World Café Report From the GRAPPA 2024 Annual Meeting and Trainee Symposium: Exploring GRAPPA’s Future Priorities
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) annual meeting, held in Seattle, Washington, USA, in July 2024, included a World Café session in which attendees addressed the following question, "In the forthcoming five to ten years, what specific areas of unmet need should GRAPPA prioritize and how should we do so?" The World Café session was attended by rheumatologists, dermatologists, patient research partners, corporate partners, nonclinician scientists, advanced practice practitioners, and trainees. Following an introduction to the World Café process, the session took place in 10 rooms, with each room accommodating approximately 20 participants with an assigned room leader, a moderator, and a young GRAPPA member (members of whom are young clinicians and early career researchers from across the globe interested in the field of psoriatic disease [PsD]) scribe. The World Café session discussion highlighted that in the next 5 to 10 years, the most reported unmet need is research, followed by clinical, educational, and administrative priorities. Many groups identified similar specific unmet needs, including research to discover novel biomarkers; further research to address (and possibly predict) transition from psoriasis to psoriatic arthritis; multispecialty collaboration in clinical practice for the management of comorbidities; cross-specialty education for a greater understanding of PsD and its comorbidities; and the expansion of the reach and impact of GRAPPA's educational materials, recommendations, and advocacy. Ideas generated by World Café participants will inform prioritization, planning, and implementation of the 5- to 10-year organizational vision of GRAPPA.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.021 |
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".