Association between synovial tissue damage and pain in late-stage knee osteoarthritis: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To identify the presence and distribution of histopathological features of synovial inflammation and tissue damage, and to test their associations with ultrasound (US) imaging measures of synovitis and patient-reported measures of pain in knee osteoarthritis (OA). DESIGN: In the cross-sectional study of 122 patients undergoing surgery for painful late-stage (Kellgren-Lawrence Grade 3 or 4) knee OA, we compared US measures of synovitis (n = 118) and pain (Knee Injury and Osteoarthritis Outcome Score) to histopathological measures of inflammation vs. synovial tissue damage in synovial tissue biopsies. Associations of histopathological features with US measures of inflammation or pain were assessed using linear or logistic regression while controlling for covariates. RESULTS: Histopathological features of inflammation were associated with higher odds of moderate/severe US synovitis (odds ratio [OR] = 1.34 [95%CI 1.04, 1.74), whereas features of synovial tissue damage were associated with lower odds of moderate/severe US synovitis (OR = 0.77 [95%CI 0.57, 1.03]). Worse histopathological scores for synovial tissue damage were associated with more pain (-1.47 [95%CI -2.88, -0.05]), even while adjusting for synovial inflammation (-1.61 [95%CI -3.12, -0.10]). CONCLUSIONS: Synovial tissue damage is associated with pain in late-stage knee OA, independent from inflammation and radiographic damage. These novel findings suggest that preventing synovial tissue damage may be an important goal of disease-modifying OA therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".