Effect of Interventions Aimed at Reducing or Modifying Saturated Fat Intake on Cholesterol, Mortality, and Major Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Debates about optimal saturated fat advice continue. PURPOSE: To systematically summarize randomized trial data on reducing or modifying saturated fat intake on cholesterol, mortality, and major cardiovascular events. DATA SOURCES: MEDLINE, Embase, and Cochrane Central Register of Controlled Trials from inception to July 2025. STUDY SELECTION: Eligible trials enrolled adults with or without cardiovascular disease and studied the effect of reducing or modifying saturated fat intake. DATA EXTRACTION: Standard Cochrane methods. DATA SYNTHESIS: for interaction = 0.05; moderate credibility of subgroup effect based on Instrument to assess the Credibility of Effect Modification Analyses assessments). LIMITATIONS: Data were limited on the replacement of saturated fat with monounsaturated fat or protein. Trials varied considerably in their efficacy in reducing saturated fat intake and in their replacement macronutrients and concomitant dietary interventions, and new trials are needed to clarify uncertainty. CONCLUSION: For persons at low cardiovascular risk, reducing or modifying saturated fat intake has little or no benefit over a period of 5 years. Among persons at high cardiovascular risk, low- to moderate-certainty evidence was found for important reductions in mortality and major cardiovascular events, particularly for MI, with respect to replacing saturated fat with polyunsaturated fat. PRIMARY FUNDING SOURCE: None. (PROSPERO: CRD42023387377).
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".