Maternal Vitamin D Supplementation and Infantile Rickets: Secondary Analysis of a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The role of maternal vitamin D supplementation in the prevention of infantile rickets is unknown, particularly in low- and middle-income countries without routine infant vitamin D supplementation. Through secondary analysis of a randomized, placebo-controlled trial in Bangladesh, we examined the dose-ranging effects of maternal vitamin D supplementation on the risk of biochemical rickets at 6 to 12 months of age. METHODS: Pregnant women (n = 1300) were randomized into 5 groups: placebo, or vitamin D 4200 IU/week, 16 800 IU/week, or 28 000 IU/week from second trimester to delivery and placebo until 6 months postpartum; or 28 000 IU/week prenatally and until 6 months postpartum. Infants underwent biochemical rickets screening from 6 to 12 months of age (n = 790). Relative risks (RR) and 95% confidence intervals (95% CI) of biochemical rickets were estimated for each group versus placebo. RESULTS: Overall, 39/790 (4.9%) infants had biochemical rickets. Prevalence was highest in the placebo group (7.8%), and the risk was significantly lower among infants whose mothers received combined prenatal and postpartum vitamin D at 28 000 IU/week (1.3%; RR, 0.16; 95% CI, 0.03-0.72). Risks among infants whose mothers received only prenatal supplementation (4200 IU, 16 800 IU, 28 000 IU weekly) were not significantly different from placebo: 3.8% (RR, 0.48; 95% CI, 0.19-1.22), 5.8% (RR, 0.74; 95% CI, 0.33-1.69), and 5.7% (RR, 0.73; 95% CI, 0.32-1.65), respectively. CONCLUSIONS: Maternal vitamin D supplementation (28 000 IU/week) during the third trimester of pregnancy until 6 months postpartum reduced the risk of infantile biochemical rickets. Further research is needed to define optimal postpartum supplementation dosing during lactation.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".