Early Environmental Upheaval and the Risk for Schizophrenia
Bibliographic record
Abstract
Why does prenatal exposure to wars, natural disasters, urbanicity, or winter increase the risk for schizophrenia? Research from the last two decades has provided rich insight about the underlying chains of causation at play during environmental upheaval, from conception to early infancy. In this review, we appraise the evidence linking schizophrenia spectrum disorder to prenatal maternal stress, obstetric complications, early infections, and maternal nutrition and other lifestyle factors. We discuss putative mechanisms, including the maternal stress system, perinatal hypoxia, and maternal-offspring immune activation. We propose that gene-environment interactions, timing during development, and sex differentiate the neuropsychiatric outcomes. Future research should pursue the translation of animal studies to humans and the longitudinal associations between early exposures, intermediate phenotypes, and psychiatric disorders. Finally, to paint a comprehensive model of risk and to harness targets for prevention, we argue that risk factors should be situated within the individual's personal ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".