Degree of Vasculopathy in Systemic Sclerosis Patients with Anti-U3RNP Antibody Indicates Need for Extensive Cardiopulmonary Screening
Bibliographic record
Abstract
To the Editor: In patients with systemic sclerosis (SSc), regular screening is needed to determine the extent and severity of organ involvement1. Specific autoantibodies are associated with clinical manifestations and are therefore used as predictors for organ involvement2. Identifying patients who are at risk for organ involvement enables distinguishing between patients with high risk who need extensive screening and followup, and patients with most likely mild disease. Antibodies against the U3RNP (or antifibrillarin) are detected in 3–8% of patients with SSc. Some studies indicate a higher risk for cardiac involvement in U3RNP+ patients, but results are conflicting3,4,5,6,7. As shown before, the degree of microangiopathy as reflected by nailfold video-capillaroscopy (NVC) is identified as an independent predictor for organ involvement in SSc8. Associations between presence of U3RNP and NVC pattern and disease manifestations have not been evaluated to date. Therefore, we evaluated degree of microangiopathy as shown by NVC and its association with cardiopulmonary involvement in anti-U3RNP+ patients with SSc. All patients participating in … Address correspondence to Dr. N.M. van Leeuwen, Department of Rheumatology, Leiden University Medical Center, C1-R, PO Box 9600, 2300 RC, Leiden, the Netherlands. E-mail: n.m.van_leeuwen{at}lumc.nl
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".