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Record W4212817779 · doi:10.3899/jrheum.211321

Basic Science Session 2. Recent Advances in Our Understanding of Psoriatic Arthritis Pathogenesis

2022· article· en· W4212817779 on OpenAlexvenueno aff
Erik Lubberts, José U. Scher, Oliver FitzGerald

Bibliographic record

VenueThe Journal of Rheumatology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
FundersCelgeneSanofiPfizerEli Lilly and CompanyAmgen
KeywordsPsoriatic arthritisMedicinePsoriasisPathogenesisImmunologyRheumatoid arthritisMicrobiomeCD8BioinformaticsAntigenBiology

Abstract

fetched live from OpenAlex

The second basic science session at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) annual meeting focused on 2 recent publications that have increased our understanding of the pathogenesis of psoriatic arthritis (PsA). Data from the first publication, presented by Prof. Erik Lubberts, showed that interleukin (IL)-17A is produced by CD4+ and not CD8+ T cells in PsA synovial fluid following T cell receptor activation. These findings contrast with previously published data, which had suggested that CD8+ T cells are a prominent source of IL-17A. In further experiments, they showed that when CD8+ T cells were stimulated with paramethoxyamphetamine/ionomycin, relatively high levels of IL-17A were detected. Prof. Jose Scher presented work on the role of the microbiome in PsA and more specifically, on pharmacomicrobiomics. He demonstrated the baseline collection of genomes and genes from the microbiota community (the metagenome) can be used as predictor for future treatment response in early rheumatoid arthritis and also likely in PsA.

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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0640.022

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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
GenreOther

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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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