Prologue: 2021 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) held its annual meeting in 2021 in an online format due to travel restrictions during the ongoing COVID-19 (coronavirus disease 2019) pandemic. The virtual meeting was attended by rheumatologists, dermatologists, representatives of biopharmaceutical companies, and patient research partners. Similar to previous years, GRAPPA's annual meeting focused on the 3 overlapping missions of education, research, and clinical care of psoriatic disease (PsD). The virtual meeting allowed a variety of different types of sessions to be held, including the trainee symposium, keynote lectures, interactive sessions (5 Meet the Experts sessions, a debate on first-line therapy, and 5 guided poster sessions), 4 workshops (trainee workshop focusing on the diagnosis of PsD, ultrasound, magnetic resonance imaging, and the International Dermatology Outcome Measures group), updates on a variety of research topics (research findings from the 2020 GRAPPA research grant awardees, 3 basic science talks, Outcome Measures in Rheumatology [OMERACT] Working Group efforts, and Collaborative Research Network progress), current "hot topics" (use of Janus kinase inhibitors, promoting diversity and inclusion in PsD, progress on the updated GRAPPA treatment recommendations, and the introduction of the Young GRAPPA member group), and the presentation of four 2021 GRAPPA grant awardees and election results. In this prologue, we introduce the papers that summarize this meeting.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.406 | 0.272 |
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".