The Need for Preclinical Biomarkers in Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
Systemic autoimmune rheumatic diseases (SARD) include systemic lupus erythematosus (SLE), Sjögren syndrome (SS), rheumatoid arthritis (RA), and systemic sclerosis (SSc). SARD are characterized by immune dysregulation, with varying commonalities in genetic susceptibility1,2, peripheral blood cytokine profiles (e.g., interferon-α)3 and the production of antinuclear antibodies (ANA). Each clinical syndrome is uniquely defined by the presence of specific autoantibodies, target organ predilection, response to therapy, and longterm prognosis. Although the precise pathogenic mechanisms underlying SARD in general, and the development of specific clinical entities in particular are largely unknown, they likely follow a common pattern of progression from an early preclinical stage through benign to pathologic autoimmunity, culminating in overt clinical disease. Several lines of evidence support this paradigm, with immunological derangements including time-dependent expansion of pathogenic autoantibodies (e.g., RA4, SLE5), discrete cellular abnormalities (e.g., decreased regulatory T cells), and rising levels of circulating proinflammatory cytokines (e.g., RA6) noted in patients with SARD prior to diagnosis. This temporal framework is likely common to all SARD; however, variation in the progression of individual cases — for example with regard to the elaboration of specific autoantibodies and other cellular and biochemical derangements — likely influences the eventual manifestation of particular clinical syndromes. The timing and nature of disease-defining variables and branch points have yet to be elucidated. Autoantibodies can be found in patients with SARD years before the emergence of clinical disease, but they are insufficient to identify individuals at risk of progression. Serological abnormalities can be found in individuals unlikely to … Address correspondence to Dr. C. Landolt-Marticorena, Toronto Western Hospital, 399 Bathurst St., Toronto, Ontario M5T 2S8, Canada. E-mail: Dr.Carol.Landolt-Marticorena{at}uhn.on.ca
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".