Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta‐analysis of data from six prospective longitudinal U.S. cohorts
Bibliographic record
Abstract
BACKGROUND: Cognitive and behavioral sequelae of prenatal alcohol exposure (PAE) continue to be prevalent in the United States and worldwide. Because these sequelae are also common in other neurodevelopmental disorders, researchers have attempted to identify a distinct neurobehavioral profile to facilitate the differential diagnosis of fetal alcohol spectrum disorders (FASD). We used an innovative, individual participant meta-analytic technique to combine data from six large U.S. longitudinal cohorts to provide a more comprehensive and reliable characterization of the neurobehavioral deficits seen in FASD than can be obtained from smaller samples. METHODS: Meta-analyses were performed on data from 2236 participants to examine effects of PAE (measured as oz absolute alcohol/day (AA/day)) on IQ, four domains of cognition function (learning and memory, executive function, reading achievement, and math achievement), sustained attention, and behavior problems, after adjusting for potential confounders using propensity scores. RESULTS: The effect sizes for IQ and the four domains of cognitive function were strikingly similar to one another and did not differ at school age, adolescence, or young adulthood. Effect sizes were smaller in the more middle-class Seattle cohort and larger in the three cohorts that obtained more detailed and comprehensive assessments of AA/day. PAE effect sizes were somewhat weaker for parent- and teacher-reported behavior problems and not significant for sustained attention. In a meta-analysis of five aspects of executive function, the strongest effect was on set-shifting. CONCLUSIONS: The similarity in the effect sizes for the four domains of cognitive function suggests that PAE affects an underlying component or components of cognition involving learning and memory and executive function that are reflected in IQ and academic achievement scores. The weaker effects in the more middle-class cohort may reflect a more cognitively stimulating environment, a different maternal drinking pattern (lower alcohol dose/occasion), and/or better maternal prenatal nutrition. These findings identify two domains of cognition-learning/memory and set-shifting-that are particularly affected by PAE, and one, sustained attention, which is apparently spared.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.045 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".