Maternal Smoking During Pregnancy and ADHD: A Comprehensive Clinical and Neurocognitive Characterization
Bibliographic record
Abstract
INTRODUCTION: Evidence from epidemiological studies has consistently shown an association between maternal smoking during pregnancy (MSDP) and attention-deficit/hyperactivity disorder (ADHD). The objective of this study is to test the hypothesis that children with ADHD exposed to MSDP show a distinctive clinical and neurocognitive profile when compared with unexposed children. METHODS: Four hundred and thirty-six children diagnosed with ADHD were stratified by exposure to MSDP and compared with regard to severity of illness, comorbidity, IQ, and executive function as assessed by a battery of neuropsychological tests. All comparisons were adjusted for socioeconomic status, ethnicity, mother's age at child's birth, and maternal alcohol consumption during pregnancy. RESULTS: Exposed children had more severe behavioral problems with greater externalizing symptoms and more conduct and oppositional defiant disorder items, lower verbal IQ, and a sluggish cognitive profile on the Continuous Performance Test (CPT). Linear regression analyses revealed a dose-response relationship between the average number of cigarettes smoked per day during pregnancy and verbal IQ, CPT omission errors T score and several other clinical variables. CONCLUSIONS: These results suggest that MSDP is associated with a more severe form of ADHD, characterized by more severe clinical manifestations and poorer neuropsychological performance. This phenotypic signature associated with MSDP may help to identify a more homogenous subgroup of children with ADHD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".