Biomarkers in Psoriasis and Psoriatic Arthritis: GRAPPA 2008
Bibliographic record
Abstract
Biomarkers can provide valuable insights into disease susceptibility and natural history and may serve as surrogate endpoints for a variety of different outcomes. At the 2008 annual meeting of GRAPPA (Group for Research and Assessment of Psoriasis and Psoriatic Arthritis), members were updated on the development of biomarkers in psoriatic arthritis (PsA). Plenary presentations included a translational approach to biomarker development (Christopher Ritchlin, University of Rochester, NY, USA), biomarkers for psoriasis (Abrar Qureshi, Harvard Medical School, MA, USA), new data on biomarkers for damage in PsA (Kurt de Vlam, University Hospitals Leuven, Belgium), and design considerations for a longitudinal study of joint damage being undertaken under the OMERACT umbrella with colleagues working on rheumatoid arthritis and ankylosing spondylitis (Costantino Pitzalis, Barts and the London School of Medicine, London, UK; Oliver FitzGerald, St. Vincent's Hospital, Dublin, Ireland). At the conclusion of this session, the meeting attendees discussed specific design issues of the proposed longitudinal study, including study duration, disease process core domains, and the instruments to be used in recording enthesitis, dactylitis, nail involvement, quality of life and structural damage. The appearance of new therapeutic options in PsA raises the need for sensitive biomarkers for both disease activity and outcome.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".