JAK-1 inhibitors in the management of atopic dermatitis in patients over 65 years old: a descriptive cohort study
Bibliographic record
Abstract
Atopic dermatitis (AD) can affect older adults. Janus kinase inhibitors (JAK-1 inhibitors), such as upadacitinib and abrocitinib, have emerged as effective treatments for moderate-to-severe AD. However, data on their safety and efficacy in older adults remain limited due to underrepresentation in clinical trials. This retrospective cross-sectional study examined the efficacy and safety of oral JAK-1 inhibitors in patients aged 65 years or older. Data was extracted from electronic medical records. Disease control was assessed using the Atopic Dermatitis Control Tool (ADCT), while adverse events were analyzed. Statistical comparisons were conducted using the Wilcoxon Signed-Rank Test. The study included 31 patients. Thirteen patients (42%) received abrocitinib, and 18 (58%) upadacitinib. The mean reduction in ADCT scores was − 84.4%, with a slightly greater improvement observed in abrocitinib users (-5.1%, p < 0.001). Mild adverse events were reported in 21 patients (68%), while 26% experienced no adverse effects. Two patients discontinued treatment due to adverse events. Both upadacitinib and abrocitinib demonstrated substantial efficacy in achieving long term AD control among older adults, with manageable safety profiles. These findings highlight the potential of JAK-1 inhibitors in this population while underscoring the need for personalized treatment approaches and further research in larger cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".