Ethnicity and Neonatal Lupus Erythematosus Manifestations Risk in a Large Multiethnic Cohort
Bibliographic record
Abstract
Objective To evaluate the association between ethnicity and neonatal lupus erythematosus (NLE), as well as specific NLE manifestations in a large multiethnic population. Methods We conducted a cohort study of the children (≤ 1 yr of age) seen in the NLE clinic at The Hospital for Sick Children (SickKids), between January 2011 and April 2019. The cohort was divided into European, non-European, and mixed European–non-European groups according to parent-reported child’s ethnicity (Canada Census categories). Outcomes were NLE and specific NLE manifestations (cardiac, cutaneous, cytopenias, transaminitis, and macrocephaly). The frequency of NLE and specific manifestations were compared between ethnic groups (Fisher exact test). We tested the association between ethnicity and (1) NLE risk, and (2) specific NLE manifestations with logistic regression models, including covariates for child’s sex, maternal rheumatic disease status during pregnancy, and maternal use of antimalarials during pregnancy (multiple comparisons threshold P < 0.008). Results We included 324 children born to 270 anti-Ro antibody–positive mothers. Median age at first visit was 1.8 (IQR 1.4–2.3) months, and median follow-up time was 12 (IQR 2–24) months. The majority was non-European (48%), with 34% European, and 18% mixed European–non-European. There was no significant association between non-European ethnicity (OR 1.18, 95% CI 0.71–1.94, P = 0.51), mixed European–non-European ethnicity (OR 1.13, 95% CI 0.59–2.16, P = 0.70), and NLE risk compared with European ethnicity. We also did not find an association between ethnicity and specific NLE manifestations in univariate or multivariable-adjusted models. Conclusion In a large multiethnic cohort, there was no association between a child’s ethnicity and NLE risk or specific NLE manifestations.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".