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Record W2914339882 · doi:10.3899/jrheum.190119

The Genetics of Psoriasis and Psoriatic Arthritis

2019· article· en· W2914339882 on OpenAlexaffvenue
Darren D. O’Rielly, Meghna Jani, Proton Rahman, James T. Elder

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsMemorial University of Newfoundland
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute for Health and Care Research
KeywordsPsoriatic arthritisPsoriasisMedicineArthritisDiseaseGenome-wide association studyImmunologyGenetic predispositionPopulationGeneticsInternal medicineGenotypeSingle-nucleotide polymorphismBiologyGene

Abstract

fetched live from OpenAlex

Psoriatic arthritis (PsA) is an inflammatory arthritis that manifests in 20-30% of patients diagnosed with psoriasis. Epidemiologic studies suggest a substantial genetic contribution to PsA. There is a strong need for genome-wide association studies on patients with PsA, including PsA-weighted or specific variants, and a need for a better understanding of the relevance of HLA alleles in disease expression. Interferon signaling and the nuclear factor-B cascade are involved in PsA, and there are genetic differences between purely cutaneous psoriasis (PsC) and PsA. Psoriasis susceptibility genes for which putative functional coding variants in TYK2 and TRAF3IP2 are strongly associated with PsC and PsA, and neutrophil extracellular traps promote Th17 induction in an Act1 D10N-dependent fashion. Genomics and serological factors may also predict treatment response in tumor necrosis factor inhibitors (TNFi) in PsA, and genetics may play a role in treatment response to TNFi. Collaborations through the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) are essential to increase study population size, which will enhance the ability to detect the genetic variants that create a predisposition to psoriatic disease and to predict response to biological therapy. (

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations60
Published2019
Admission routes2
Has abstractyes

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