Tipping the Scales: A Corpus-Based Reconstruction of Adjective Scales in the McGill Pain Questionnaire
Bibliographic record
Abstract
Modern medical diagnosis relies on precise pain assessment tools in translating clinical information from patient to physician. The McGill Pain Questionnaire (MPQ) is a clinical pain assessment technique that utilizes 78 adjectives of different intensities in 20 categories to quantify a patient’s pain. The questionnaire’s efficacy depends on a predictable pattern of adjective use by patients experiencing pain. In this study, I recreate the MPQ’s adjective intensity orderings using data gathered from patient forums and modern NLP techniques. I extract adjective intensity relationships by searching for key linguistic contexts, and then combine the relationship information to form robust adjective scales. Of 17 adjective relationships predicted by this research, 10 show agreement with the MPQ, which is statistically significant at the .5 alpha level. The results suggest predictable patterns of adjective use by people experiencing pain, but call into question the MPQ’s categories for grouping adjectives.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".