Is Plant Sterols a Good Strategy to Lower Cholesterol?
Bibliographic record
Abstract
Cardiovascular disease (CVD) has emerged as the leading cause of dealth worldwide today. Lowering circulating total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) is one of the most effective approaches of CVD prevention. Dietary guidelines and health organizations approved using plant sterols (PS) as the alternative to conventional method in attenuating circulating TC and LDL-C levels and risk of CVD. However, current findings apprear to be controversial on the efficacy of PS. Giving the rise of the field "Nutrigenetics", single nucleotide polymorphisms (SNPs) such as CYP7A1-rs3808607 have been identified that strongly associate with cholesterol metabolism in response to PS intake, towards causing inter-individual variations. This review article aims to discuss the efficacy of dietary PS in managing cholesterol levels based on findings from recent studies. The scope includes reviewing evidence on supporting the efficacy, the metabolic claims, inter-individual variations as well as sitosterolemia associated with PS intake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".