Predictive Factors for Poor Responders to Lebrikizumab in Real-World Treatment for Atopic Dermatitis: Transition of Clinical and Laboratory Indexes
Bibliographic record
Abstract
Abstract: Background: Some patients with atopic dermatitis (AD) do not sufficiently respond to anti-interleukin-13 antibody lebrikizumab in real-world practice. Identifying predictive factors for poor responders to lebrikizumab is important to optimize treatment strategies for AD. Objective: To clarify predictive factors for poor responders to lebrikizumab, defined as patients with Investigator’s Global Assessment >2 at week 12 or 24 of treatment in real-world practice. Methods: From May 2024 to April 2025, we conducted a prospective study in 124 Japanese AD patients treated with lebrikizumab. The transition of clinical and laboratory indexes was evaluated during 24-week lebrikizumab treatment, stratified by poor responders versus responders. We compared baseline characteristics between week 12 and 24 poor responders versus respective responders. Results: Both week 12 and 24 responders showed lower magnitudes of decreasing Eczema Area and Severity Index (EASI) throughout 24-week treatment compared with respective responders. Lactate dehydrogenase levels in both week 12 and 24 responders significantly decreased and plateaued at week 4, while there was no significant decrease in both week 12 and 24 poor responders. Total eosinophil count in both week 12 and 24 responders slightly increased at week 4 and 12, while there was no significant increase in both week 12 and 24 poor responders. Week 12 poor responders had higher baseline EASI compared with responders. Week 24 poor responders had higher body mass index (BMI) compared with responders. Conclusions: Higher baseline EASI may predict week 12 poor responders to lebrikizumab, while higher BMI may predict week 24 poor responders. These baseline characteristics could help optimize treatment for AD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".