Relationship between MPQ and VAS in 962 patients. A rationale for their use.
Bibliographic record
Abstract
BACKGROUND: 1) To analyse the information provided both by the Visual Analogue Scale (VAS) and by the McGill Pain Questionnaire (MPQ) in a cross-sectional study with patients affected by different kinds of pain and to study the relationship between VAS and MPQ scores in the same patient sample. METHODS: 962 patients affected by different kinds of pain (i.e. neuropathic pain, acute post-traumatic pain, chronic musculo-skeletal pain, headache, and cancer pain) were enrolled into the study during the first visit for pain management. The horizontal 10cm VAS and the Italian version of the MPQ were administered. RESULTS: VAS scores proved to be significantly lower in acute post traumatic and in chronic musculo- skeletal pain compared to headache and neuropathic pain. VAS scores were signi- ficantly higher in neuropathic pain compared to cancer pain. MPQ total score (Pain Rating Index, PRI) related to neuropathic pain was significantly higher than scores reported in the other pain groups, with the exception of cancer pain. Cancer pain MPQ total score was higher than acute post-traumatic and chronic musculo-skeletal PRI pain scores. Different patterns of MPQ dimensions emerged within each pain group. The association between VAS and PRI, analysed by means of stepwise multiple regression analyses was significantly different among the groups (p<0.0001). The percentage of VAS variance explained by MPQ PRI score ranged from 6% (headache) to 32% (neuro-pathic pain). CONCLUSIONS: Several differences emerged among the pain groups. VAS and MPQ resulted to address pain aspects only partially overlapping. In some clinical conditions (headache and cancer) the MPQ can provide more detailed and clinically useful information about patients' pain experience.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".