Strategies for Biomarker Development in Psoriatic Disease: A Report from the GRAPPA 2010 Annual Meeting
Bibliographic record
Abstract
Psoriatic disease includes psoriasis and associated comorbidities (arthritis, uveitis, inflammatory bowel disease, cardiovascular disease, metabolic syndrome, and anxiety/depression) and is remarkably diverse in disease presentation and course. The marked heterogeneity of musculoskeletal involvement in psoriatic arthritis (PsA) presents major challenges to clinicians regarding diagnosis, risk stratification, and management. Members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) have begun collaborative efforts to develop biomarkers that can assist in the diagnosis and management of patients with psoriasis and related comorbidities. This brief review provides a rationale for biomarker research in PsA, consideration of types and sources of biomarkers, and examples of important biomarker studies in PsA, followed by a review of trial designs for biomarker research and a discussion of potential funding sources.
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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 | 0.045 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".