Optimal therapy and prospects for new medicines in eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome)
Bibliographic record
Abstract
INTRODUCTION: The prevalence of eosinophilic granulomatosis with polyangiitis (EGPA; previously known as Churg-Strauss syndrome) is lower than that of other antineutrophil cytoplasm antibody (ANCA)-associated vasculitides (AAV's), and only a few randomized controlled trials have been conducted for this rare disease. However, recent international efforts have helped delineate the best treatment approach. AREAS COVERED: At present, EGPA conventional therapy is by default similar to that of other AAVs. Limited, non-severe EGPA can initially be treated with glucocorticoids (GCs) alone. Patients with life-threatening manifestations and/or major organ involvement must receive a combination of GCs and an immunosuppressant, mainly cyclophosphamide. Remission can be achieved in >85% of patients with these first-line treatments, but vasculitis relapses occur in more than one-third of patients, and about 85% cannot stop GC treatment because of GC-dependent asthma and/or ENT manifestations. A few biologic agents, including rituximab or mepolizumab, are now under investigation after interesting preliminary results. Expert commentary: Treatment for EGPA still has several unmet needs. Several biologic agents are now under investigation in randomized controlled trials, but a few others should be considered soon. Their benefit should be demonstrated for devising more EGPA-tailored therapeutic strategies (ideally GC-free).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".