Cross‐Sectional and Longitudinal Associations Between Serum Levels of High‐Sensitivity C‐Reactive Protein, Knee Bone Marrow Lesions, and Knee Pain in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To describe associations between serum high-sensitivity C-reactive protein (hsCRP), knee bone marrow lesions (BMLs), and knee pain, cross-sectionally and longitudinally, in patients with knee osteoarthritis (OA). METHODS: Patients (n = 192) with symptomatic knee OA (mean age 63 years, range 50-79, women 53%) were assessed at baseline and after 24 months. Serum hsCRP was measured using enzyme-linked immunosorbent assay. Knee BMLs were scored using the modified Whole-Organ Magnetic Resonance Imaging (MRI) Score from T2-weighted fat-supressed fast spin-echo MRI. Knee pain was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index. RESULTS: Quartiles of baseline serum hsCRP were associated with the presence of knee BMLs (prevalence ratio 1.07 per quartile [95% confidence interval (95% CI) 1.00, 1.15]) and total knee pain scores (β 13.66 per quartile [95% CI 2.26, 25.07]) in multivariable analyses. Longitudinally, higher baseline hsCRP was associated with an increase in BML score (risk ratio 1.37 per quartile [95% CI 1.10, 1.70]), and change in hsCRP was positively associated with change in BML score (β 0.19 [95% CI 0.05, 0.34]) in adjusted analyses. Baseline hsCRP was not associated with change in total knee pain, but change in hsCRP was positively and significantly associated with change in total knee pain (β 4.71 [95% CI 0.48, 8.94]). This became nonsignificant after adjustment for changes in BML score. CONCLUSION: In patients with knee OA, serum hsCRP is associated with knee BML scores and, to a lesser extent, pain both cross-sectionally and longitudinally, suggesting that inflammation is linked with BMLs and their associated pain.
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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) | 0.000 | 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".