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Record W4407766219 · doi:10.1089/derm.2025.0004

Real-World Effectiveness and Safety of Lebrikizumab for Moderate-to-Severe Atopic Dermatitis: A 16-Week Study in Japan

2025· article· en· W4407766219 on OpenAlexvenueno aff
Teppei Hagino, Akihiko Uchiyama, Marina Onda, Keiji Kosaka, Takeshi Araki, Sei‐ichiro Motegi, Hidehisa Saeki, Eita Fujimoto, Naoko Kanda

Bibliographic record

VenueDermatitis · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtopic dermatitisDermatology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.290
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2025
Admission routes1
Has abstractyes

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