The weight-bearing pain is closest associated with physical function among different pain patterns: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Abstract Objective To compare the correlations between different pain patterns and physical function, and identify the most related pain pattern with physical function in knee OA. Methods 736 participants with radiological knee OA were included from the Osteoarthritis Initiative (OAI). Five pain patterns were assessed, including pain severity, intermittent, constant, weight-bearing, and non-weight-bearing pain patterns. Physical function was evaluated by the Western Ontario and McMaster Universities Arthritis Index physical function subscale (WOMAC-PF), Knee Injury and Osteoarthritis Outcome Score Function in Sport and Recreation (KOOS-FSR) and 20-Meter Walking Test (20-MWT). Linear regression analysis were used to exam the associations between pain patterns and physical function, and heat map was plotted to visualize the standardized β coefficients. Results Among all pain patterns, the weight-bearing pain pattern had the strongest correlation with WOMAC-PF and KOOS-FSR at baseline (β = 0.451, p < 0.001; β = -0.354, p < 0.001), year-2 follow up (β = 0.345, p < 0.001; β = -0.279, p < 0.001) and 2-year change (β = 0.430, p < 0.001; β = -0.279, p < 0.001). Except for weight-bearing pain pattern at year-2 follow up (β = 0.079, p = 0.049), pain in other linear models showed no significant correlation with 20-MWT, and weight-bearing pain was always closest to the statistical threshold value (p < 0.05). Conclusions Weight-bearing pain pattern was most closely associated with physical function. Therapeutic targets related to weight-bearing pain should be preferred when administering analgesic therapies to improve physical function in knee OA.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".