Prologue: 2020 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 2020 in an online format due to travel restrictions during the coronavirus disease 2019 (COVID-19; caused by SARS-CoV-2) pandemic. The virtual meeting was attended by 351 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. Trainee sessions this year included the annual trainee symposium and a grant-writing workshop. Plenary sessions included updates on COVID-19 and psoriatic disease from multispecialty and patient perspectives, and updates on pustular psoriasis and associated musculoskeletal manifestations. Progress on research and updates were presented for the following groups: Collaborative Research Network, Outcome Measures in Rheumatology (OMERACT) Psoriatic Arthritis Working Group, International Dermatology Outcome Measures, Composite Measures, Education Committee, and Treatment Guidelines. New this year were 3 concurrent workshops on ultrasound assessment of joints and entheses, magnetic resonance imaging of psoriatic arthritis, and pustular psoriasis efficacy endpoints; 6 "Meet the Expert" sessions; and facilitated "poster tours." In our 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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.274 | 0.188 |
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".