Cartilage and Bone Biomarkers in Rheumatoid Arthritis: Prediction of 10-year Radiographic Progression
Bibliographic record
Abstract
OBJECTIVE: As current predictors of joint destruction have low specificity, serological biomarkers reflecting bone and cartilage destruction have been proposed as tools in assessing prognosis of rheumatoid arthritis (RA). We examined whether serum concentrations of a panel of biomarkers could predict radiographic progression in patients with RA. METHODS: A cohort of 238 patients with RA was followed longitudinally for 10 years with collection of clinical data and serum samples. These analyses focus on the 136 patients with radiographs of the hands available at baseline and at 5 and/or 10 years. Radiographs were scored according to the van der Heijde-modified Sharp score (SHS). Baseline sera were analyzed for receptor activator of nuclear factor-kappaB ligand (RANKL), osteoprotegerin (OPG), human cartilage glycoprotein-39 (YKL-40), C2C, collagen cross-linked C-telopeptide (CTX-I), and cartilage oligomeric matrix protein (COMP). Multivariate linear and logistic regression analyses were used to identify predictors of radiographic progression. RESULTS: Baseline CTX-I levels were higher in progressors [0.41 ng/ml (interquartile range 0.31-0.75)] than in nonprogressors [0.32 ng/ml (IQR 0.21-0.49)], and were independently associated with 10-year change in radiographic damage score [ss = 16.4 (IQR 5.7-27.1)]. We found no association between radiographic progression and baseline serum levels of RANKL, OPG, C2C, YKL-40, or COMP. CONCLUSION: This longterm followup study of patients with RA indicates a relationship between elevated CTX-I levels in serum and subsequent joint destruction. This association was, however, weak, and our study does not support that serum CTX-I or any of the other tested biomarkers will serve as more useful prognostic markers than current predictors such as anti-cyclic citrullinated peptide, radiographic damage early in the disease course, and signs of inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".