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Record W2329183575 · doi:10.1097/mol.0b013e328353292e

Plasma noncholesterol sterols

2012· review· en· W2329183575 on OpenAlexafffund
Dylan MacKay, Peter J.H. Jones

Bibliographic record

VenueCurrent Opinion in Lipidology · 2012
Typereview
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsDyslipidemiaCholesterolCholesterol synthesisSterolMedicinePharmacologyBiologyInternal medicineBiochemistryDiseaseReductase

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Noncholesterol sterols (NCSs) in plasma encompass endogenous cholesterol precursors and exogenous phytosterols and cholesterol metabolites, which are used as surrogate measures of cholesterol synthesis and cholesterol absorption, respectively. The ratios of cholesterol synthesis to cholesterol absorption surrogates are also utilized to assess the overall balance of cholesterol metabolism, with higher values representing more synthesis and lower values more absorption. The objective of this review is to focus on recent findings using plasma NCSs and their potential in customizing dietary and pharmacological hypolipidemic therapies. RECENT FINDINGS: NCSs are often used to assess the impact of pharmacological and dietary interventions on cholesterol metabolism. Various forms of dyslipidemia have been characterized using NCSs, and NCSs may be a valuable tool in selecting appropriate treatment therapies. NCSs levels are affected by genetic, dietary and physiological factors and have been related to cardiovascular disease risk. SUMMARY: The expanded use of plasma NCSs is currently limited by the lack of standardized methodology. However, noncholesterol sterols are still a valuable research tool for the overall assessment of cholesterol metabolism and may have clinical potential in the personalization of diet and medicine.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.253
GPT teacher head0.446
Teacher spread0.194 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations38
Published2012
Admission routes2
Has abstractyes

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