Eosinophilic granulomatosis with polyangiitis (formerly Churg–Strauss syndrome): where are we now?
Bibliographic record
Abstract
Antineutrophil-cytoplasm antibody (ANCA)-associated vasculitides (AAV), classified as small-sized vessel vasculitides, include: granulomatosis with polyangiitis (GPA) (formerly Wegener's granulomatosis disease), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA) (formerly Churg–Strauss syndrome) [1]. Although they share some common features, EGPA has certain specificities, namely asthma, blood and tissue eosinophilia, and frequent ear, nose and throat (ENT) involvement, while ANCA (targeting neutrophil myeloperoxidase) are found in only a subset of patients (30–70%) [2, 3]. Moreover, EGPA diagnosis can be challenging, as its manifestations can overlap with those of primary hypereosinophilic syndromes (HES) [4]. The EGPA Consensus Task Force's first recommendations for EGPA diagnosis and individualised patient management Members of the EGPA Task Force and co-authors of the article cited in this editorial are: Chiara Baldini (Rheumatology Unit, Dept of Internal Medicine, University of Pisa, Pisa, Italy); Elisabeth Bel (Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands); Paolo Bottero (Allergy and Clinical Immunology Outpatient Clinic, Ospedale “G. Fornaroli” di Magenta, Azienda Ospedaliera di Legnano, Milan, Italy); Jean-François Cordier (Dept of Respiratory Medicine, National Referral Center for Rare Lung Diseases, Hôpital Louis-Pradel, Hospices Civils de Lyon, Lyon, France); Vincent Cottin (Dept of Respiratory Medicine, National Referral Center for Rare Lung Diseases, Hôpital Louis-Pradel, Hospices Civils de Lyon, Lyon, France); Klaus Dalhoff (Medical Clinic, Dept of Rheumatology, Vasculitis Center, University Clinic of Schleswig-Holstein, Lübeck and Bad Bramstedt, Germany); Bertrand Dunogué (Dept of Internal Medicine, National Referral Center for Rare Autoimmune and Systemic Diseases (Vasculitis, Scleroderma), INSERM U1016, Hôpital Cochin, APHP, Université Paris Descartes, Paris, France); Wolfgang Gross (Medical Clinic, Dept of Rheumatology, Vasculitis Center, University Clinic of Schleswig-Holstein, Lübeck and Bad Bramstedt, Germany); Julia Holle (Medical Clinic, Dept of Rheumatology, Vasculitis Center, University Clinic of Schleswig-Holstein, Lübeck and Bad Bramstedt, Germany); Marc Humbert (Dept of Respiratory and Critical Care Medicine, National Referral Center for Severe Pulmonary Hypertension, INSERM UMR-S 999, Hôpital Bicêtre, APHP, Université Paris-Sud, Le Kremlin-Bicêtre, France); David Jayne (Vasculitis and Lupus Clinic, Addenbrooke’s Hospital, Cambridge, UK); J. Charles Jennette (Dept of Pathology and Laboratory Medicine and UNC Kidney Center, University of North Carolina, Chapel Hill, NC, USA); Romain Lazor (Interstitial and Rare Lung Disease Unit, Dept of Respiratory Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland); Alfred Mahr (Dept of Internal Medicine, Hôpital Saint-Louis, Université Paris 7 René Diderot, Paris, France); Peter A. Merkel (Division of Rheumatology, University of Pennsylvania, Philadelphia, PA, USA); Luc Mouthon (Dept of Internal Medicine, National Referral Center for Rare Autoimmune and Systemic Diseases (Vasculitis, Scleroderma), INSERM U1016, Hôpital Cochin, APHP, Université Paris Descartes, Paris, France); Renato Alberto Sinico (Clinical Immunology Unit and Renal Unit, Dept of Medicine, Azienda Ospedaliera San Carlo Borromeo, Milan, Italy); Ulrich Specks (Division of Pulmonary and Critical Care Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA); Augusto Vaglio (Nephrology Unit, University Hospital of Parma, Parma, Italy); and Michael E. Wechsler (Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, CO, USA).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".