Prologue: Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 Annual Meeting
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting was held from July 11 to July 13, 2024, in Seattle, Washington, USA, and was attended by 256 rheumatologists, dermatologists, trainees, patient research partners, patient organization representatives, industry partners, and others. The meeting featured several workshops on various topics including epidemiology, new educational initiatives, use of artificial intelligence for both education and clinical management of disease, use of magnetic resonance imaging in the diagnosis and management of psoriatic disease (PsD), and many more. Young-GRAPPA held a workshop and business meeting about their projects, and many of the young GRAPPiAns contributed as coauthors of the articles in this supplement. Debates focused on whether clinical enthesitis indices reflect true enthesitis and whether they should be discontinued, and whether musculoskeletal symptoms in PsD should be managed by dermatologists or rheumatologists. Here we provide an overview of the features of the GRAPPA 2024 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 | 0.008 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.171 | 0.105 |
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".