Fetal Origins of Mental Health: The Developmental Origins of Health and Disease Hypothesis
Bibliographic record
Abstract
The quality of fetal growth and development predicts the risk for a range of noncommunicable, chronic illnesses. These observations form the basis of the "developmental origins of health and disease" hypothesis, which suggests that the intrauterine signals that compromise fetal growth also act to "program" tissue differentiation in a manner that predisposes to later illness. Fetal growth also predicts the risk for later psychopathology. These findings parallel studies showing that antenatal maternal emotional well-being likewise predicts the risk for later psychopathology. Taken together, these findings form the basis for integrative models of fetal neurodevelopment, which propose that antenatal maternal adversity operates through the biological pathways associated with fetal growth to program neurodevelopment. The authors review the literature and find little support for such integrated models. Maternal anxiety, depression, and stress all influence neurodevelopment but show modest, weak, or no associations with known stress mediators (e.g., glucocorticoids) or with fetal growth. Rather, compromised fetal development appears to establish a "meta-plastic" state that increases sensitivity to postnatal influences. There also remain serious concerns that observational studies associating either fetal growth or maternal mental health with neurodevelopmental outcomes fail to account for underlying genetic factors. Finally, while the observed relation between fetal growth and adult health has garnered considerable attention, the clinical relevance of these associations remains to be determined. There are both considerable promise and important challenges for future studies of the fetal origins of mental health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".