Facial erythema after the treatment of dupilumab in SLE patient
Bibliographic record
Abstract
BACKGROUND: Dupilumab is a receptor antagonist binding to the alpha subunit of the interleukin-4 receptor. Through binding to it, dupilumab inhibits signaling of both IL-4 and IL-13, the representative Th2 biomarkers. Recently, in addition to the treatment effects for atopic dermatitis (AD), there is an emerging adverse event as facial erythema. CASE PRESENTATION: A twenty-seven-year-old female patient developed erythema and desquamation on the face and neck after dupilumab administration. She had AD on her arms, legs, and trunk before the treatment but there was no atopic clinical feature in her face and neck. With the treatment of dupilumab, her skin lesions of the body have improved from the beginning of the treatment. In the patch test, including dupilumab, there was no specific finding other than the 1+ response to neomycin on day 2. In the intradermal test to dupilumab, a positive result was observed 15 min later, but negative both days 1 and 2. The blood examination showed an elevation of both ANA as 1:80 and anti-phospholipid antibodies (Anti-cardiolipin IgM, IgG, and Anti- beta 2 GPI IgG). She was diagnosed with Systemic lupus erythematosus (SLE) based on diagnostic criteria by a rheumatologist. CONCLUSION: Dupilumab is an emerging therapeutic agent for AD, and treatment cases are increasing in Korea. However, there are several adverse events during the treatment of dupilumab. Herein, we report the unexpected adverse event during the treatment of dupilumab in SLE patients.
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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) | 0.000 | 0.001 |
| 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) | 0.002 | 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".