Versus a Single-item Measure of Pain
Bibliographic record
Abstract
OBJECTIVE: Studies of pain in systemic sclerosis (SSc) have used a variety of measures, including single-item measures and the 15-item short-form McGill Pain Questionnaire (MPQ-SF). The objective of our study was to compare the performance of the MPQ-SF to a single-item pain numerical rating scale (NRS) and determine whether the MPQ-SF effectively differentiates between sensory and affective components of pain in SSc. METHODS: A cross-sectional, multicenter study of 1091 patients from the Canadian Scleroderma Research Group Registry who completed the MPQ-SF and pain NRS. Correlations of MPQ-SF total scores and pain NRS scores with relevant outcome measures (disability, quality of life, depressive symptoms) were compared. To assess whether the MPQ-SF differentiated between sensory and affective factors, confirmatory factor analysis modeling was used, and correlations of sensory and affective factor scores with other outcome measures were compared. RESULTS: MPQ-SF total score and the pain NRS correlated similarly with other outcome measures, as did the sensory and affective scores. MPQ-SF sensory and affective factors were highly correlated (0.92), and a single-factor model fit as well as a 2-factor (sensory and affective) model. CONCLUSION: The substantial overlap between sensory and affective subscales of the MPQ-SF and the similarity of the MPQ-SF and NRS pain measures compared to other patient-reported outcomes suggest that the 15-item MPQ-SF does not provide tangible advantages compared to the single-item pain NRS. These findings support recommendations to use a single-item NRS pain measure in SSc as it is less burdensome to patients than the MPQ-SF.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".