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Record W2344562275 · doi:10.3899/jrheum.160116

Report of the GRAPPA-OMERACT Psoriatic Arthritis Working Group from the GRAPPA 2015 Annual Meeting

2016· article· en· W2344562275 on OpenAlexaffvenue
Ana‐Maria Orbai, Philip J. Mease, Maarten de Wit, Umut Kalyoncu, Willemina Campbell, William Tillett, Lihi Eder, Musaab Elmamoun, Oliver FitzGerald, Dafna D. Gladman, Niti Goel, Laure Gossec, Chris A. Lindsay, Ingrid Steinkoenig, Philip Helliwell, Neil McHugh, Vibeke Strand, Alexis Ogdie

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis Center, Johns Hopkins UniversityCelgenePfizerJohns Hopkins UniversityRheumatology Research Foundation
KeywordsPsoriatic arthritisMedicineMedical physicsSet (abstract data type)Family medicineOutcome (game theory)Physical therapyDomain (mathematical analysis)Focus groupWorking groupArthritisInternal medicineComputer science

Abstract

fetched live from OpenAlex

The GRAPPA-OMERACT psoriatic arthritis (PsA) working group is in the process of updating the PsA core domain set to improve and standardize the measurement of PsA outcomes. Work streams comprise literature reviews of domains and outcome measurement instruments, an international qualitative research project with PsA patients to generate domains important to patients, outcome measurement instrument assessment, conduct of domain consensus panels with patients and physicians, and evidence-based selection of instruments. Patient research partners are involved in each of the projects. The working group will present findings and seek endorsement for the new PsA core domain set, outcome measurement set, and research agenda at the OMERACT meeting in May 2016.

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)0.047
metaresearch head score (Gemma)0.055
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: Editorial · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.007
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0160.008

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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
GenreEditorial

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

Citations26
Published2016
Admission routes2
Has abstractyes

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