Prologue: Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2022 Annual Meeting
Bibliographic record
Abstract
The 2022 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) was held from July 14 to 17, 2022, in New York City, New York, USA, and was attended by 420 rheumatologists, dermatologists, basic scientists, allied health professionals, patient research partners, and industry partners from 31 countries. A GRAPPA executive retreat, a Trainee Symposium, and the Patient Research Partners Network meeting were held prior to the annual meeting. Presentations included updates in basic research, focusing on biomarkers, personalization of treatments, and the promise of single-cell omics, elucidating the pathogenesis of psoriatic disease (PsD). Presentations also highlighted guttate and plaque psoriasis (PsO), the impact of coronavirus disease 2019 (COVID-19) and its treatments on patients with PsD globally, and the effects of sex and gender in PsD. Reports of ongoing projects included an update on the recently published treatment recommendations, educational initiatives, and the Diagnostic Ultrasound Enthesitis Tool (DUET) study. A session on early identification of psoriatic arthritis (PsA) among patients with PsO included an update on PsA screening tools. Debates were held on whether early intervention for PsO will reduce PsA, whether interleukin (IL)-17 or IL-23 inhibition is a better treatment for PsO and PsA, similarities and differences between axial PsA and axial spondyloarthritis with PsO, and data affecting the understanding of guttate and plaque PsO. Reports from the International Dermatology Outcome Measures (IDEOM) and Young GRAPPiAns concurrent sessions were presented in addition to reports of several other partner groups. Here, we highlight features of the annual meeting and introduce the manuscripts published together 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.191 | 0.132 |
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".