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Record W2331297101 · doi:10.2174/157016111795495567

Methods to Study Postprandial Lipemia

2011· review· en· W2331297101 on OpenAlex
Teik Chye Ooi, Børge G. Nordestgaard

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

VenueCurrent Vascular Pharmacology · 2011
Typereview
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsPostprandialMedicineTriglycerides bloodInternal medicineCholesterolTriglyceride

Abstract

fetched live from OpenAlex

Postprandial lipemia (PPL) refers to a dynamic sequence of plasma lipid/lipoprotein changes induced by ingestion of food. PPL results from absorption of digested dietary lipids which form chylomicrons (CM) and increased hepatic production of VLDL, stimulated by increased delivery of fats to the liver. In general, PPL occurs over 4-6 h in normal individuals, depending on the amount and type of fats consumed. The complexity of PPL changes is compounded by ingestion of food before the previous meal is fully processed. PPL testing is done to determine the impact of (a) exogenous factors such as the amount and type of food consumed, and (b) endogenous factors such as the metabolic/genetic status of the subjects, on PPL. To study PPL appropriately, different methods are used to suit the study goal. This paper provides an overview of the methodological aspects of PPL testing. It deals with markers of postprandial lipoproteins, testing conditions and protocols and interpretation of postprandial data. The influence of the meal itself will not be discussed as it is the subject of another paper in this series.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.139
GPT teacher head0.513
Teacher spread0.374 · 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