Associations between single-question Visual Analogue Scale pain score and weight-bearing and non–weight-bearing domains of Western Ontario and McMaster Universities Arthritis Index pain: data from 2 phase 3 clinical trials
Bibliographic record
Abstract
Abstract Introduction: Visual Analogue Scale (VAS) and the pain subscale of the Western Ontario and McMaster Universities Arthritis Index (WOMAC) are commonly used measuring tools of osteoarthritis (OA) pain. Objectives: The objective of this cross-sectional study was to explore the associations between single-question VAS pain and the weight-bearing and non–weight-bearing domains of WOMAC pain. Methods: Data from 2093 patients with OA participating in 2 phase 3 clinical trials were included for post hoc analyses. Univariate Pearson correlations and comparison of r values were made using z statistics obtained using the Fisher r to z test for all items of the VAS pain scale, the WOMAC pain subscale, the weight-bearing and non–weight-bearing constructs of WOMAC pain subscale, and by subgroups of WOMAC pain quintiles and Kellgren–Lawrence grades . Results: The correlations between VAS pain and WOMAC pain were significant ( r = 0.67, P < 0.001) with a slope of 0.57 (95% confidence interval [CI]: 0.54–0.61). A similar correlation was found for weight-bearing pain ( r = 0.68, P < 0.001, slope: 0.62 (95% CI: 0.59–0.65) but significantly lower for non–weight-bearing pain ( r = 0.55, P < 0.001, slope: 0.49 (95% CI: 0.46–0.52). The degree of disagreement between the 2 instruments seemed to be lesser in the extreme ends of the scales, and the observed association between Kellgren–Lawrence grade and disagreement between VAS and WOMAC was driven by non–weight-bearing pain. Conclusion: In conclusion, VAS pain and WOMAC pain subscale correlation was found to be moderate and the VAS pain scale correlated more accurately with the WOMAC pain weight-bearing questions. This constitutes novel insight into patient with OA pain reporting.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".