The Effect of Maternal Stress during Pregnancy on IQ and ADHD Symptomatology.
Bibliographic record
Abstract
OBJECTIVE: Maternal stress during pregnancy (MSDP) has been linked to a decrease in Intelligence Quotient (IQ) in the general population. The purpose of this study is to first examine the association between MSDP and IQ in children with Attention-Deficit/Hyperactivity Disorder (ADHD) and second, to confirm, in a large sample, the link between MSDP and ADHD behavioral symptomatology. METHODS: Four hundred ten children diagnosed with ADHD, ages six to 12, were consecutively recruited from the ADHD clinic and day hospital at the Douglas Institute from 1999 to 2013. IQ was assessed using the WISC III and IV. Symptom severity was evaluated using the Child Behavior Checklist (CBCL) and Connor's Global Index for Parents (CGI-P) and Teachers (CGI-T). RESULTS: No significant effect of MSDP on full scale IQ was observed, but MSDP had a significant effect on CBCL and CGI scores. Elevated MSDP was significantly associated with increased CBCL internalizing scores (β=4.2, p<.01), CBCL externalizing scores (β=1.9, p=.04), CGI-P restless-impulsive scores (β=2.6, p=.01), CGI-P emotional lability scores (β=3.1, p=.02), and CGI-T restless-impulsive (β=2.2, p=.05) and emotional lability (β=3.4, p=.04) scores. MSDP increased the variance explained of ADHD symptomatology even after controlling for various factors (i.e. familial income, parental education, smoking and drinking during pregnancy, gender and age). CONCLUSION: The study demonstrates that in children with ADHD, MSDP does not have an impact on IQ but rather on ADHD symptomatology, highlighting the importance of potentially offering psychological and social support to mothers who experience stress during pregnancy.
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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 | 0.008 |
| 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 | 0.001 | 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".