Impact of a Prenatal Vitamin D Supplementation Program on Vitamin D Deficiency, Rickets and Early Childhood Caries in an Alaska Native Population
Bibliographic record
Abstract
Background: Early childhood rickets increased in Alaska Native children after decreases in vitamin D-rich subsistence diet in childbearing-aged women. We evaluated the impact of routine prenatal vitamin D supplementation initiated in Alaska’s Yukon Kuskokwim Delta in Fall 2016. Methods: We queried electronic health records of prenatal women with 25(OH) vitamin D testing during the period 2015−2019. We evaluated 25(OH)D concentrations, vitamin D3 supplement refills, and decayed, missing, and filled teeth (dmft) scores and rickets in offspring. Results: Mean 25(OH)D concentrations increased 36.5% from pre- to post-supplementation; the percentage with deficient 25(OH)D decreased by 66.4%. Women with ≥ 60 vitamin D3 refill days had higher late pregnancy 25(OH)D concentrations than those with no refill days (p < 0.0001). Women with late pregnancy insufficient 25(OH)D concentrations had offspring with higher dmft scores than those with sufficient 25(OH)D (RR 1.3, p < 0.0001). Three children were diagnosed with nutritional rickets during the period 2001−2021, and none after 2017. Conclusions: These findings suggest that prenatal vitamin D supplementation can improve childhood outcomes in high-risk populations with high rates of rickets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".