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Record W2967922157 · doi:10.5650/jos.ess19116

Is Plant Sterols a Good Strategy to Lower Cholesterol?

2019· review· en· W2967922157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Oleo Science · 2019
Typereview
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsCholesterolSingle-nucleotide polymorphismPlant sterolsLdl cholesterolDiseaseMedicineInternal medicineEndocrinologyBiologySterolBiochemistryGenotype

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.404
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it