Associations between biomarkers of matrix metabolism and inflammation with pain and fatigue in participants suspected of early hip and or knee osteoarthritis: data from the CHECK study
Bibliographic record
Abstract
OBJECTIVES: To assess the associations of biomarkers in serum [highsensitivity C-reactive protein (hs-CRP), serum cartilage oligomeric protein (sCOMP), serum propeptide of type I procollagen (sPINP) and serum osteocalcin (sOC)] and urine [urinary type II collagen telopeptide (uCTX-2)] with the extent and progression of nocturnal pain, pain while walking, and fatigue in participants with hip and/or knee pain suspected to be early stage osteoarthritis (OA). METHODS: hs-CRP, uCTX-2, sCOMP, sPINP and sOC were measured at baseline in 1,002 participants of the Cohort Hip and Cohort Knee (CHECK). Nocturnal pain, pain while walking and fatigue were assessed by self-reported questionnaires at baseline and 2-year follow-up. Associations between these biomarkers and symptoms were examined using logistic and linear regression analyses. RESULTS: hs-CRP was significantly associated with mild nocturnal pain (OR 1.18 95% CI 1.01-1.37), with mild and moderate pain while walking (OR 1.17 95% CI 1.01-1.35 and OR 1.56 95% CI 1.29-1.90, respectively) and with progression of nocturnal pain (OR 1.25 95% CI 1.07-1.46). uCTX-2 was associated with mild nocturnal pain (OR 1.40 95% CI 1.05-1.85) and with mild and severe-extreme pain while walking (OR 1.35 95% CI 1.04-1.75 and OR 2.55 95% CI 1.03-6.34, respectively). sPINP was associated with severe-extreme nocturnal pain (OR 0.45 95% CI 0.25-0.82). No significant associations were found for sCOMP and sOC, nor for any of the biomarkers and fatigue. CONCLUSION: This study of biomarkers in a large cohort of participants with hip and/or knee pain suspected to reflect early stage hip and/or knee OA suggests that inflammation and cartilage matrix degeneration play a role in pain, but not in fatigue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".