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Record W2944529732 · doi:10.1177/1203475419833609

The Use of Janus Kinase Inhibitors in Vitiligo: A Review of the Literature

2019· review· en· W2944529732 on OpenAlexaff
Nicole Relke, Melinda Gooderham

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsProbity Medical ResearchSKiN HealthQueen's University
Fundersnot available
KeywordsVitiligoMedicineJanus kinaseDermatologyDiseasestatImmunologyPathologySignal transductionCytokineSTAT3

Abstract

fetched live from OpenAlex

Vitiligo is a common acquired depigmenting disorder characterized by the development of white macules and patches due to the loss of melanocytes. Patients with vitiligo can be stigmatized by society, making the disease a source of psychological stress that can considerably affect quality of life. The goal of vitiligo treatment is to obtain skin repigmentation in the majority of cases, and less commonly to depigment the remaining normal skin. There is no consistent, long-term, durable therapy for vitiligo for all patients, highlighting the unmet need for new safe and effective therapies to control this disease. Recently, JAK inhibitors have been explored as a promising novel treatment option in vitiligo. The JAK and signal transducers and activators of transcription (STAT) pathway is an attractive therapeutic target because IFN-γ-dependent cytokines produced through this pathway have been implicated in the pathogenesis of disease. This literature review describes vitiligo pathophysiology, explains the usefulness of the JAK inhibitors for treatment, and summarizes published case reports, case series, and open-label studies. Research outlined here shows JAK inhibitors in patients with vitiligo have a favorable safety profile and effectively produce repigmentation of lesions, especially with concomitant ultraviolet exposure. Additional studies are required to confirm efficacy, establish safety, and investigate durability of repigmentation.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.317
Teacher spread0.265 · 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
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

Citations57
Published2019
Admission routes1
Has abstractyes

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