Von Willebrand factor antigen as a marker of disease activity in childhood-onset antineutrophil cytoplasmic antibody–associated vasculitis
Bibliographic record
Abstract
OBJECTIVE: Von Willebrand factor (VWF) antigen plays a role in vascular inflammation and thrombosis, both of which are important in the pathogenesis of ANCA-associated vasculitis (AAV). Previous work found that VWF correlates with disease activity in childhood-onset primary CNS vasculitis. We sought to determine the relationship between VWF and disease activity over time in children with AAV. METHODS: AAV patients with more than one VWF level measured were included in this retrospective study, and the relationships between active vasculitis, VWF and other disease measures were analysed. Generalized estimating equations analysis was used to account for repeated VWF measurements within a patient. Repeated measures correlation was used to determine associations of paired laboratory observations. Diagnostic performance was evaluated using receiver operating curve analysis. RESULTS: A total of 732 total VWF measurements were collected in 33 AAV patients. VWF antigen levels were higher during active disease [median 2.03 IU/ml, interquartile range (IQR) 1.35, 2.55] compared with inactive disease (median 1.18 IU/ml, IQR 0.94, 1.53). VWF antigen was the only variable that was significantly associated with active disease (odds ratio 3.01, P < 0.001, 95% CI 2.3, 3.93). The effect of VWF did not show a substantial difference between the disease subtypes. There was a moderate positive correlation between VWF antigen and disease activity, with an acceptable sensitivity and specificity rates. CONCLUSION: Increased VWF antigen levels correlate with active vasculitis in this paediatric-onset AAV cohort and may be used as an additional biomarker in childhood AAV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".