Understanding the impact of maternal and infant nutrition on infant/child health: multiethnic considerations, knowledge translation, and future directions for equitable health research
Bibliographic record
Abstract
A mother's intrauterine environment influences her health and that of her offspring, at birth and in the future. Herein, we present an overview of our Canadian Institutes of Health Research (CIHR)-funded grant "Understanding the impact of maternal and infant nutrition on infant/child health"-set within The NutriGen Birth Cohort Alliance. NutriGen is a consortium of four Canadian prospective birth cohorts representing >5000 mother-child pairs of diverse ethnic groups including South Asians, White Europeans, and Indigenous peoples. We summarize our objectives and main findings on outcomes of maternal diet, gestational diabetes, birth weight, cardiometabolic health, the microbiome, and epigenetic modifications. We append this work with 10 key messages when conducting multiethnic research and review our knowledge translation products. We describe the clinical impact of our research on maternal and child health and conclude with future directions on biomarker discovery, expansion to other ethnic groups, and interventions for high-risk populations.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".