Brief Report: Risk of Childhood Rheumatic and Nonrheumatic Autoimmune Diseases in Children Born to Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Several autoimmune diseases have familial aggregation and, possibly, common genetic predispositions. In a large population-based study, we evaluated whether children born to mothers with systemic lupus erythematosus (SLE) have an increased risk of rheumatic and nonrheumatic autoimmune diseases versus children born to mothers without SLE. METHODS: Using the Offspring of SLE Mothers Registry, we identified children born live to SLE mothers and their matched controls, and ascertained autoimmune diseases based on ≥1 hospitalization or ≥2 physician visits with a relevant diagnostic code. We adjusted for maternal age, education, race/ethnicity, obstetric complications, calendar birth year, and sex of child. RESULTS: A total of 509 women with SLE had 719 children, while 5,824 matched controls had 8,493 children. The mean ± SD follow-up period was 9.1 ± 5.8 years. Children born to mothers with SLE had a similar frequency of rheumatic autoimmune diagnoses (0.14%; 95% confidence interval [95% CI] 0.01-0.90) versus controls (0.19% [95% CI 0.11-0.32]). There was a trend toward more nonrheumatic autoimmune diseases in SLE offspring (1.11% [95% CI 0.52-2.27]) versus controls (0.48% [95% CI 0.35-0.66]). In multivariate analyses, we did not see a clear increase in rheumatic autoimmune disease (odds ratio [OR] 0.71 [95% CI 0.11-4.82]), but children born to mothers with SLE had a substantially increased risk of nonrheumatic autoimmune disease versus controls (OR 2.30 [95% CI 1.06-5.03]). CONCLUSION: Although the vast majority of offspring have no autoimmune disease, children born to women with SLE may have an increased risk of nonrheumatic autoimmune diseases versus controls. Additional studies assessing offspring through to adulthood would be additionally enlightening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".