Association of different pain patterns with physical function in participants with knee osteoarthritis: data from the osteoarthritis initiative
Bibliographic record
Abstract
BACKGROUND: Pain is a multidimensional experience and a key symptoms of knee osteoarthritis (KOA). However, it remains unknown whether there is a specific pain pattern that is more strongly associated with physical function compared to other pain patterns among individuals with KOA. This study aimed to compare the correlations between different pain patterns and physical function, and identify the most related pain pattern with physical function in KOA. METHODS: 412 participants with radiological KOA were included from the Osteoarthritis Initiative (OAI). Pain severity and four pain patterns were assessed, including 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), 20-Meter Walking Test (20-MWT) and Repeated Chair Stand test (RCS). RESULTS: Among pain severity and all pain patterns, the weight-bearing pain pattern had the strongest correlation with WOMAC-PF, and showed significant correlations with both WOMAC-PF and KOOS-FSR at baseline, year-2 follow up, and 2-year change (p < 0.001). All pain patterns and pain severity showed weakly significant correlation with 20-MWT and RCS. CONCLUSIONS: Weight-bearing pain pattern was most closely associated with self-reported physical function. Therapeutic targets related to weight-bearing pain should be preferred when administering analgesic therapies to improve physical function in KOA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".