Prologue: Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2023 Annual Meeting
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2023 annual meeting was held on July 13 to 15, 2023, in Dublin, Ireland, and was attended by 285 rheumatologists, dermatologists, trainees, patient research partners (PRPs), representatives of patient organizations, and industry partners. The 20th anniversary of GRAPPA was celebrated with a special presentation and archival video. Ahead of the meeting, the PRP Network met, a workshop was held by the International Dermatology Outcome Measures (IDEOM) group, and there was a workshop in which researchers discussed advancing ultrasound use to improve the management of psoriatic disease (PsD). Young-GRAPPA also held a workshop and business meeting. Multiple presentations highlighted important topics currently influencing PsD, including ensuring that patients are included in advancing research, the role of depression in PsD, the use of magnetic resonance imaging for spinal lesions, and animal models of PsD, among others. Debates focused on whether biologics should be used for mild psoriasis, whether methotrexate should remain the first-line treatment for PsD, and whether PsD is really a primary enthesitis driving joint synovitis. Here we provide an overview of the features of the GRAPPA 2023 annual meeting and introduce the manuscripts published together in this supplement as a meeting report.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.174 | 0.101 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".