Multivitamin use and risk of preeclampsia: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Preeclampsia is associated with adverse maternal and neonatal outcomes. It is unclear whether multivitamin use reduces the risk of preeclampsia. This systematic review and meta-analysis aimed to evaluate the association between multivitamin use and the risk of preeclampsia. MATERIAL AND METHODS: We searched PubMed, Embase and the Cochrane Library from database inception to July 2021. Randomized controlled trials (RCTs), case-control and cohort studies assessing the association between multivitamin use and risk of preeclampsia were eligible. Studies of treatment with a single micronutrient were excluded. Relative risks and 95% confidence intervals (95% CI) were calculated using random-effects models. RoB2, the Newcastle Ottawa Scale and GRADE were used to assess risk of bias and quality of evidence. The protocol was registered in PROSPERO (no. CRD42021214153). RESULTS: Six studies were included (33 356 women). Only two RCTs were found, both showing a significantly decreased risk of preeclampsia in multivitamin users. These studies were not compatible for meta-analysis due to clinical heterogeneity. A meta-analysis of observational studies using a random-effects model showed an unchanged risk of preeclampsia following multivitamin use (relative risk 0.85, 95% CI 0.69-1.03). The quality of evidence according to GRADE was very low. CONCLUSIONS: Very weak evidence suggests that multivitamin use might reduce the risk of preeclampsia; however, more research is needed. Large RCTs should be prioritized. The results of this review do not allow any final conclusions to be drawn regarding a preventive effect of multivitamin use in relation to preeclampsia.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".