Identification of Early and Late Responders to Anti-IL-13 Antibody Tralokinumab in Atopic Dermatitis: A Real-World Japanese Study
Bibliographic record
Abstract
Abstract: Background: Tralokinumab, an anti-IL-13 antibody, is an effective treatment for patients with atopic dermatitis (AD). However, predictive factors for responders to tralokinumab remain unclear in real-world practice. Objective: This study aimed to identify predictive factors for early and late responders to tralokinumab treatment. Early responders were defined as patients achieving investigator’s global assessment (IGA) 0/1 at week 12, whereas late responders were defined as those without IGA 0/1 at week 12 but achieving IGA 0/1 at week 24. Methods: A prospective study was conducted with 108 Japanese AD patients treated with tralokinumab between October 2023 and August 2024. Patients’ background factors and baseline clinical or laboratory indexes were compared between responders and poor responders. Results: Both early and late responders had a higher proportion of systemic therapy-naive patients compared with poor responders. Early responders had higher proportion of females, younger age, shorter disease duration, lower body mass index, and monocyte-to-lymphocyte ratio, whereas late responders had lower immunoglobulin E, thymus and activation-regulated chemokine, platelet-to-lymphocyte ratio, and C-reactive protein compared with poor responders. Conclusions: This study provides valuable insights for optimizing treatment strategies in AD, in selecting patients who may respond to tralokinumab at early or late phases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".