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Record W4249372549 · doi:10.1080/096374800111112

The effect of palmitic acid on lipoprotein cholesterol levels

2000· article· en· W4249372549 on OpenAlexaff
M. Thomas Clandinin, Stephanie Cook, S. D. Konard, Margaret A. French

Bibliographic record

VenueInternational Journal of Food Sciences and Nutrition · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCholesterolPalmitic acidChemistryInternal medicineEndocrinologyLipoproteinEndogenyMedicineBiochemistryFatty acid

Abstract

fetched live from OpenAlex

The present study assessed the effect of high versus low palmitic acid intakes of plasma lipoprotein cholesterol levels and on rates for endogenous synthesis of cholesterol in normal and hypercholesterolemic subjects. On day 21 of each diet treatment, a fasting blood sample was drawn for lipoprotein determination and to provide a measure of the background level of deuterium. A priming dose of deuterium was consumed and a second blood sample obtained 24 hours after the first sample. Isotope ratio mass spectrometry was used to determine the incorporation of deuterium into the newly synthesized cholesterol molecule and fractional synthetic rates were calculated. Four diets were formulated to provide combinations of two levels of 16 : 0 at two levels of 18 : 2n–6. Subjects received each of the four diet treatments for 21 days, followed by washout periods of 21 days. Serum total cholesterol and LDL-cholesterol was not significantly affected by the high level of 16 : 0 when diets also contained a high level of 18 : 2n–6. Fractional synthesis rates of cholesterol observed for each diet treatment did not differ significantly, suggesting no relationship between the endogenous synthesis of cholesterol and dietary 16 : 0 content. The results indicate that 16 : 0 has no effect on serum lipoprotein profiles in the presence of recommended intakes for 18 : 2n–6

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.120

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.268
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations34
Published2000
Admission routes1
Has abstractyes

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