Effects of Vitamin D Levels on Cardiovascular Diseases: A SystematicReview
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) are a group of pathologies that involve heart and blood vessel disorders and are considered the main cause of death in the world. Epidemiological studies have shown the association between low vitamin D (VD) levels and CVD. This vitamin, in addition to acting on bone metabolism, plays a role in modulating the cardiovascular system. Objective: The present study assesses the effects of VD levels on CVD through a systematic literature review. Method: For this purpose, the PICO strategy was used to prepare the guiding question, and articles were searched in the PubMed, Scopus, Science Direct, and Web of Science databases by two authors independently. To guarantee the quality of the evidence of the articles included in the review, the Newcastle-Ottawa scale was used. The literature review followed the PRISMA recommendations. Results: In this review, 22,757 articles were identified, but only 10 were considered eligible, of which 70 % are prospective cohorts and 30 % are retrospective cohorts. The study showed that low 25(OH)D levels correlate with an increased risk of cardiovascular events and death from CVD, including in patients who had preexisting CVD. However, one article did not demonstrate this association. Conclusion: As a result, VD correlates with cardiovascular events and the risk of death.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".