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Record W2610917967 · doi:10.3899/jrheum.170150

Defining Outcome Measures for Psoriatic Arthritis: A Report from the GRAPPA-OMERACT Working Group

2017· article· en· W2610917967 on OpenAlexvenueno aff
Alexis Ogdie, Maarten de Wit, Kristina Callis Duffin, Willemina Campbell, Jeffrey Chau, Laura C. Coates, Lihi Eder, Musaab Elmamoun, Oliver FitzGerald, Dafna D. Gladman, Niti Goel, Jana James, Umut Kalyoncu, John Latella, Chris A. Lindsay, Philip J. Mease, Denis O’Sullivan, Ingrid Steinkoenig, Vibeke Strand, William Tillett, Ana‐Maria Orbai

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis Center, Johns Hopkins UniversityNational Institutes of HealthNational Institute for Health and Care ResearchJohns Hopkins UniversityRheumatology Research Foundation
KeywordsPsoriatic arthritisMedicinePsoriasisRheumatologySet (abstract data type)Core (optical fiber)ArthritisInternal medicineMedical physicsPhysical therapyDermatologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Outcome Measures in Rheumatology (OMERACT) Psoriatic Arthritis (PsA) Core Set working group recently published the updated 2016 psoriatic arthritis (PsA) core domain set, a set of disease features that should be measured in all clinical trials. At the GRAPPA annual meeting in July 2016, the PsA working group presented the updated PsA core domain set endorsed by 90% of participants at OMERACT in May 2016 and drafted a roadmap for the development of the PsA core outcome measurement set. In this manuscript, we review the development process of the PsA core domain set and the ongoing and proposed work streams for development of a PsA core measurement set.

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)0.186
metaresearch head score (Gemma)0.222
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.186
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1860.222
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.319
Teacher spread0.273 · 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
GenreMethods

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

Citations56
Published2017
Admission routes1
Has abstractyes

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