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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it