Predictive Factors for Poor Responders to Tralokinumab in Moderate-to-Severe Atopic Dermatitis: A Real-World Analysis
Bibliographic record
Abstract
Abstracts: Background: Some patients with moderate-to-severe atopic dermatitis (AD) show insufficient response to treatment with tralokinumab, an anti-interleukin-13 antibody. Identifying predictive factors for poor responders to tralokinumab can help optimize treatment strategies for AD patients. Objective: To identify predictive factors for poor responders to tralokinumab, defined as an investigator’s global assessment >2 at week 12 or 24. Methods: A prospective study was conducted from October 2023 to August 2024, including 109 Japanese patients with moderate-to-severe AD. Baseline features were compared between poor responders versus responders at week 12 or 24. Results: Poor responders at week 12 showed higher baseline eczema area and severity index (EASI), lactate dehydrogenase (LDH), and eosinophil-to-lymphocyte ratio (ELR) compared with responders. Poor responders at week 24 had older age, longer disease duration, and higher proportions of previous systemic therapies, previous dupilumab, or previous 15 mg upadacitinib treatment, compared with responders. Conclusions: Higher baseline EASI, LDH, and ELR may predict poor response to tralokinumab at week 12. Older age, longer disease duration, and previous usage of systemic therapy, dupilumab, or 15 mg upadacitinib may predict poor response to tralokinumab at week 24. AD patients with the above features may as well avoid tralokinumab treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".