Predictive Factors for Long-Term High Responders to Upadacitinib Treatment in Patients with Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: Upadacitinib, a Janus kinase 1 inhibitor, is an effective medicine for moderate-to-severe atopic dermatitis (AD). Identifying long-term responders to upadacitinib is crucial for optimal treatment strategies in real-world clinical practice. To identify predictive factors for long-term high responders to upadacitinib 15 mg or 30 mg, defined as achievers of investigator’s global assessment (IGA) 0/1 with ≥2-point improvement from baseline IGA at week 48. Methods: A retrospective study was conducted from August 2021 to September 2023 on 63 AD patients treated with upadacitinib 15 mg and 31 patients with 30 mg. Patients of each group were categorized into long-term high responders (achievers of IGA 0/1 at week 48) and low responders (non-achievers). We compared baseline values of clinical indexes and laboratory parameters between long-term responders and nonresponders. Results: In 15 mg group, long-term high responders showed lower rate of bronchial asthma (BA), lower values of baseline eczema area and severity index (EASI) of head and neck, IgE, and systemic inflammatory response index (SIRI) compared with low responders. In 30 mg group, long-term high responders showed lower baseline levels of IgE compared with low responders. Conclusion: Patients with lower baseline EASI of head and neck, IgE, or SIRI or without BA and those with lower baseline IgE may have a higher potential to become long-term high responders to upadacitinib 15 mg and 30 mg treatment, respectively.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".