Development of the Norwegian Short-Form McGill Pain Questionnaire (NSF-MPQ)
Bibliographic record
Abstract
The Short-Form McGill Pain Questionnaire (SF-MPQ) contains 15 pain descriptors (11 Sensory, four Affective). The aim was to develop a valid Norwegian SF-MPQ (NSF-MPQ). Descriptors were selected among 333 previously collected Norwegian pain adjectives, selection criteria being conceptual equivalence to the SF-MPQ and adjectives used by > 33%. Pain intensity scoring systems of the SF-MPQ were modelled. The NSF-MPQ, a pain drawing and the Disability Rating Index were presented to 277 patients from five different clinical settings. All pain descriptors were used by ≥ 33% in at least one of the five clinical groups, patients with persistent pain using most descriptors. Cronbach's α was adequately high (0.74–0.87). Spearman rank (ρ) correlations were moderate to very high between groups of pain descriptors (0.68–0.97). Pain descriptor scores showed low to moderate correlations with the two pain intensity variables (VAS and Present Pain Intensity) (0.27–0.52). All scores showed low correlations with pain area extension (0.20–0.45) and disability (0.05–0.30), indicating construct validity of the NSF-MPQ. The NSF-MPQ discriminated between two or more patient groups on item level, but discriminative ability on total score and subscore levels was mediocre. The NSF-MPQ seems to express a different construct than pain distribution and disability, and allows registration of distinctions of pain qualities.
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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 | 0.012 | 0.016 |
| 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 | 0.000 | 0.001 |
| 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".