Transition of Blood Biomarkers During 24-Week Treatment With Lebrikizumab or Tralokinumab in Atopic Dermatitis: A Real-World Analysis Stratified by Prior Systemic Therapy
Bibliographic record
Abstract
Abstract: Background: The transition of blood biomarkers has not been precisely examined in real-world treatments with anti-interleukin (IL)-13 antibodies for atopic dermatitis (AD). Objective: To evaluate the transition of blood biomarkers during 24-week treatment with lebrikizumab or tralokinumab for patients with AD in real-world settings, stratified by the presence or absence of prior systemic therapy. Methods: We conducted a retrospective study of Japanese patients with AD who received lebrikizumab ( n = 148) or tralokinumab ( n = 173). We measured serum immunoglobulin E (IgE), thymus and activation-regulated chemokine (TARC), lactate dehydrogenase (LDH), and total eosinophil count (TEC) at weeks 0, 4, 12, and 24 in systemic therapy-naïve or -experienced patients. Results: IgE, TARC, and LDH decreased throughout the 24-week treatment with lebrikizumab or tralokinumab, while TEC transiently increased at week 4 or 12 in both systemic therapy-naïve and -experienced patients, and the magnitudes of decreasing IgE and TARC or increasing TEC were higher in the latter. Conclusion: IgE, TARC, and LDH decreased during 24-week treatment with lebrikizumab or tralokinumab, while TEC transiently increased at week 4 or 12 in both systemic therapy- naïve and -experienced patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".