Evidence-Based Guideline for the diagnosis and management of eosinophilic granulomatosis with polyangiitis
Bibliographic record
Abstract
Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, characterized by asthma, eosinophilia and granulomatous or vasculitic involvement of several organs. The diagnosis and management of EGPA are often challenging and require an integrated, multidisciplinary approach. Current practice relies on recommendations and guidelines addressing the management of ANCA-associated vasculitis and not specifically developed for EGPA. Here, we present evidence-based, cross-discipline guidelines for the diagnosis and management of EGPA that reflect the substantial advances that have been made in the past few years in understanding the pathogenesis, clinical subphenotypes and differential diagnosis of the disease, as well as the availability of new treatment options. Developed by a panel of European experts on the basis of literature reviews and, where appropriate, expert opinion, the 16 statements and five overarching principles cover the diagnosis and staging, treatment, outcome and follow-up of EGPA. These recommendations are primarily intended to be used by healthcare professionals, pharmaceutical industries and drug regulatory authorities, to guide clinical practice and decision-making in EGPA. These guidelines are not intended to limit access to medications by healthcare agencies, nor to impose a fixed order on medication use. This article presents the first Evidence-Based Guideline dedicated specifically to the diagnosis and management of eosinophilic granulomatosis with polyangiitis. The 16 statements and five overarching principles cover the diagnosis and staging, treatment, outcome and follow-up of eosinophilic granulomatosis with polyangiitis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".