Risk of Allergic Conditions in Children Born to Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Limited evidence suggests a potentially increased risk of allergic conditions in offspring born to women with systemic lupus erythematosus (SLE). In a large population-based study, we aimed to determine if children born to mothers with SLE have an increased risk of allergic conditions compared to children born to mothers without SLE. METHODS: Using the Offspring of SLE Mothers Registry, we identified children born live to mothers with SLE and their matched controls, and ascertained the number of allergic conditions (asthma, allergic rhinitis, eczema, urticaria, angioedema, and anaphylaxis) based on ≥1 hospitalization or ≥1 or 2 physician(s) visit(s) with a relevant diagnostic code. We adjusted for maternal age, education, race/ethnicity, obstetrics complications, calendar year of birth, sex of the child, and maternal medication. RESULTS: There were 509 women with SLE who had 719 children, while 5,824 matched controls had 8,493 children. The mean ± SD followup period was 9.1 ± 5.8 years. Compared to controls, more children born to mothers with SLE had evidence of allergic conditions (43.9% [95% confidence interval (95% CI) 40.4-47.6] versus 38.1% [95% CI 37.0-39.1]). In multivariate analysis (n = 9,212), children born to mothers with SLE had an increased risk of allergic conditions versus control children (odds ratio 1.35 [95% CI 1.13-1.61]). CONCLUSION: Compared to children from the general population, children born to women with SLE may have an increased risk of allergic conditions. Genetics, shared environmental exposures, as well as in utero exposure to maternal autoantibodies and cytokines may mediate this increased risk.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".