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Record W4385445578 · doi:10.3899/jrheum.2023-0534

Proceedings of the Collaborative Research Network Meeting at the GRAPPA 2022 Annual Meeting

2023· article· en· W4385445578 on OpenAlexaffvenue
Beverly Ng, Deepak R. Jadon, Frank Behrens, Maarten de Wit, Oliver FitzGerald, Dafna D. Gladman, Philip J. Mease, D. C. O'SULLIVAN, Stephen R. Pennington, Georg Schett, Vinod Chandran, Kurt de Vlam

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsToronto Western Hospital
FundersNIHR Cambridge Biomedical Research CentreNational Institute for Health and Care ResearchCelltrionCelgeneBiogenGilead SciencesAmgenPfizerEli Lilly and Company
KeywordsPsoriatic arthritisMedicinePsoriasisCohortSteering committeeArthritisMedical physicsPhysical therapyFamily medicineDermatologyInternal medicineEngineering managementEngineering

Abstract

fetched live from OpenAlex

At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2022 annual meeting, the Collaborative Research Network (CRN) met to present updates on several projects. These included the GRAPPA-Industry biomarker projects, Axial Psoriatic Arthritis Molecular and Clinical Characterisation Study, Axial Involvement in Psoriatic Arthritis Cohort (AXIS) study, and the Health Initiatives in Psoriasis and Psoriatic Arthritis Consortium European States (HIPPOCRATES). The meeting concluded with a discussion on pathways to further academia-industry collaboration.

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)0.038
metaresearch head score (Gemma)0.029
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 score0.047
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0470.019

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 head0.025
GPT teacher head0.320
Teacher spread0.295 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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