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Record W4400654945 · doi:10.3899/jrheum.2024-0230

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Celebrates Its 20th Anniversary

2024· article· en· W4400654945 on OpenAlexaffvenue
Dafna D. Gladman, Philip Helliwell, Philip J. Mease

Bibliographic record

VenueThe Journal of Rheumatology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsPsoriatic arthritisPsoriasisMedicineAlternative medicineDermatologyRheumatologyFamily medicineDiseaseMedical physicsInternal medicinePhysical therapyPathology

Abstract

fetched live from OpenAlex

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) started in August 2003 with 40 initial participants and celebrated its 20th anniversary with 1036 members, many of whom attended the annual meeting in Dublin, Ireland, on July 15 to 17, 2023. GRAPPA arose from a need experienced by psoriatic arthritis (PsA) and psoriasis (PsO) investigators to meet to address questions related to psoriatic disease (PsD). Though other groups were meeting at the time to classify and discuss PsA, GRAPPA arose from a desire to include international clinical and investigational researchers of both dermatology and rheumatology. The organization has built awareness of PsO and PsA, developed and validated research assessment tools to measure clinical status and disease outcomes, published multiple treatment recommendations, supported basic and clinical research on PsD pathophysiology, fostered interactions across research fields, and educated the future generation of PsO and PsA researchers. The group continues to focus on major priorities affecting patients with PsD and will continue evolving in the next decades.

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)0.017
metaresearch head score (Gemma)0.020
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 score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0060.003
Open science0.0020.007
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0160.016

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 head0.320
Teacher spread0.284 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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