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Record W4400654904 · doi:10.3899/jrheum.2024-0436

GRAPPA 2023 Collaborative Research Network Meeting

2024· article· en· W4400654904 on OpenAlexafffundvenue
Vinod Chandran, Dafna D. Gladman, Philip J. Mease, Lihi Eder, Stephen R. Pennington, Oliver FitzGerald, Christopher T. Ritchlin, Dan E. Webster, Kurt de Vlam, Wilson Liao

Bibliographic record

VenueThe Journal of Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsWomen's College HospitalToronto Western HospitalUniversity Health Network
FundersSun PharmaUniversity of TorontoDepartment of Medicine, University of TorontoEuropean Federation of Pharmaceutical Industries and AssociationsRegeneron PharmaceuticalsEuropean CommissionAmgenPfizerAstraZenecaEli Lilly and Company
KeywordsPsoriatic arthritisPsoriasisEnthesitisMedicineDermatologyMedical physics

Abstract

fetched live from OpenAlex

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Collaborative Research Network (CRN)/research committee met during the GRAPPA 2023 annual meeting. Updates were provided on GRAPPA research projects, including the Axial Involvement in Psoriatic Arthritis (AXIS), Axial Psoriatic Arthritis Molecular and Clinical Characterisation Study, Diagnostic Ultrasound Enthesitis Tool (DUET), and Sex- and Gender-Based Analysis of the Effectiveness of Advanced Therapies (SAGE) studies, as well as the Health Initiatives in Psoriasis and Psoriatic Arthritis Consortium European States (HIPPOCRATES) and Elucidating the Landscape of Immunoendotypes in Psoriatic Skin and Synovium (ELLIPSS) studies. The highlight of the meeting was a presentation and discussion on the use of digital tools to study psoriatic disease.

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)0.040
metaresearch head score (Gemma)0.025
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 score0.069
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0020.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0690.035

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 head0.037
GPT teacher head0.359
Teacher spread0.322 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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