Interferon-α for Induction and Maintenance of Remission in Eosinophilic Granulomatosis with Polyangiitis: A Single-center Retrospective Observational Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by frequent relapses following induction therapy. Interferon-α (IFN-α) can reverse the underlying Th2-driven immune response and has successfully induced remission in previous reports. We undertook this study to investigate its efficacy and safety in patients with EGPA. METHODS: We conducted a retrospective monocentric cohort study including 30 patients (16 women) with active EGPA under IFN-α treatment. Primary endpoints were remission induction, occurrence of relapses, prednisolone (PSL) dosage at time of remission, and adverse events. Remission was defined by a Birmingham Vasculitis Activity Score (BVAS) of 0. Pulmonary function tests were recorded at baseline and at time of remission. Health-related quality of life was analyzed by questionnaire at baseline and following 12 months of treatment. RESULTS: At baseline, the median BVAS was 6 (interquartile range 4-13.5) and remission or partial response was achieved in 25/30 patients. After initiation of IFN-α treatment, the median PSL dosages could be reduced from 17.5 mg/day at baseline to 5.5 mg/day at time of remission. Following remission, 17 relapses (5 major) in 16 patients were observed. Pulmonary function tests improved and the time of hospitalization decreased. Adverse events at initiation of treatment were common, but mostly transient. Severe adverse events occurred during treatment in 4 patients (autoimmune hepatitis, n = 1; drug-induced neuropathy, n = 3). CONCLUSION: IFN-α treatment results in high rate of remission and maintenance in EGPA with significant reduction in oral corticosteroids, although reversible adverse events may occur. IFN-α represents an alternative therapeutic option in cases of refractory to standard treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".