Vitamin D Deficiency in Pregnant Women and Their Neonates
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Vitamin D deficiency during pregnancy is a worldwide problem. Studies have reported prevalence ranged 18-84% in pregnant women. Receiving adequate calcium and vitamin D during pregnancy period is necessary for calcium homeostasis, fetal growth and bone mineralization. This study was aimed to determine the prevalence of vitamin D deficiency in pregnant women and their neonates in Shahroud city in the northeast Iran. METHODS: In this cross-sectional study, 284 pregnant women and their neonates referred to Fatemiyeh Hospital of Shahroud were included. Blood samples of mothers and umbilical cords were collected during the delivery and were sent to laboratory in order to measure calcium and 25-hydroxy vitamin D. FINDINGS: Amounts of Vitamin D insufficiency (20-30 ng/mL) and deficiency (<20 ng/mL) in (mothers, neonates) were found to be (60.2%, 48.9%) and (1.1%, 2.5%) respectively. Calcium deficiency (<8.5 mg/dL) was present in 33.5% of mothers and 25% of neonates. There was a weak correlation between maternal serum and cord blood 25-hydroxy vitamin D (r=0.12, p=0.053). CONCLUSION: More than half of the mothers and their neonates had some degrees of vitamin D deficiency. It is recommended to evaluate the nutritional status of vitamin D in pregnant women along with public health interventions to be carried out.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".