Effectiveness and safety of dupilumab and JAK-inhibitors in daily practice
Bibliographic record
Abstract
Atopic dermatitis (AD) is one of the most common chronic inflammatory skin diseases worldwide. Until recently, adequate systemic treatment options for patients with moderate-to-severe AD were limited. Due to the increased understanding of the underlying pathophysiology of AD in the past decade, more targeted therapies for moderate-to-severe AD have been developed that address this unmet need. The advent of these new advanced systemic drugs since 2017, including biologics (i.e. dupilumab and tralokinumab) and Janus kinase (JAK)-inhibitors (i.e. baricitinib, upadacitinib and abrocitinib), therefore represents an important advancement in the therapeutic landscape for AD. In this thesis, including data from the real-life Dutch BioDay registry, we demonstrate that dupilumab treatment resulted in a significant improvement of clinical and patient-reported outcomes together with a significant decrease in severity-associated serum biomarkers and alterations in the skin microbiome moving it towards healthy skin. Effectiveness of dupilumab sustained even in the long-term use in both pediatric, adult and elderly patients as well as patients with or without pathogenic filaggrin variants, while the majority of patients was able to prolong their dosing interval. The JAK-inhibitors also showed to be effective treatment options for adult patients with moderate-to-severe AD, with upadacitinib and abrocitinib providing the highest effectiveness rates based on both physician and patients’ perspectives. Safety assessments showed that dupilumab has a favorable long-term safety profile, while the JAK-inhibitors still need to prove their safety in the long-term use.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".