Maternal Vitamin D Status among Different Ethnic Groups and Its Potential Contribution to Adverse Pregnancy and Child Outcomes
Bibliographic record
Abstract
Maternal vitamin D deficiency in pregnancy is a widespread public health concern. Race and ethnicity as biological and cultural factors, respectively, can affect vitamin D status through differences in skin color, sunlight exposure, and dietary intake. Low maternal vitamin D status in pregnancy may affect both mother and fetus adversely. Vitamin D deficiency and insufficiency are linked to a wide variety of adverse pregnancy outcomes such as gestational diabetes, preeclampsia, and preterm delivery. Furthermore, maternal vitamin D deficiency has been linked to several adverse health outcomes in infants and children. The examples include, but not limited to, impaired growth, skeletal problems, and autoimmune diseases such as type 1 diabetes and asthma. This chapter reviews the vitamin D status during pregnancy across different ethnic groups, looking into the adverse pregnancy and child outcomes, followed by a discussion on the association between maternal and child vitamin D status and successful interventions. Strong evidence exists about the association between vitamin D and some health outcomes during pregnancy, while more studies are needed to confirm the other claim. The existing body of evidence justifies the need for well-designed policies and systematic interventions to ensure optimal vitamin D status of pregnant women and their offsprings across different ethnic and racial groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".