Shedding light on gestational loss: The role of Vitamin D 25(OH)D deficiency in miscarriage – A systematic review and meta-analysis
Bibliographic record
Abstract
A BSTRACT Objectives: Miscarriage is a common pregnancy complication with various contributing factors. Recent studies suggest that maternal Vitamin D deficiency may increase the risk of early pregnancy loss. Vitamin D is essential for immune regulation, placental development, and fetal growth. This meta-analysis aimed to evaluate the association between maternal Vitamin D status and miscarriage. Materials and Methods: This meta-analysis followed PRISMA 2020 guidelines, including observational studies from PubMed, EMBASE, Scopus, Web of Science, and Cochrane (2018–2025). Eligible studies reported serum 25(OH)D levels and miscarriage outcomes. Data on serum Vitamin D, miscarriage incidence, and odds ratios (ORs) were extracted. Study quality was assessed using the Newcastle–Ottawa Scale. Statistical analyses were performed using Review Manager 5.4 and R, with evaluation of heterogeneity and publication bias. Results: Women who experienced miscarriage had significantly lower Vitamin D levels than those with ongoing pregnancies (mean difference: −5.48 ng/mL; 95% confidence interval [CI]: −9.77 to −1.19; P = 0.02). The miscarriage rate was higher among Vitamin D-deficient women (34%; 95% CI: 0.21–0.47) than in those with sufficient levels (16%; 95% CI: 0.08–0.24). Deficiency was significantly associated with miscarriage risk (OR: 2.02; 95% CI: 1.37–2.98; P = 0.0004). No significant publication bias was observed. Conclusion: Maternal Vitamin D deficiency is significantly associated with an increased risk of miscarriage. These findings support the potential benefit of assessing and optimizing Vitamin D status in preconception and antenatal care to improve the pregnancy outcomes.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".