Prologue: 2019 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)
Bibliographic record
Abstract
The 2019 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) was held in Paris, France, and was attended by rheumatologists, dermatologists, representatives of biopharmaceutical companies, and patients. As in previous years, GRAPPA members held a symposium for trainees to discuss their research in psoriatic disease with experts in the field. Other subjects featured during the annual meeting included a composites workshop to review continuous composite measures; the GRAPPA-Collaborative Research Network's third annual meeting; the need for a precision medicine approach to the treatment of psoriatic disease; updates from working groups in International Dermatology Outcome Measures and Outcome Measures in Rheumatology; a debate on the effectiveness of methotrexate in the treatment of psoriatic arthritis (PsA); updating recommendations for optimal treatment approaches for patients with PsA; an update on GRAPPA's research and educational projects; and the GRAPPA ultrasound (US) working group's goal to optimize the evaluation of enthesitis in patients with PsA using US through the development of a diagnostic US enthesitis tool. In this Prologue, we introduce the papers that summarize that meeting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.195 | 0.138 |
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".