Real-World Effectiveness and Safety of Lebrikizumab for Moderate-to-Severe Atopic Dermatitis: A 16-Week Study in Japan
Bibliographic record
Abstract
Abstract: Background: Real-world data on the effectiveness and safety of lebrikizumab for atopic dermatitis (AD) are limited. Objective: To evaluate the real-world effectiveness and safety of lebrikizumab on Japanese patients with AD. Methods: This two-center study included 126 Japanese patients with moderate-to-severe AD treated with lebrikizumab plus topical corticosteroids for 16 weeks. Eczema area and severity index (EASI), investigator’s global assessment (IGA), peak-pruritus (PP)-numerical rating scale (NRS), sleep quality NRS, AD control tool (ADCT), dermatology life quality index (DLQI), patient-oriented eczema measure (POEM), immunoglobulin E (IgE), thymus and activation-regulated chemokine (TARC), lactate dehydrogenase (LDH), and total eosinophil count (TEC) were assessed during the treatment. Results: Lebrikizumab reduced all clinical indexes at week 4, which was maintained until week 16. The achievement rates of EASI 50, 75, 90, 100, and IGA 0/1 at week 16 were 83.1%, 57.1%, 27.3%, 11.7%, and 33.3%, respectively. The achievement rates of ≥4-point reduction in PP-NRS, sleep quality NRS, or DLQI, ADCT <7-point, and POEM ≤7-point at week 16 were 75.9%, 68.8%, 65.9%, 76.9%, and 80.4%, respectively. IgE, TARC, and LDH decreased while TEC increased during the treatment. No new safety concerns were observed. Conclusion: The 16-week treatment with lebrikizumab generated favorable effectiveness and safety in Japanese AD patients in real-world practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".