Maternal and newborn outcomes among women with schizophrenia: a retrospective population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: More women with schizophrenia are becoming pregnant, such that contemporary data are needed about maternal and newborn outcomes in this potentially vulnerable group. We aimed to quantify maternal and newborn health outcomes among women with schizophrenia. DESIGN: Retrospective cohort study. SETTING: Population based in Ontario, Canada, from 2002 to 2011. POPULATION: Ontario women aged 15-49 years who gave birth to a liveborn or stillborn singleton infant. METHODS: Women with schizophrenia (n = 1391) were identified based on either an inpatient diagnosis or two or more outpatient physician service claims for schizophrenia within 5 years prior to conception. The reference group comprised 432 358 women without diagnosed mental illness within the 5 years preceding conception in the index pregnancy. MAIN OUTCOME MEASURES: The primary maternal outcomes were gestational diabetes mellitus, gestational hypertension, pre-eclampsia/eclampsia, and venous thromboembolism. The primary neonatal outcomes were preterm birth, and small and large birthweight for gestational age (SGA and LGA). Secondary outcomes included additional key perinatal health indicators. RESULTS: Schizophrenia was associated with a higher risk of pre-eclampsia (adjusted odds ratio, aOR 1.84; 95% confidence interval, 95% CI 1.28-2.66), venous thromboembolism (aOR 1.72, 95% CI 1.04-2.85), preterm birth (aOR 1.75, 95% CI 1.46-2.08), SGA (aOR 1.49, 95% CI 1.19-1.86), and LGA (aOR 1.53, 95% CI 1.17-1.99). Women with schizophrenia also required more intensive hospital resources, including operative delivery and admission to a maternal intensive care unit, paralleled by higher neonatal morbidity. CONCLUSIONS: Women with schizophrenia are at higher risk of multiple adverse pregnancy outcomes, paralleled by higher neonatal morbidity. Attention should focus on interventions to reduce the identified health disparities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".