Familial Autoimmunity in Systemic Sclerosis — Results of a French-based Case-Control Family Study
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of autoimmune diseases in first-degree relatives of patients with systemic sclerosis (SSc), and to compare those results with control families in order to identify patterns of autoimmune diseases in relatives. METHODS: A retrospective case-control postal questionnaire survey was performed in France to recruit patients with SSc belonging to an association of patients with SSc and unrelated age-matched and sex-matched controls. Each participant was asked to self-report on the existence of autoimmune diseases in their first-degree relatives. The prevalence of autoimmune diseases in the families of patients with SSc was compared with the corresponding prevalence in the families of controls. RESULTS: A total of 121 families out of 373 (32.4%) with a member having SSc reported at least 1 autoimmune disease in 1 or more first-degree relatives. The most frequent autoimmune diseases in SSc families when adjusted for family size were autoimmune thyroid disease (AITD; 4.9%), rheumatoid arthritis (4.1%), psoriasis (3.9%), and type 1 diabetes mellitus (2.9%). Compared with control families, AITD and connective tissue diseases (SSc, systemic lupus erythematosus, or Sjögren's syndrome) were more likely to occur in families with SSc (p = 0.01 and p = 0.01, respectively), with OR of 3.20 (95% CI 1.25-8.18) and 5.20 (95% CI 1.22-21.8). In contrast, inflammatory bowel disease was less likely to occur within families with SSc (p = 0.02, OR 0.29, 95% CI 0.11-0.80). In addition, the coexistence of more than 1 autoimmune disease in the index SSc case was associated with familial aggregation of autoimmune diseases. CONCLUSION: Our results show that autoimmune diseases cluster within families of patients with SSc. This supports the notion that these diseases might arise on a shared genetic basis underlying several autoimmune phenotypes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".