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Record W2581275469 · doi:10.3390/nu9020091

Effect on Insulin, Glucose and Lipids in Overweight/Obese Australian Adults of 12 Months Consumption of Two Different Fibre Supplements in a Randomised Trial

2017· article· en· W2581275469 on OpenAlexaff
Sebely Pal, Suleen Ho, Roland J. Gahler, Simon Wood

Bibliographic record

VenueNutrients · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverweightMedicinePsylliumInternal medicineInsulinCholesterolObesityRandomized controlled trialEndocrinologyFood scienceBiologyDietary fiber

Abstract

fetched live from OpenAlex

(PGX), a complexed polysaccharide, on insulin, glucose and lipids in overweight and obese individuals. In this double-blind 12 months study, participants were randomised into three groups: control (rice flour); PGX or psyllium (PSY). Participants followed their usual lifestyle and diet but consumed 5 g of their supplement before meals. Insulin was significantly lower in the PGX and PSY groups compared to control at 3 and 6 months and in the PSY group compared to control at 12 months. Serum glucose was significantly lower in the PGX group at 3 months compared to control. Total cholesterol was significantly lower in the PGX and PSY groups compared to control at 3 and 6 months. High density lipoprotein (HDL) cholesterol was significantly increased in the PGX group compared to control at 12 months. low density lipoprotein (LDL) cholesterol was significantly lower in the PGX group at 3 and 6 months compared to control and in the PSY group at 3 months compared to control. A simple strategy of fibre supplementation may offer an effective solution to glucose, insulin and lipid management without the need for other nutrient modification.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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 designRandomized trial
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

Citations30
Published2017
Admission routes1
Has abstractyes

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