Clinical significance of antibodies to Ro52/TRIM21 in systemic sclerosis
Bibliographic record
Abstract
INTRODUCTION: Autoantibodies to Ro52 recently identified as TRIM21 are among the most common autoantibodies in systemic autoimmune rheumatic diseases, but their clinical association remains poorly understood. We undertook this study to determine the clinical and serologic associations of anti-Ro52/TRIM21 antibodies in patients with systemic sclerosis (SSc). METHODS: Detailed clinical data and sera from 963 patients with SSc enrolled in a multicenter cohort study were collected and entered into a central database. Antibodies to Ro52/TRIM21 and other autoantibodies were detected with an addressable laser-bead immunoassay and different enzyme-linked immunosorbent assay (ELISA) systems. Associations between anti-Ro52/TRIM21 antibodies and clinical and other serologic manifestations of SSc were investigated. RESULTS: Anti-Ro52/TRIM21 antibodies were present in 20% of SSc patients and overlapped with other main SSc-related antibodies, including anti-centromere (by immunofluorescence and centromere protein (CENP)-A and CENP-B ELISA), anti-topoisomerase I, anti-RNA polymerase III, and anti-Pm/Scl antibodies. Anti-Ro52/TRIM21 antibodies were strongly associated with interstitial lung disease (odds ratio (OR), 1.53; 95% confidence interval (CI), 1.11 to 2.12; P = 0.0091) and overlap syndrome (OR, 2.06; 95% CI, 1.01 to 4.19; P = 0.0059). CONCLUSIONS: Anti-Ro52/TRIM21 antibodies were the second most common autoantibodies in this SSc cohort. In SSc, anti-Ro52/TRIM21 antibodies may be a marker of interstitial lung disease and overlap syndrome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".