Prenatal maternal subjective distress predicts higher autistic-like traits in offspring: The Iowa Flood Study
Bibliographic record
Abstract
Autism spectrum disorder prevalence more than quadrupled in the United States between 2000 and 2020. Ice storm-related prenatal maternal stress (PNMS) predicts autistic-like trait severity in children exposed early in gestation. The objective was to determine the extent to which PNMS influences the severity and trajectory of autistic-like traits in prenatally flood-exposed children at ages 4-7 years and to test moderation by sex and gestational timing. Soon after the June 2008 floods in Iowa, USA, 268 women pregnant during the disaster were assessed for objective hardship, subjective distress, and cognitive appraisal of the experience. When their children were 4, 5½, and 7 years old, mothers completed the Social Communication Questionnaire (SCQ) to assess their children's autistic-like traits; 137 mothers completed the SCQ for at least one age. The final longitudinal multilevel model showed that the greater the maternal subjective distress, the more severe the child's autistic-like traits, controlling for objective hardship. The effect of PNMS on rate of change was not significant, and there were no significant main effects or interactions involving sex or timing. Prenatal maternal subjective distress, but not objective hardship or cognitive appraisal, predicted more severe autistic-like traits at age 4, and this effect remained stable through age 7.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".