IgG4:IgG RNA ratio differentiates active disease from remission in granulomatosis with polyangiitis: a new disease activity marker? A cross-sectional and longitudinal study
Bibliographic record
Abstract
OBJECTIVES: ) B cells and plasma cells are implicated in the pathogenesis of GPA. We hypothesized that the presence of these cells in peripheral blood could serve as disease activity parameter in GPA. METHODS: We included 35 proteinase 3-antineutrophil cytoplasmic antibodies-positive patients with GPA in a cross-sectional study. Active disease was defined as Birmingham Vasculitis Activity Score (BVAS) ≥ 3 (n = 15), remission as BVAS of 0 (n = 17), and low disease activity (LDA) as BVAS of 1-2 and clinical remission (n = 3). Healthy subjects (n = 10), patients with systemic lupus erythematosus (n = 24), and patients with rheumatoid arthritis (n = 19) functioned as control subjects. An additional longitudinal study was performed in ten patients with GPA. Using a validated qPCR test, we measured the IgG4:IgG RNA ratio in all groups and compared the results with known biomarkers. RESULTS: The median qPCR score was higher in active GPA (21.4; IQR 12.1-29.6) than in remission/LDA (3.3; IQR 1.6-5.6) (Mann-Whitney U test, p < 0.0001) and outperformed other known disease activity parameters in detecting activity. A cutoff qPCR score of 11.2% differentiated active disease from remission/LDA accurately (AUC 0.993). The qPCR test correlated well with the BVAS (Spearman r = 0.77, p < 0.0001). In the longitudinal study, a decrease in BVAS correlated with qPCR score reduction (paired t test, p < 0.05). CONCLUSIONS: The IgG4:IgG RNA ratio in GPA accurately distinguishes active disease from remission and correlates well with disease activity in these single-center studies. If these results are confirmed in larger longitudinal studies, this test might help to steer treatment decisions in patients with GPA.
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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 | 0.003 | 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 | 0.001 | 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".