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Record W2923617071 · doi:10.3390/nu11040717

Effect of PolyGlycopleX (PGX) Consumption on Blood Lipid Profiles in Healthy, Low CVD Risk Overweight Adults

2019· article· en· W2923617071 on OpenAlexaff
Vicky Solah, Deborah A. Kerr, Deasy Irawati, Wendy Hunt, Xingqiong Meng, Roland J. Gahler, Haelee Fenton, Stuart Johnson, Anthony P. James, Simon Wood

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverweightMedicineEnvironmental healthConsumption (sociology)ObesityInternal medicineFood scienceChemistry

Abstract

fetched live from OpenAlex

Raised blood lipid levels are associated with a risk of a cardiovascular disease (CVD). Moderate reductions in several CVD factors such as total, low-density lipoprotein (LDL) cholesterol and non-high-density lipoprotein (non-HDL) cholesterol concentrations may be more effective in reducing overall risk than a major reduction in just one. A blind, randomised controlled trial was conducted with 120 healthy overweight (BMI 25⁻30) adults aged 25⁻70 years who were non-smokers, not diabetic and of low risk of cardiovascular disease, as assessed by the Framingham risk equation. Participants consumed 4.5 g PolyGlycopleX (PGX) as softgel capsules (PGXS) or 5 g PGX granules (PGXG) or 5 g rice flour (RF) with meals three times a day for 12 weeks. Total, LDL and non-HDL cholesterol were all significantly reduced (-6%, -5% and -3.5%, respectively) post the PGX granule treatment; however, PGX in softgel capsule form did not affect blood lipid profiles. Daily consumption of PGX granules in overweight low CVD risk adults produced lipid changes indicating a CVD preventative benefit.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.304
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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