Long-term efficacy of anti-IL-4 receptor antibody in a patient with aspirin-exacerbated respiratory disease and IgG4-related disease
Bibliographic record
Abstract
BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) and IgG4-related disease (IgG4RD) share a common pathway of Th2-mediated immune mechanism; there have been several cases of IgG4RD developed in patients with asthma, especially in those comorbid with chronic rhinosinusitis (CRS). IgG4RD has often been treated with systemic corticosteroids, rituximab, or immune-suppressive agents, but frequently failed with relapse. CASE PRESENTATION: Here, we present a case of a 64-year-old male patient with severe AERD with CRS complicated with IgG4RD, who has been successfully treated and maintained with anti-IL-4 receptor antibody, dupilumab after achieving unsatisfactory responses with previous treatments including steroids, rituximab, omalizumab, and reslizumab. The patient's symptoms (periorbital swelling and asthmatic/nasal symptoms) were remarkably improved; serum levels of IgG4/IgE as well as plasmablast/eosinophil counts progressively decreased without any recurrence sign for over 2 years of dupilumab treatment. CONCLUSION: These findings demonstrate that blocking the IL-4/IL-13 pathway with dupilumab can be an effective treatment with long-term safety in patients with severe AERD with CRS complicated by IgG4RD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".