Associated Autoimmune Diseases in Systemic Sclerosis Define a Subset of Patients with Milder Disease: Results from 2 Large Cohorts of European Caucasian Patients
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and potential associations with the systemic sclerosis (SSc) phenotype of additional autoimmune diseases (AID). METHODS: A multicenter study was performed in France and Italy to recruit consecutive European Caucasian patients with SSc systematically assessed for the coexistence of predefined AID known to occur with connective tissue diseases. RESULTS: We recruited 585 French and 547 Italian patients with SSc. Specific AID were found in 114/585 (19%) French and 179/547 (33%) Italians with SSc (p < 0.0001). Sjögren's syndrome and thyroiditis were the predominant AID in both cohorts (12% for Sjögren's syndrome and 6% for thyroiditis in the combined populations). The frequency of myositis, primary biliary cirrhosis, rheumatoid arthritis, and systemic lupus erythematosus was low (< 4%) and similar in both cohorts. The coexistence of at least 1 of the AID in the whole cohort was associated in multivariate analysis with the limited cutaneous subtype, the presence of antinuclear antibodies, and a lower prevalence of digital ulcers. CONCLUSION: Our study shows that 21% of this large series of European Caucasian patients with SSc have developed at least 1 AID. This latter condition identified a subset of patients with milder disease. Thus, associations of AID and autoimmune background in SSc have to be considered for further therapeutic and biological investigations in SSc.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".