Vitamin D supplementation and cardiovascular disease events: a systematic review and pooled meta-analysis of randomized clinical trials
Bibliographic record
Abstract
Several randomized clinical trials have been undertaken to evaluate the effect of vitamin D supplementation on reducing the risk of cardiovascular disease (CVD) or CVD mortality; however, mixed results have been reported. The objective of this analysis is to quantify the effect of vitamin D supplements on CVD events and CVD mortality in adults. PubMed, the Cochrane Library and ClinicalTrial.gov were searched for randomized placebo-control trials on adults using common keywords related to vitamin D and CVDs. Two reviewers independently extracted data. The risk of bias was assessed using the Cochrane tool. Data analysis was done using Comprehensive Meta-Analysis V2 (Biostat, Englewood, NJ, USA) to calculate risk ratio (RR) and 95% confidence interval (95%CI). The systematic review and meta-analysis have been registered at PROSPERO (CRD42020165293). One thousand two hundred twenty four abstracts were retrieved, of which 9 (compromising 114 379 participants) were used. This analysis reveals that compared with placebo, vitamin D did not reduce any CVD events (RR = 0.95, 95%CI: 0.88-1.04), CVD mortality (RR = 1.04, 95%CI: 0.871-1.242), myocardial infarction event (RR = 0.96, 95%CI: 0.83-1.11), or myocardial infarction mortality (RR = 1.527, 95%CI: 0.828-2.816). Current evidence does not support the use of vitamin D for the prevention of major cardiovascular events. PROSPERO Registration Number: (CRD42020165293).
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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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.042 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".