Malignancies in Patients with Anti-RNA Polymerase III Antibodies and Systemic Sclerosis: Analysis of the EULAR Scleroderma Trials and Research Cohort and Possible Recommendations for Screening
Bibliographic record
Abstract
OBJECTIVE: To analyze the characteristics of anti-RNA polymerase III antibodies (anti-RNAP3)- positive patients with systemic sclerosis (SSc) in the European League Against Rheumatism Scleroderma Trials and Research group (EUSTAR) registry with a focus on the risk of cancer and the characteristics of malignancies, and the aim to provide guidelines about potential cancer screening in these patients. METHODS: (1) Analysis of the EUSTAR database: 4986 patients with information on their anti-RNAP3 status were included. (2) Case-control study: additional retrospective data, including malignancy history, were queried in 13 participating EUSTAR centers; 158 anti-RNAP3+ cases were compared with 199 local anti-RNAP3- controls, matched for sex, cutaneous subset, disease duration, and age at SSc onset. (3) A Delphi exercise was performed by 82 experts to reach consensus for cancer screening in anti-RNAP3+ patients. RESULTS: In the EUSTAR registry, anti-RNAP3 were associated in multivariable analysis with renal crisis and diffuse cutaneous involvement. In the case-control study, anti-RNAP3 were associated with gastric antral vascular ectasia, rapid progression of skin involvement, and malignancies concomitant to SSc onset (OR 7.38, 95% CI 1.61-33.8). When compared with other anti-RNAP3+ patients, those with concomitant malignancies had older age (p < 0.001) and more frequent diffuse cutaneous involvement (p = 0.008). The Delphi exercise highlighted the need for malignancy screening at the time of diagnosis for anti-RNAP3+ patients and tight followup in the following years. CONCLUSION: Anti-RNAP3+ patients with SSc have a high risk of concomitant malignancy. These results have implications for clinical practice and suggest regular screening for cancer in anti-RNAP3+ patients.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".