Maternal dietary intake during pregnancy has longstanding consequences for the health of her offspring
Bibliographic record
Abstract
Over the past 100 years, advances in pharmaceutical and medical technology have reduced the burden of communicable disease, and our appreciation of the mechanisms underlying the development of noncommunicable disease has broadened. During this time, a number of studies, both in humans and animal models, have highlighted the importance of maintaining an optimal diet during pregnancy. In particular, a number of studies support the hypothesis that suboptimal maternal protein and fat intake during pregnancy can have long-term effects on the growing fetus, and increase the likelihood of these offspring developing cardiovascular, renal, or metabolic diseases in adulthood. More recently, it has been shown that dietary intake of a number of micronutrients may offset or reverse the deleterious effects of macronutrient imbalance. Furthermore, maternal fat intake has also been identified as a major contributor to a healthy fetal environment, with a beneficial role for unsaturated fats during development as well as a beneficial impact on cell membrane physiology. Together these studies indicate that attempts to optimise maternal nutrition may prove to be an efficient and cost-effective strategy for preventing the development of cardiovascular, renal, or metabolic diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".