Serum Pyridinoline Levels and Prediction of Severity of Joint Destruction in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Previous studies indicated that pyridinoline, a collagen crosslink in cartilage and bone, might be a good marker to predict joint destruction in patients with rheumatoid arthritis (RA), although large prospective studies are lacking. We evaluated the predictive value of serum pyridinoline levels for joint destruction, both at baseline for longterm prediction and during the disease course for near-term prediction. METHODS: Patients with early RA from the Leiden Early Arthritis Clinic were studied. Radiographs at baseline and yearly during 7 years of followup were scored according to the Sharp-van der Heijde Scoring (SHS) method. Pyridinoline serum levels at baseline and during followup were measured by ELISA. The association between baseline pyridinoline levels and difference in SHS over 7 years was tested, with a multivariate normal regression model. Second, the association between pyridinoline levels determined during the disease course and progression of SHS over the next year was tested with a multivariable linear regression analysis. RESULTS: Studying baseline pyridinoline serum levels in 437 patients revealed that the mean SHS over 7 years was 6% higher for every higher pyridinoline level (nmol/l) at baseline (p = 0.001). Subsequently, during followup (n = 184 patients) the progression in SHS in the upcoming year was 17% higher for every higher nmol/l pyridinoline level (p = 0.001). The area under the receiver-operation characteristic curve for rapid radiological progression was 0.59. CONCLUSION: Increased pyridinoline serum levels, both at baseline and during the disease course, are associated with more severe joint destruction during the coming year(s), although the predictive accuracy as a sole predictor was moderate.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".