Effects of prenatal alcohol exposure (PAE): insights into FASD using mouse models of PAE
Bibliographic record
Abstract
The potential impact of prenatal alcohol exposure (PAE) varies considerably among exposed individuals, with some displaying serious alcohol-related effects and many others showing few or no overt signs of fetal alcohol spectrum disorder (FASD). In animal models, variables such as nutrition, genetic background, health, other drugs, and stress, as well as dosage, duration, and gestational timing of exposure to alcohol can all be controlled in a way that is not possible in a clinical situation. In this review we examine mouse models of PAE and focus on those with demonstrated craniofacial malformations, abnormal brain development, or behavioral phenotypes that may be considered FASD-like outcomes. Analysis of these data should provide a valuable tool for researchers wishing to choose the PAE model best suited to their research questions or to investigate established PAE models for FASD comorbidities. It should also allow recognition of patterns linking gestational timing, dosage, and duration of PAE, such as recognizing that binge alcohol exposure(s) during early gestation can lead to severe FASD outcomes. Identified patterns could be particularly insightful and lead to a better understanding of the molecular mechanisms underlying FASD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".