Presence of Antitopoisomerase I Antibody Alone May Not Be Sufficient for the Diagnosis of Systemic Sclerosis
Bibliographic record
Abstract
Systemic sclerosis (SSc) is characterized by altered immune function and vascular damage, which lead to extensive fibrosis. Experts underscore the importance of using SSc-specific autoantibodies to divide the disease into subsets for prognosis, management, and research1,2. One representative feature of the immunological abnormalities in patients with SSc is the presence of antinuclear antibodies (ANA) associated with autoantibody targets. The anticentromere (ACA), antitopoisomerase I (anti–topo I), anti-RNA polymerase I/III (ARA I/III), and anti-Th/To constitute about 80–85% of autoantibodies specific for SSc and can assist the physician in assessment3,4,5. The 2013 classification criteria for SSc provide 3 points (toward a 9-point diagnosis) for patients who test positive for anti-ACA, anti-ARA III, or anti–topo I antibodies1. All 3 classical autoantibodies remain stable throughout the course of disease and tend to have a mutually exclusive association. While the presence of anti–topo I antibodies is thought to be highly specific and diagnostic for SSc, the significance of certain positive results remains unclear6,7. Since the development of ELISA to detect anti–topo I antibodies, a number of different … Address correspondence to Dr. T.M. Frech, University of Utah, Internal Medicine, 30 N. 1900 E., Salt Lake City, Utah 84132, USA. E-mail: tracy.frech{at}hsc.utah.edu
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".