MATERNAL VITAMIN D DEFICIENCY AND RISK OF PRETERM BIRTH: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Preterm birth remains a leading cause of neonatal morbidity and mortality worldwide, with significant implications for long-term child health. Among the various maternal risk factors, vitamin D deficiency has gained attention for its potential role in adverse pregnancy outcomes, including preterm delivery. However, the current literature presents inconsistent findings, necessitating a comprehensive evaluation of existing evidence to clarify this association. Objective: This systematic review aims to assess the relationship between maternal vitamin D deficiency during pregnancy and the risk of preterm birth. Methods: A systematic review was conducted following PRISMA guidelines. Databases including PubMed, Scopus, Web of Science, and the Cochrane Library were searched for studies published between 2019 and 2024. Inclusion criteria encompassed observational studies and randomized controlled trials examining maternal 25(OH)D levels and preterm birth outcomes. Studies involving animals, non-English publications, and lacking full-text access were excluded. Two independent reviewers conducted screening, data extraction, and quality assessment using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. Results: Eight studies involving 15,392 pregnant women were included, comprising five cohort and three case-control studies. All studies reported a statistically significant association between maternal vitamin D deficiency (commonly defined as <20 ng/mL) and increased risk of preterm birth. Adjusted odds ratios and risk ratios across studies indicated a consistent trend, with p-values ranging from <0.05 to <0.001. Risk of bias was generally low to moderate. Conclusion: Maternal vitamin D deficiency is significantly associated with an elevated risk of preterm birth. These findings support the clinical relevance of monitoring and managing vitamin D levels during pregnancy. Nonetheless, further large-scale randomized trials are needed to confirm causality and define optimal intervention strategies.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".