Update on Biomarkers in Psoriatic Arthritis: A Report from the GRAPPA 2010 Annual Meeting: Table 1.
Bibliographic record
Abstract
Biomarkers may be used to screen patients with psoriasis for psoriatic arthritis (PsA) and to assess disease activity and severity. Candidate biomarkers should fulfil the key features of the OMERACT (Outcome Measures in Rheumatology) filter, that is, truth, discrimination, and feasibility. A number of biomarkers are currently being investigated in psoriatic disease for important clinical outcomes. Serum high sensitivity C-reactive protein, cartilage oligomeric matrix protein, interleukin 6 (IL-6), osteoprotegerin, matrix metalloprotease-3 (MMP-3), and the ratio of C-propeptide of type II collagen (CPII) to collagen fragment neoepitopes Col2-3/4 (long mono) (C2C) show promise as serum biomarkers that distinguish subjects with PsA from those with psoriasis alone. Serum MMP-3 and melanoma inhibitory activity, synovial fluid IL-1, IL-1 receptor-α, IL-6, IL-8, and chemokine CCL3 and synovial tissue CD3-positive T cells may prove useful as biomarkers of PsA activity. Circulating osteoclast precursors, Dickkopf-1, macrophage colony stimulating factor, receptor activator of nuclear factor-κB ligand, and bone alkaline phosphatase are strong candidates as biomarkers of radiographic change. Prospective studies to identify biomarkers for psoriatic disease are high on the research agenda of the Group for Research and Assessment of Psoriasis and PsA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".