Synovitis Is Associated With Constant Pain in Knee Osteoarthritis: A Cross-sectional Study of OMERACT Knee Ultrasound Scores
Bibliographic record
Abstract
OBJECTIVE: To examine the association between ultrasound (US)-detected knee inflammation and intermittent and constant pain experiences in patients with knee osteoarthritis (OA). METHODS: Participants with radiographically early-stage (Kellgren-Lawrence arthritis grading scale [KL] ≤ 2) and late-stage (KL ≥ 3) disease and frequent symptoms underwent musculoskeletal US measures of inflammation using the Outcome Measures in Rheumatology (OMERACT) knee US scoring system. Pain experiences were captured using the Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP) tool. We assessed the association between US-synovitis and ICOAP pain experiences using a series of linear, logistic, or multinomial logistic regression models (as appropriate for each variable), while adjusting for age, sex, BMI, and radiographic stage. Secondary analyses were performed similarly by radiographic stage. RESULTS: Pain and synovitis measures from 248 patients (453 knees) were included. Worse synovitis was associated with higher ICOAP constant pain scores (β 8.05, 95% CI 0.67-15.43), but not intermittent pain scores. Moderate-to-severe synovitis was associated with a 4.73-fold increased relative risk (95% CI 1.06-17.00) of a constant pain pattern. In secondary analyses, moderate-to-severe synovitis in early radiographic OA was associated with 2.70-higher odds (95% CI 1.04-7.02) of any constant pain, 3.28-higher odds (95% CI 1.43-7.52) of any intermittent pain, and with higher intermittent (β 10.47, 95% CI 1.03-19.91) and constant (β 12.62, 95% CI 3.02-22.23) pain scores. No associations were identified for synovitis in those with late radiographic OA. CONCLUSION: In patients with knee OA, moderate-to-severe synovitis is most strongly associated with constant pain. Inflammation may play context-specific roles across pain experiences, especially in earlier radiographic stages of 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".