Red meat consumption, risk of incidence of cardiovascular disease and cardiovascular mortality, and the dose–response effect
Bibliographic record
Abstract
BACKGROUND: Red and (particularly) processed meats are high in cholesterol and saturated and solid fatty acids. Their consumption is considered one of the risk factors for metabolic disorders. Numerous studies demonstrated a possible association between red meat consumption and cardiovascular disease (CVD). In this protocol, we propose a systematic review of the literature to examine the associations of red meat consumption with CVD incidence and mortality, and explore the potential dose-response relationship. METHODS: We will search MEDLINE/PubMed, Scopus, SciELO, LILACS, ScienceDirect, Web of Science, Cochrane (CENTRAL), WHOLIS, PAHO, and Embase. We will include prospective epidemiological studies (longitudinal cohort). Risk of bias will be assessed using the Newcastle-Ottawa scale (NOS). Four independent researchers will conduct all evaluations. Disagreements will be referred to a fifth reviewer. We will summarize our findings using a narrative approach and tables to describe the characteristics of the included studies. The heterogeneity between trial results will be evaluated using a standard chi-squared test with P < .05. We will conduct the study in accordance with the guideline of the Preferred Reporting Items for Systematic Review and Meta-analyses Protocols (PRISMA-P). RESULTS: This review will evaluate the association between red meat consumption and incidence of CVD and mortality (primary outcome measures). The secondary outcome measure will include the dose-response effect. CONCLUSION: The findings of this systematic review will summarize the latest evidence of the association between red meat consumption and incidence of CVD and mortality and the dose-response effect through a systematic review and meta-analysis. REGISTRATION: PROSPERO CRD42019100914.
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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 | 0.037 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".