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Record W2441495064 · doi:10.15761/god.1000160

HLA-Cw6 status predicts efficacy of biologic treatments in psoriasis patients

2016· article· en· W2441495064 on OpenAlexaff
Wayne Gulliver, Heather M. Young, Susanne Gulliver, Shane Randell

Bibliographic record

VenueGlobal Dermatology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPsoriasisImmunologyHuman leukocyte antigenMedicineComputational biologyBiologyAntigen

Abstract

fetched live from OpenAlex

Over the past decade, biologic therapies have been developed to treat auto-inflammatory conditions such as psoriasis. They have the advantage of better target specificity than traditional systemics such as methotrexate and cyclosporine and therefore significantly reduce side-effects and toxicity associated with wide spread systemic treatments. It has been suggested that the efficacy of biologics used in the treatment of psoriasis may be related with HLA-Cw6 status.Using HLA-Cw6 as a biomarker would therefore provide an advantage in the selection of a biologic agent for successful treatment based on a patient's genetic makeup and thus allowing us to use HLA-Cw6 to individualize therapy for patients with moderate-to-severe psoriasis.In the present study, the HLA-Cw6 status was determined for psoriasis patients previously treated with etanercept, adalimumab, efalizumab, infliximab or ustekinumab.The success or failure rates of the biologic treatments were compared for patients with and without the HLA-Cw6 allele.The HLA-Cw6 status was significantly associated to the treatment outcomes for biologics efalizumab (no longer on the market), infliximab and ustekinumab; but not etanercept or adalimumab.These results support the use of HLA-Cw6 status as a biomarker for biologic treatment in moderate-to-severe psoriasis patients.

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)0.000
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 score0.071
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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