Pain assessment in context: a state of the science review of the McGill pain questionnaire 40 years on
Bibliographic record
Abstract
The McGill pain questionnaire (MPQ) and its later derivative the short form-MPQ have been used widely both in experimental and clinical pain studies. They have been of considerable importance in stimulating research into the perception of pain and now, with the publication of its latest variant, the short form-MPQ-2, it is appropriate to appraise their utility in the light of subsequent research into the nature of pain and the purpose of pain assessment. Following a description of the content and development of the questionnaires, issues of validity, reliability, and utility are addressed, not only in terms of the individual pain descriptors and the scales, but also in terms of methods of quantification. In addition, other methods of pain depiction are considered. In the second part of the review, advances in pain measurement and methodology, in the elucidation of pain mechanisms and pathways, in the psychology of pain, and in the nature of pain behavior are presented and their implications for pain assessment in general and the MPQ family of measures in particular will be addressed. It is suggested that pain assessment needs to be cast in its social context. We need to understand the influences on pain expression using a socio-communication model of pain that recognizes the function of pain and the importance of both innate pain responses and the effects of social learning. The review concludes with recommendations for future use of the MPQ and identifies a number of research challenges which lie ahead.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".