MétaCan
Menu
Back to cohort
Record W2762621290 · doi:10.1039/c7fo01194f

Effects of the physical-form and the degree-of-saturation of oil on postprandial plasma triglycerides, glycemia and appetite of healthy Chinese adults

2017· article· en· W2762621290 on OpenAlexaff
Sze‐Yen Tan, Elaine Peh, Phei Ching Siow, Alejandro G. Marangoni, Christiani Jeyakumar Henry

Bibliographic record

VenueFood & Function · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
FundersAgency for Science, Technology and Research
KeywordsPostprandialAppetiteMedicineInternal medicineEndocrinologyDegree (music)Saturation (graph theory)InsulinMathematicsPhysics

Abstract

fetched live from OpenAlex

) completed all test visits. The participants consumed a standard dinner and fasted overnight before attending the test session in the following morning. Blood samples were taken before the participants consumed the test meal, and subsequently at fixed intervals. Plasma was analysed for triglycerides, glucose, insulin, and non-esterified fatty acids (NEFA). Appetite sensations were also measured every 30 minutes for 360 minutes. After the test meal consumption, a significant interaction effect (repeated measures ANOVA) was found on temporal changes in triglycerides (p < 0.001). Plasma triglycerides increased significantly in both PO and RBO only, but not in oleogel test meals. PO and RBO also suppressed the rise of glucose (time × treatment effect, p = 0.011) at 20, 30 and 45 min. However, no significant differences were found between palm and rice bran oils in triglycerides and glucose. Changes in insulin, NEFA and appetite did not differ among all treatments. Transformation of oils to oleogels is a novel approach to reduce after-meal triglycerides. This trial was registered with ClinicalTrials.gov as NCT02969057.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.127

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 head0.197
Teacher spread0.188 · 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 designObservational
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

Citations20
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueFood & FunctionSame topicFood Chemistry and Fat AnalysisFrench-language works237,207