Relationship Between Markers of Platelet Activation and Inflammation with Disease Activity in Wegener’s Granulomatosis
Bibliographic record
Abstract
OBJECTIVE: There remains a need for biomarkers to guide therapy in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. Our objective was to determine whether measures of platelet activation or inflammation are associated with disease activity in Wegener's granulomatosis (WG). METHODS: Study subjects were participants in a clinical trial. Soluble CD40 ligand (sCD40L), C-reactive protein, interleukin 6 (IL-6), IL-8, monocyte chemoattractant protein 1 (MCP-1), P-selectin, vascular endothelial growth factor, and proteinase 3 (PR3)-specific ANCA were measured by ELISA using plasma samples obtained at baseline (active disease), at remission, and prior to, during, and after first flares. Disease activity was assessed by the Birmingham Vasculitis Activity Score for WG (BVAS/WG). Association of biomarkers with disease activity was determined with conditional logistic and linear regression. RESULTS: Over a mean followup of 27 months, 180 subjects underwent 2044 visits; markers were measured in 563 samples. Longitudinally, all markers other than IL-6 were associated with disease activity. The strongest associations for active disease at baseline versus remission were observed for sCD40L (OR 4.72, 95% CI 2.47-9.03), P-selectin (OR 6.26, 95% CI 2.78-14.10), PR3-ANCA (OR 9.41, 4.03-21.99), and inversely for MCP-1 (OR 0.36, 95% CI 0.22-0.57). BVAS/WG increased by 0.80 (95% CI 0.44-1.16), 0.83 (95% CI 0.42-1.25), and 0.81 (95% CI 0.48-1.15) per unit-increase in PR3-ANCA, sCD40L, and P-selectin, respectively; and decreased by 1.54 (95% CI 0.96-2.12) per unit-increase in MCP-1. CONCLUSION: Cytokines arising from within the circulation, including those of platelet activation, correlate with disease activity in WG.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".