Serum Biomarkers in Patients with Relapsing Eosinophilic Granulomatosis with Polyangiitis (Churg-Strauss)
Bibliographic record
Abstract
INTRODUCTION: Previous studies suggest a role for eotaxin-3, TARC/CCL17 and IgG4 in newly-diagnosed patients with eosinophilic granulomatosis with polyangiitis (EGPA, Churg-Strauss) with highly active disease. The role of these biomarkers in relapsing disease is unclear. METHODS: Serum levels of TARC/CCL17, eotaxin-3, IgG4, and IgG4/IgG ratio were determined in serum samples from a longitudinal cohort of patients with EGPA (105 visits of 25 patients). Epidemiological, clinical and laboratory data were available for all visits. RESULTS: At the first visit, 80% of patients were using glucocorticoids and 68% additional immunosuppressive drugs. Disease flares were seen at 18 visits. The median BVAS and BVAS/WG scores at time of relapse were 4 and 2, respectively. None of the biomarkers tested were useful to discriminate between active disease and remission. Patients treated with prednisone had lower eotaxin-3 and eosinophil levels compared to patients not taking glucocorticoids irrespective of disease activity. Use of immunosuppressive agents was not associated with biomarker levels. CONCLUSIONS: Serum levels of TARC/CCL17, eotaxin-3, IgG4, and IgG4/IgG ratio do not clearly differentiate active and inactive disease in established EGPA. Defining biomarkers in EGPA remains a challenge especially during times of glucocorticoid use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".