A practical guide to using oral Janus kinase inhibitors for atopic dermatitis from the International Eczema Council
Bibliographic record
Abstract
BACKGROUND: Janus kinase inhibitors (JAKi) have the potential to alter the landscape of atopic dermatitis (AD) management dramatically, owing to promising efficacy results from phase III trials and their rapid onset of action. However, JAKi are not without risk, and their use is not appropriate for all patients with AD, making this a medication class that dermatologists should understand and consider when treating patients with moderate-to-severe AD. OBJECTIVES: To provide a consensus expert opinion statement from the International Eczema Council (IEC) that provides a pragmatic approach to prescribing JAKi, including choosing appropriate patients and dosing, clinical and laboratory monitoring and advice about long-term use. METHODS: An international cohort of authors from the IEC with expertise in JAKi selected topics of interest were placed 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 that JAKi be considered for patients with moderate-to-severe AD seeking the benefits of a rapid reduction in disease burden and itch, oral administration and the potential for flexible dosing. Baseline risk factors should be assessed prior to prescribing JAKi, including increasing age, venous thromboembolisms, malignancy, cardiovascular health, kidney/liver function, pregnancy and lactation, and immunocompetence. Patients being considered for JAKi treatment should be current on vaccinations and we provide a generalized framework for laboratory monitoring, although clinicians should consult individual product labels for recommendations as there are variations among the different JAKi. 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 patients with AD to assess the durability and safety of continuous long-term JAKi use, combination medication regimens and the effects of flexible, episodic treatment over time. CONCLUSIONS: The decision to initiate JAKi treatment should be shared between the 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".