Pregnancy complications and new-onset maternal autoimmune disease
Bibliographic record
Abstract
BACKGROUND: Autoimmune diseases disproportionately impact women and female-specific aspects of reproduction are thought to play a role. We investigated the time-varying association between pregnancy complications and new-onset autoimmune disease in females during the reproductive and midlife years. METHODS: We conducted a population-based cohort study of 1 704 553 singleton births to 1 072 445 females in Ontario, Canada (2002-17) with no pre-existing autoimmune disease. Pregnancy complications were preeclampsia, stillbirth, spontaneous preterm birth and severe small for gestational age (SGA). Royston-Parmar models were used to estimate the time-varying association between pregnancy complications and a composite of 25 autoimmune diseases from date of delivery to date of autoimmune disease diagnosis or censoring at death, loss of health insurance, or 31 March 2021. Models were adjusted for baseline socio-demographics, parity and comorbidities. RESULTS: At 19 years (median = 10.9 years of follow-up), cumulative incidence of autoimmune disease was 3.1% in those with a pregnancy complication and 2.6% in those without complications. Adjusted hazard ratio (AHR) curves as a function of time since birth were generally L-shaped. Universally, risks were most elevated within the first 3 years after birth [at 1 year: preeclampsia AHR 1.22, 95% confidence interval (CI) 1.09-1.36; stillbirth AHR 1.36, 95% CI 0.99-1.85; spontaneous preterm birth AHR 1.30, 95% CI 1.18-1.44; severe SGA AHR 1.14, 95% CI 0.99-1.31] and plateaued but remained elevated thereafter. CONCLUSIONS: Prior history of pregnancy complications may be an important female-specific risk factor to consider during clinical assessment of females for possible autoimmune disease to facilitate timely detection and treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".