Intellectual disability and other neuropsychiatric outcomes in high-risk children of mothers with schizophrenia, bipolar disorder and unipolar major depression
Bibliographic record
Abstract
BACKGROUND: Recent evidence points to partially shared genetics of neuropsychiatric disorders. AIMS: We examined risk of intellectual disability and other neuropsychiatric outcomes in 3174 children of mothers with schizophrenia, bipolar disorder or unipolar major depression compared with 3129 children of unaffected mothers. METHOD: We used record linkage across Western Australian population-based registers. The contribution of obstetric factors to risk of intellectual disability was assessed. RESULTS: Children were at significantly increased risk of intellectual disability with odds ratios (ORs) of 3.2 (95% CI 1.8-5.7), 3.1 (95% CI 1.9-4.9) and 2.9 (95% CI 1.8-4.7) in the maternal schizophrenia, bipolar disorder and unipolar depression groups respectively. Multivariate analysis suggests familial and obstetric factors may contribute independently to the risk. Although summated labour/delivery complications (OR = 1.4, 95% CI 1.0-2.0) just failed to reach significance, neonatal encephalopathy (OR = 7.7, 95% CI 3.0-20.2) and fetal distress (OR = 1.8, 95% CI 1.1-2.7) were independent significant predictors. Rates of rare syndromes in children of mothers with mental disorder were well above population rates. Risk of pervasive developmental disorders, including autism, was significantly elevated for children of mothers with bipolar disorder. Risk of epilepsy was doubled for children of mothers with unipolar depression. CONCLUSIONS: Our findings provide epidemiological support for clustering of neuropsychiatric disorders. Further larger epidemiological studies are warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".