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Record W3119565724 · doi:10.1097/der.0000000000000658

Off-label Studies on the Use of Ruxolitinib in Dermatology

2021· article· en· W3119565724 on OpenAlexvenueno aff
Kyle Tegtmeyer, Mohan Ravi, Jeffrey Zhao, Nolan J. Maloney, Peter Lio

Bibliographic record

VenueDermatitis · 2021
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRuxolitinibMedicineAlopecia areataDermatologyClinical trialMyelofibrosisAtopic dermatitisRandomized controlled trialMEDLINEPsoriasisVitiligoOff-label useInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Ruxolitinib (Jakafi) is a Janus kinase 1 and 2 small molecule inhibitor that the Food and Drug Administration approved for myelofibrosis and polycythemia vera. It has been expanded to off-label treatment for a variety of dermatologic conditions, with several clinical trials ongoing. A review of available studies and cases of off-label uses was performed to guide clinicians seeking evidence on the efficacy of this Janus kinase inhibitor for dermatologic disorders. MATERIALS AND METHODS: PubMed/MEDLINE, EMBASE, Scopus, and ClinicalTrials.gov databases were searched with the term "ruxolitinib," and results were manually reviewed to identify published data on off-label uses of ruxolitinib. Studies included are structured by quality of evidence available. RESULTS: Ruxolitinib may have utility in the treatment of atopic dermatitis, psoriasis, and vitiligo, with data from open-label and randomized trials supporting efficacy of topical formulations. Evidence of utility for alopecia areata is mixed and differs depending on topical versus oral form. Evidence for numerous other conditions is available through case reports and case series. CONCLUSIONS: There is growing evidence supporting potential off-label use of oral and topical ruxolitinib for a wide range of skin conditions. There are several ongoing investigations of ruxolitinib use in dermatology that will undoubtedly better define its efficacy and appropriate use in dermatology.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.128
GPT teacher head0.338
Teacher spread0.210 · 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 designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

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