An abundance of seafood consumption studies presents new opportunities to evaluate effects on neurocognitive development
Bibliographic record
Abstract
The relationship between seafood eaten during pregnancy and neurocognition in offspring has been the subject of considerable scientific study for over 25 years. Evaluation of this question led two scientific advisory committees to the Dietary Guidelines for Americans (DGAC), the Food and Agriculture Organization of the United Nations with the World Health Organization (FAO/WHO), Health Canada, the European Food Safety Authority (EFSA), and the U.S. Food and Drug Administration (FDA) to conclude through 2014 that seafood consumed by pregnant women is likely to benefit the neurocognitive development of their children. The evidence they reviewed included between four and ten studies of seafood consumption during pregnancy that reported beneficial associations. In contrast there are now 29 seafood consumption studies available describing over 100,000 mothers-child pairs and 15 studies describing over 25,000 children who ate seafood. A systematic review of these studies using Nutrition Evaluation Systematic Review methodology is warranted to determine whether recent research corroborates, builds on, or significantly alters the previous conclusions. Studies that evaluate the integrated effects of seafood as a complete food more directly and completely evaluate impacts on neurocognition as compared to studies that evaluate individual nutritients or toxicological constituents in isolation. Here we address how the findings could add to our understanding of whether seafood consumed during pregnancy and early childhood affects neurocognition, including whether such effects are clinically meaningful, lasting, related to amounts consumed, and affected by any neurotoxicants that may be present, particularly mercury, which is present at varying levels in essentially all seafood. We provide the history, context and rationale for reexamining these questions in light of currently available data.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".