Long‐Term Outcomes of Children Born to Anti‐Ro Antibody–Positive Mothers With and Without Rheumatic Disease
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to estimate the prevalence of allergy, and/or neurodevelopmental and autoimmune diagnoses in children born to anti-Ro antibody-positive mothers. METHODS: We conducted a cohort study of children born to anti-Ro antibody-positive mothers observed in the neonatal lupus erythematosus (NLE) clinic at The Hospital for Sick Children. Participants one year of age or older were invited to complete a health status questionnaire. Prevalence of allergic, neurodevelopmental, and autoimmune disease diagnoses was compared between the NLE cohort and the non-NLE population-based CHILD Cohort Study cohort. Descriptive statistics were used for demographics, NLE manifestations, and outcomes. Fisher's exact test compared the prevalence of diagnoses between subgroups. We tested the association between allergies and neurodevelopmental conditions and NLE with logistic regression models. A P-value < 0.006 was considered significant. RESULTS: We included 321 participants born to anti-Ro antibody-positive mothers. The median age at survey completion was six years, 51% of participants were female, and 50% (n = 162) had NLE. We found no significant difference in any disease prevalence between children with and without NLE manifestations (P = 0.57) or between children born to mothers with and without a rheumatic disease (P = 0.11). Disease prevalence was similar between the NLE and CHILD cohorts (allergic disease 30% vs 22% [P= 0.25], neurodevelopmental conditions 5% vs 2% [P = 0.45], autoimmune disease 4% vs 2% [P = 0.68]). CONCLUSION: In a large multiethnic cohort of infants born to anti-Ro antibody-positive mothers, there was no significant difference in the prevalence of allergic, neurodevelopmental, or autoimmune diseases between children with and without NLE or between those born to anti-Ro antibody-positive mothers and a population-based non-NLE cohort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".