The Transition of Blood Biomarkers During 96-Week Treatment with Upadacitinib for Atopic Dermatitis: A Real-World Analysis Stratified by Age
Bibliographic record
Abstract
Abstract: Background: The transition of laboratory indexes is unknown for long-term (2-year) upadacitinib treatment in atopic dermatitis (AD). Objective: To assess the 96-week real-world effects of upadacitinib on the transition of immunoglobulin E (IgE), thymus and activation-regulated chemokine (TARC), lactate dehydrogenase (LDH), and total eosinophil count (TEC) in Japanese patients with AD, stratified by age groups (<18, 18–64, and ≥65 years). Methods: In this prospective, single-center study, patients received upadacitinib 15 mg or 30 mg plus topical corticosteroids from August 2021 to November 2024. Laboratory indexes (IgE, TARC, LDH, and TEC) and clinical indexes (eczema area and severity index, peak pruritus-numerical rating scale) were measured at weeks 0, 4, 12, 24, 36, 48, 60, 72, 84, and 96. Results: Upadacitinib 15 and 30 mg generated a sustained reduction of clinical indexes until week 96 in all age groups. TEC decreased by week 4 or 12 and remained below baseline in all age groups. Patients aged ≥65 years maintained the lowest TEC and clinical indexes among the three age groups. Conclusion: Upadacitinib provided long-term reduction of TEC across all age groups in parallel with reduced clinical indexes of AD. TEC may act as a potential biomarker reflecting treatment responsiveness to upadacitinib.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".