Dupilumab in patients with chronic spontaneous urticaria (LIBERTY-CSU CUPID): Two randomized, double-blind, placebo-controlled, phase 3 trials
Bibliographic record
Abstract
BACKGROUND: antihistamines (H1-AH) or anti-IgE (omalizumab) treatment. Dupilumab blocks IL-4/IL-13 signaling and is approved for multiple type 2/atopic indications. OBJECTIVE: We conducted two phase 3, randomized, placebo-controlled, double-blind trials comparing dupilumab with placebo in patients with symptomatic CSU despite H1-AH. METHODS: In LIBERTY-CSU CUPID Study A, patients were omalizumab-naive (n = 138, aged ≥6 years). In Study B, patients were omalizumab-intolerant/incomplete responders (n = 108, aged ≥12 years). The primary end point was either change from baseline over 7 days in the Urticaria Activity Score (UAS7) or Itch Severity Score (ISS7) at week 24, with the other as a key secondary end point, depending on regional regulatory requirements. Studies were pooled for safety assessment. RESULTS: In Study A, UAS7 and ISS7 improved with dupilumab versus placebo (difference -8.5 [95% CI, -13.2 to -3.9; P = .0003] and -4.2 [95% CI, -6.6 to -1.8; P = .0005]). In Study B, tested at α = 0.043 after interim analysis, UAS7 improved (difference -5.8 [95% CI, -11.4 to -0.3; P = .0390]), with a numerical trend in ISS7 (difference -2.9 [95% CI, -5.7 to -0.07; nominal P = .0449, not significant]). Pooled safety data were consistent between dupilumab and placebo and with the known dupilumab safety profile. CONCLUSIONS: Dupilumab reduced urticaria activity by reducing itch and hives severity in omalizumab-naive patients with CSU uncontrolled with H1-AH. Although the primary end point for Study B was not met, dupilumab effects were small in patients who were omalizumab-intolerant/incomplete responders.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".