A practical guide to using oral JAK inhibitors for atopic dermatitis from the International Eczema Council
Bibliographic record
Abstract
Background Janus kinase inhibitors (JAKinibs) have the potential to dramatically alter the landscape of atopic dermatitis (AD) management due to their promising efficacy results from phase 3 trials and rapid onset of action. However, JAKinibs are not without risk, and their use is not appropriate for all AD patients, making this a medication class that dermatologists should understand and consider when treating patients with moderate-to-severe AD. Objective This consensus expert opinion statement from the International Eczema Council (IEC) provides a pragmatic approach to prescribing JAKinibs, including choosing appropriate patients, dosing, clinical and lab monitoring, as well as long-term use. Methods An international cohort of authors from the IEC with expertise in JAKinibs selected topics of interest and were formed into authorship groups covering 10 subsections. The groups performed topic-specific literature reviews, consulted up-to-date adverse event (AE) data, referred to product labels and provided analysis and expert opinion. The manuscript guidance and recommendations were reviewed by all authors as well as the IEC Research Committee. Results We recommend JAKinibs be considered for patients with moderate to severe AD seeking the benefits of rapid reduction in disease burden and itch, oral administration, and the potential for flexible dosing. Baseline risk factors should be assessed prior to prescribing JAKinibs, including increasing age, venous thromboembolisms, malignancy, cardiovascular health, kidney/liver function, pregnancy and lactation, and immunocompetence. Patients being considered for JAKinib therapy should be current on vaccinations and we provide a generalized framework for laboratory monitoring, though clinicians should consult individual product labels for recommendations as there are variations among the JAKinib class. Patients who achieve disease control should be maintained on the lowest possible dose, as many of the observed AEs occurred in a dose-dependent manner. Future studies are needed in AD patients to assess the durability and safety of continuous long-term use of JAKinibs, combination medication regimens, and the effects of flexible, episodic treatment over time. Conclusions The decision to initiate a JAKinib should be shared among patient and provider, accounting for AD severity and personal risk/benefit assessment, including consideration of baseline health risk factors, monitoring requirements and treatment costs.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.093 | 0.100 |
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".