The Long-Term Effects of Prenatal Alcohol Exposure on Offspring: Insights from the ALSPAC Cohort
Bibliographic record
Abstract
Prenatal alcohol exposure (PAE) is a significant public health concern, associated with adverse developmental outcomes throughout the lifespan. The Avon Longitudinal Study of Parents and Children (ALSPAC), a globally recognized longitudinal birth cohort, provides a robust dataset for examining the effects of PAE on physical, cognitive, and behavioral health outcomes. A structured search was conducted to identify peer-reviewed studies that utilized ALSPAC data to explore the effects of PAE. Studies were included if they satisfied the inclusion criteria (i.e., published between in English language 1999-2024, and examined the associations between PAE and outcomes in children and adolescents. The results reveal mixed findings. While conclusions from some studies suggest significant association exist between moderate levels of PAE and mild cognitive deficits and/or increase behavioral problems, especially in specific domains such as hyperactivity and inattention, other studies showed no relationship between low-to-moderate PAEs and cognitive or behavioral outcomes. Conversely, higher PAE levels were more often significantly associated with adverse outcomes such as reduced Intelligence Quotient (IQ), behavioral and emotional problems, lower birth weight, increased risk of depression, and adolescent drug and alcohol-related problems. ALSPAC-based studies demonstrate that higher levels of PAE are linked to significant risks for cognitive, behavioral, and physical development, even though low-level PAE exposure may not cause significant harm to development. The findings underscore the necessity of cautious public health engagement concerning alcohol consumption during pregnancy and emphasize the significance of critical consideration of multiple confounding factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".