Prologue: 2016 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)
Bibliographic record
Abstract
The 2016 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) was held in Miami, Florida, USA, and attended by rheumatologists, dermatologists, and representatives of biopharmaceutical companies and patient groups. As in previous years, GRAPPA members held a symposium for trainees to discuss their research in psoriatic disease with experts in the field. A strategic planning session was convened by the Steering Committee this year to review the work of GRAPPA since its inception in 2003. Other subjects featured during the annual meeting included a partnership with KPMG LLP (UK) to conduct interviews at research centers worldwide to analyze the process of care in psoriasis and psoriatic arthritis (PsA); a discussion of the effects of interleukin 17-related pathways on the skin and joints in psoriasis and PsA; summaries of recently published treatment recommendations and related guides; 4 separate discussions of psoriasis patient examinations; updates from working groups in the Outcome Measures in Rheumatology and the International Dermatology Outcome Measures; a discussion of patient centricity from GRAPPA's patient research partners; and an update of research and educational projects from GRAPPA. In this prologue, we introduce the papers that summarize that 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.168 | 0.100 |
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".