Relationship Between Vitamin D During Perinatal Development and Health
Bibliographic record
Abstract
Vitamin D deficiency is a highly prevalent condition that is present in 40% to 80% of pregnant women. There is emerging evidence that vitamin D deficiency may be a risk modifying factor for many chronic diseases, including osteomalacia, rickets, multiple sclerosis, schizophrenia, heart disease, type 1 diabetes, and cancer. Heightened susceptibility to these diseases may originate in early life during the development of tissue structure and function. It is suspected that biologic mechanisms can "memorize" the metabolic effects of early nutritional environment through fetal and neonatal imprinting. Inadequate vitamin D nutrition during perinatal life may establish a poor foundation that may produce long-term threats to human health. This review summarizes the risks of vitamin D deficiency for human health and provides the current vitamin D recommendations for mothers and their newborns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".