Prologue: 2017 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)
Bibliographic record
Abstract
The 2017 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) was held in Amsterdam, the Netherlands, 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 discussion of the history, clinical features, controversies, and immunogenetics of juvenile psoriatic arthritis; updates from working groups in Outcome Measures in Rheumatology and International Dermatology Outcome Measures; a discussion of the benefits and challenges of setting up a longitudinal psoriatic arthritis (PsA) database; 3 separate discussions of the effects of the microbiome on skin and joints in psoriasis and PsA; a discussion of options for assessing joints and entheses in PsA by ultrasonography and magnetic resonance imaging; an update on GRAPPA's research and educational projects; a discussion of patient centricity, including the incorporation of patient research partners (PRP) into psoriasis and PsA research and educational efforts, from GRAPPA's PRP; and a discussion of the GRAPPA-Collaborative Research Network's inaugural meeting. 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.141 | 0.080 |
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".