Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2)
Bibliographic record
Abstract
The objective of the present research was to develop a single measure of the major symptoms of both neuropathic and non-neuropathic pain that can be used in studies of epidemiology, natural history, pathophysiologic mechanisms, and treatment response. We expanded and revised the Short-form McGill Pain Questionnaire (SF-MPQ) pain descriptors by adding symptoms relevant to neuropathic pain and by modifying the response format to a 0-10 numerical rating scale to provide increased responsiveness in longitudinal studies and clinical trials. The reliability, validity, and subscale structure of the revised SF-MPQ (SF-MPQ-2) were examined in responses from 882 individuals with diverse chronic pain syndromes and in 226 patients with painful diabetic peripheral neuropathy who participated in a randomized clinical trial. The data suggest that the SF-MPQ-2 has excellent reliability and validity, and the results of both exploratory and confirmatory factor analyses provided support for four readily interpretable subscales-continuous pain, intermittent pain, predominantly neuropathic pain, and affective descriptors. These results provide a basis for use of the SF-MPQ-2 in future clinical research, including clinical trials of treatments for neuropathic and non-neuropathic pain conditions.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".