Toward a Multibiomarker Panel to Optimize Outcome and Predict Response in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Juvenile idiopathic arthritis (JIA) is a complex disease with heterogeneous pathogenesis, autoinflammatory and auto-immune, involving both innate and adaptive immunity. All JIA subtypes display joint inflammation, but with distinct clinical phenotypes, disease courses, outcomes, and response to different treatment approaches1. In the last decade, much attention was focused on discovery and potential use of different biomarkers that could provide support in diagnostic and prognostic evaluations. In the sense of diagnostics and personalized therapy decisions, biomarkers could play a major role to support initial diagnosis, allow disease monitoring, and possibly indicate the reoccurrence of inflammatory responses even before clinical manifestation. Such a candidate biomarker(s) should be validated and proven as highly sensitive, obtained by standardized methodology and evaluable in everyday clinical practice. Two reviews by Swart, et al 2 and Gohar, et al 3 exhaustingly elaborated current knowledge and possible clinical usage of different biomarkers in JIA, pointing out applicability of S100 proteins. The phagocyte-specific S100 proteins (calgranulins) S100A8 (calgranulin A, also referred to as myeloid-related protein, MRP8), S100A9 (calgranulin … Address correspondence to Prof. Dr. J. Vojinovic, University of Nis, Faculty of Medicine, Department of Pediatric Rheumatology, Bul dr Zorana Djindjica, 81 Nis, 18000 Serbia. E-mail: vojinovic.jelena{at}gmail
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".