Neurodevelopmental consequences of maternal distress: what do we really know?
Bibliographic record
Abstract
A simple internet search of 'maternal stress and pregnancy' turns up hundreds of hits explaining that an adverse intrauterine environment can affect fetal development and potentially lead to various learning, behavioral, and mood disorders in childhood, as well as complex diseases such as obesity and cardiovascular conditions later in life. Indeed, a growing body of literature now links several intrauterine challenges, including maternal obesity and stress, with adverse developmental outcomes in the child. Over the past 5 years, nearly 5000 publications have explored the consequences of maternal distress on young offspring, a marked increase from the 475 published studies over a comparable period 20 years ago. Yet, despite this explosion of research and widespread warnings to pregnant mothers, we still lack a basic understanding of the pathophysiology linking adverse maternal health to the onset of disease in the child, especially regarding how prenatal and perinatal challenges might affect brain development. Recent studies have begun to explore the cellular basis of the abnormal brain cytoarchitecture associated with fetal exposure to intrauterine challenges. Here, our goal is to review the scientific evidence that maternal distress interferes with key neurodevelopmental steps, as an entry point toward mapping the pathophysiology of pre- and perinatal stress on the unborn child's brain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".