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Record W4402348974 · doi:10.1093/bjd/ljae342

A practical guide to using oral Janus kinase inhibitors for atopic dermatitis from the International Eczema Council

2024· article· en· W4402348974 on OpenAlexafffund
Carter Haag, Andrew Alexis, Valéria Aoki, Robert Bissonnette, Andrew Blauvelt, Raj Chovatiya, Michael J. Cork, Simon G. Danby, Lawrence F. Eichenfield, Kilian Eyerich, Melinda Gooderham, Emma Guttman‐Yassky, DirkJan Hijnen, Alan D. Irvine, Norito Katoh, Dédée F. Murrell, Yael A. Leshem, Adriane A. Levin, Ida Vittrup, Jill I. Olydam, Raquel Leão Orfali, Amy S. Paller, Yael Renert‐Yuval, David Rosmarin, Jonathan I. Silverberg, Jacob P. Thyssen, Sonja Ständer, Nick Stefanovic, Gail Todd, JiaDe Yu, Eric L. Simpson

Bibliographic record

VenueBritish Journal of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsQueen's UniversityInnovaderm (Canada)
FundersEli Lilly JapanSanofi GenzymeOtsuka PharmaceuticalGenentechNational Jewish HealthTorii PharmaceuticalKiniksa PharmaceuticalsTaiho PharmaceuticalKyowa Kirin Pharmaceutical DevelopmentCastle BiosciencesCelgeneBioCrystSun PharmaDermiraValeant Pharmaceuticals InternationalPfizerIncyteRegeneron PharmaceuticalsGaldermaBristol-Myers SquibbEli Lilly and CompanyAstraZenecaCSL BehringLEO PharmaGilead SciencesSanofiGlaxoSmithKlineAmgen
KeywordsMedicineAtopic dermatitisDosingPharmacovigilanceOmalizumabAdverse effectMEDLINEExpert opinionClinical trialIntensive care medicineInternal medicineDermatologyImmunology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.345
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2024
Admission routes2
Has abstractyes

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