Patient-Level, Visit-by-Visit Data Highlight the Extent of Skin and Itch Improvement in Atopic Dermatitis With Lebrikizumab
Bibliographic record
Abstract
■ Lebrikizumab is a monoclonal antibody that binds with high affinity and slow dissociation rate to IL-13, thereby blocking the downstream effects of IL-13 with high potency 1 ■ Lebrikizumab has demonstrated clinical benefit in patients with moderate-to-severe AD in the randomized, placebo-controlled, Phase 3 ADvocate1 (NCT04146363) and ADvocate2 (NCT04178967) trials 2,3 OBJECTIVE ■ To report individual patients' level, visit-by-visit, of response using EASI and Pruritus NRS evaluations over 52 weeks of treatment CONCLUSIONS ■ Based on individual patient data, lebrikizumab is an efficacious treatment for AD and shows stable improvements in skin and itch measures through 1 year ■ Some patients had deep improvements (eg, EASI 100, Pruritus NRS 0) during the Induction Period; many patients also maintained or improved their skin or itch outcomes with some achieving levels of deep improvement through 1 year METHODS Study Design: ADvocate1 and ADvocate2 ABBREVIATIONS AD=atopic dermatitis; BMI=body mass index; BSA=body surface area; EASI=Eczema Area and Severity Index; EASI 100=100% improvement from baseline in EASI; EASI 90/75/50=at least 90/75/50% improvement from baseline in EASI
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".