Maternal Schizophrenia and the Risk of a Childhood Chronic Condition
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Maternal schizophrenia heightens the risk for certain perinatal complications, yet it is not known to what degree future childhood chronic health conditions (Childhood-CC) might arise. STUDY DESIGN: This population-based cohort study using health administrative data from Ontario, Canada (1995-2018) compared 5066 children of mothers with schizophrenia to 25 324 children of mothers without schizophrenia, propensity-matched on birth-year, maternal age, parity, immigrant status, income, region of residence, and maternal medical and psychiatric conditions other than schizophrenia. Cox proportional hazard models generated hazard ratios (HR) and 95% confidence intervals (CI) for incident Childhood-CCs, and all-cause mortality, up to age 19 years. STUDY RESULTS: Six hundred and fifty-six children exposed to maternal schizophrenia developed a Childhood-CC (20.5/1000 person-years) vs. 2872 unexposed children (17.1/1000 person-years)-an HR of 1.18, 95% CI 1.08-1.28. Corresponding rates were 3.3 vs. 1.9/1000 person-years (1.77, 1.44-2.18) for mental health Childhood-CC, and 18.0 vs. 15.7/1000 person-years (1.13, 1.04-1.24) for non-mental health Childhood-CC. All-cause mortality rates were 1.2 vs. 0.8/1000 person-years (1.34, 0.96-1.89). Risk for children exposed to maternal schizophrenia was similar whether or not children were discharged to social service care. From age 1 year, risk was greater for children whose mothers were diagnosed with schizophrenia prior to pregnancy than for children whose mothers were diagnosed with schizophrenia postnatally. CONCLUSIONS: A child exposed to maternal schizophrenia is at elevated risk of chronic health conditions including mental and physical subtypes. Future research should examine what explains the increased risk particularly for physical health conditions, and what preventive and treatment efforts are needed for these children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".