Eotaxin-3 as a Biomarker of Activity in Established Eosinophilic Granulomatosis with Polyangiitis
Bibliographic record
Abstract
To the Editor: Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare antineutrophil cytoplasmic antibody–associated vasculitis affecting small to medium vessels1,2. The majority of patients with EGPA respond to glucocorticoids ± immunosuppressive agents3, though a significant proportion of them develop relapses and may require biologic treatment4. Novel biomarkers of disease activity for EGPA are needed because the clinical value of eosinophil count, serum IgE, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) in treated patients is low5. Eotaxin-3 is an eotactic chemokine that induces chemotaxis and activation of eosinophilic granulocytes in vitro , and may be involved in the pathogenesis of EGPA. Polzer, et al suggested that elevated eotaxin-3 expression was associated with high disease activity in EGPA6, while Dejaco, et al proposed that eotaxin-3 levels could not reliably discriminate between active and inactive disease in established EGPA7. In our current study, we investigated the clinical utility of eotaxin-3 as a possible biomarker of EGPA relapse. A total of 38 patients with EGPA (27 women and 11 men at a mean age … Address correspondence to Prof. S.V. Moiseev, Clinic of Nephrology, Internal and Occupational Diseases, Rossolimo, 11/5, Moscow 119435, Russia. E-mail: clinpharm{at}mtu-net.ru
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".