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Record W4416535253 · doi:10.1016/j.jaip.2025.10.052

Systemic Therapy for Atopic Dermatitis: Choosing Biologics or Janus Kinase Inhibitors for Children and Adults

2025· article· en· W4416535253 on OpenAlexafffund
I M Scholl, Aaron M. Drucker, Carsten Flohr, L.A. Gerbens

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsWomen's College Hospital
FundersNational Institutes of HealthAgency for Science, Technology and ResearchAmsterdam University Medical CentersCanadian Dermatology FoundationPhysicians' Services Incorporated FoundationNational Eczema AssociationCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchAmerican Academy of Dermatology
KeywordsAtopic dermatitisJanus kinaseDosingSystemic therapyPopulationBiologic AgentsQuality of life (healthcare)

Abstract

fetched live from OpenAlex

Atopic dermatitis is a highly prevalent chronic inflammatory skin disease worldwide, with a significant burden on patients' quality of life. Although most patients with atopic dermatitis are effectively managed with topical treatments, a significant minority requires systemic therapy. In recent years, the therapeutic landscape for this patient population has expanded beyond conventional treatments, introducing novel targeted therapies such as biologic agents and Janus kinase inhibitors. These novel therapies vary in aspects such as efficacy, safety profile, route of administration, monitoring requirements, and impact on comorbidities, necessitating individualized treatment decision-making. As a result, selecting the most appropriate therapy can be challenging. This article provides a practical overview of the current targeted therapies to support informed clinical decision-making regarding treatment selection, monitoring, and dosing considerations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.020
GPT teacher head0.357
Teacher spread0.337 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations2
Published2025
Admission routes2
Has abstractyes

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Same venueThe Journal of Allergy and Clinical Immunology In PracticeSame topicDermatology and Skin DiseasesFrench-language works237,207