Facial Rash Events in Dupilumab Phase 3 Atopic Dermatitis Trials: A Pooled Analysis
Bibliographic record
Abstract
Abstract: Background: Since dupilumab approval for moderate-to-severe atopic dermatitis (AD) in 2017, facial rash reports appeared in literature. Objective: To report facial rash treatment-emergent adverse events (TEAEs) incidence in dupilumab clinical trials for moderate-to-severe AD. Methods: Data were pooled from 14 randomized, placebo-controlled dupilumab clinical trials in pediatric (6–17 years) and adult (≥18 years) patients and were searched for facial rash TEAEs occurring during study treatment period, focusing on Medical Dictionary for Regulatory Activities (MedDRA) Lowest Level Terms (LLTs). Results: Pooled dataset included 1349 placebo-treated and 2615 dupilumab-treated patients. Facial rash incidence was low but slightly higher in dupilumab (dupilumab: 25 [1.0%]; placebo: 9 [0.7%] patients). The following LLT rates were numerically higher in dupilumab than placebo-treated patients: Erythema facial (7 [0.3%, 0.8 number of events (nE)/100 patient-years (PY)] vs 1 [<0.1%, 0.1 nE/100PY]); Redness facial (2 [<0.1%, 0.2 nE/100PY] vs 0); and rash on face (5 [0.2%, 0.4 nE/100PY] vs 1 [<0.1%, 0.1 nE/100PY]). Most cases were mild-to-moderate and recovered/resolved during study period; none led to treatment discontinuation. Conclusions: Facial rash analysis focused on MedDRA LLTs in dupilumab clinical trials for moderate-to-severe AD showed a small increase in dupilumab-treated patients compared with placebo. Most were mild-to-moderate and not treatment-limiting.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".