The effect of dietary micronutrient intake on abdominal aortic calcification: a study protocol for systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Healthy dietary choices have an important role in preventing chronic diseases such as cardiovascular disease (CVD). Increasing evidence suggests micronutrient intake (essential minerals and vitamins) is associated with abdominal aortic calcification (AAC), which is an advanced marker of CVD. However, the existing reports seem inconsistent. Some studies reported micronutrients are associated with a lower risk of AAC, while others have reported an increased risk. Therefore, this systematic review and meta-analysis sought to summarise the available evidence on the association of dietary micronutrient intake on AAC. METHODS AND ANALYSIS: A comprehensive systematic search of the PubMed/MEDLINE, EMBASE, Web of Science and Google Scholar databases from their inception up to September 1, 2024, will be conducted. All clinical studies that report eligible exposure/s (dietary micronutrient intake) and outcome/s (presence/severity of AAC) will be included, and this systematic review and meta-analysis protocol will be reported following the revised Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines. The risk of bias for observational studies will be assessed using the Newcastle-Ottawa Scale and publication bias will be evaluated through visual inspection of funnel plots and the Egger's and Begg's regression tests. The Der Simonian and Laird random-effects model meta-analysis will be calculated to provide pooled results, and the weighted risk ratio with their 95% confidence intervals will be presented. ETHICS AND DISSEMINATION: CRD42024575902.
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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 | 0.096 | 0.139 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".