Childhood-Onset Systemic Lupus Erythematosus: Pregnancy and Birth Outcomes in Ontario
Bibliographic record
Abstract
Objectives Childhood-onset systemic lupus erythematosus (cSLE) is a chronic, multisystem, autoimmune disease. Pregnancy and birth outcomes of cSLE are not well understood. Our objectives were to describe and evaluate pregnancy, neonatal, and maternal outcomes among female cSLE patients in Ontario, and to identify demographic and disease characteristics associated with adverse outcomes. Methods A population-based retrospective cohort study linked clinical data for eligible female cSLE patients diagnosed between 1985 and 2011 and followed for ≥1 year from date of diagnosis to March 31, 2023, with multiple health administrative datasets housed at the Institute for Clinical Evaluative Sciences. Descriptive statistics, adjusted, and univariate analyses were used to determine significant associations between risk factors (including demographic and early disease characteristics) and adverse outcomes. Results 489 female cSLE patients were diagnosed between 1985-2011 and followed for 16.8±7.2 years. A total of 423 pregnancies occurred in 175 women. 131 women had at least 1 live birth while 44 had no live births. 46.1% pregnancies resulted in fetal death (including still birth, miscarriage or abortion), 32% of live births were preterm, and 33.3% of neonates were admitted to neonatal intensive care (Table 1). Our adjusted analysis shows that patients who were older at time of cSLE diagnosis have lower odds of fetal death [OR= 0.87, 95% CI (0.78-0.97)], after controlling for years since cSLE diagnosis, ethnicity, income, anti-dsDNA antibodies, and biopsy-proven lupus nephritis. Our univariate analyses show that odds of preterm birth are higher for patients with non-white ethnicity [OR=2.43, 95% CI (1.22-4.85)], anti-Sm antibodies [OR=2.82, 95% CI (1.43-5.56)], and biopsy-proven lupus nephritis [OR=2.51, 95% CI (1.27-4.98)]. Table 1: Pregnancy, neonatal, and maternal outcomes among female cSLE patients Conclusion Investigating pregnancy, neonatal, and maternal outcomes is crucial for providing targeted health care for cSLE patients and their newborns. Factors such as age at diagnosis, non-white ethnicity, and early disease characteristics like anti-Sm antibodies, and biopsy-proven lupus nephritis are significantly associated with adverse pregnancy and birth outcomes. Understanding these associations will enhance patient care and improve health resource management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".