Effectiveness of Tralokinumab for Different Anatomical Sites and Clinical Signs in Atopic Dermatitis: A 36-Week Real-World Study
Bibliographic record
Abstract
Abstract: Background: An anti-interleukin-13 antibody tralokinumab is effective for atopic dermatitis (AD), but its effectiveness on different anatomical sites and clinical signs remains unclear. Objective: To assess the effectiveness of tralokinumab on different anatomical sites and clinical signs of AD. Methods: This study included 129 moderate-to-severe AD patients treated with tralokinumab for 36 weeks. Eczema Area and Severity Index (EASI) scores were analyzed on four anatomical sites (head/neck, trunk, upper, and lower limbs) and four clinical signs (erythema, edema/papulation, excoriation, and lichenification) at weeks 0, 4, 12, 24, and 36. Results: Tralokinumab consistently reduced EASI scores on 4 anatomical sites and 4 clinical signs. The magnitude of decreasing EASI appeared highest on lower limbs while the achievement rates of EASI 75 at week 36 on 4 anatomical sites were mostly similar (72.6–77.6%). The magnitude of decreasing EASI and achieving EASI 75 or 100 appeared highest for excoriation, and the rates of EASI 75 at week 36 for erythema, excoriation, lichenification and edema/papulation were 71.1%, 69.4%, 68.4%, and 60.5%, respectively. Conclusions: Tralokinumab reduced EASI scores across various anatomical sites and clinical signs in moderate-to-severe AD patients. These findings suggest that tralokinumab may be widely useful for diverse skin manifestations of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".