Prologue: 2009 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)
Bibliographic record
Abstract
The 2009 Annual Meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) was held in June 2009 in Stockholm, Sweden, and was attended by rheumatologists, dermatologists, biopharmaceutical company representatives, and patient groups. A primary goal of GRAPPA is to foster outreach and interdisciplinary communication between the fields of rheumatology and dermatology. Several members attended an adjacent meeting of the International Federation of Psoriasis Associations; reports were also provided of recent meetings of the American Academy of Dermatology and the Assessment of SpondyloArthritis (ASAS) working group. In a training session of the GRAPPA meeting, members served as faculty while rheumatology fellows and dermatology residents presented original research work. In one module of the meeting, several response measures were discussed. In another module, discussions were held on the need for dermatologists to be able to diagnose psoriatic arthritis (PsA) among their psoriasis patients; several PsA screening questionnaires were presented, and progress was reported on developing online training videos as an aid to educate clinicians in their diagnoses. Other topics for discussion at the GRAPPA meeting included presentations on genetic associations with PsA and on comorbidities in patients with PsA. Current and future research projects also were outlined.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.133 | 0.090 |
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".