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Record W4220890073 · doi:10.3390/foods11071002

Acute Effects of Split Pea-Enriched White Pan Bread on Postprandial Glycemic and Satiety Responses in Healthy Volunteers—A Randomized Crossover Trial

2022· article· en· W4220890073 on OpenAlexafffund
Ronak Fahmi, Heather Blewett, Nancy Olson, Donna Ryland, Michel Aliani

Bibliographic record

VenueFoods · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPostprandialCrossover studyFood scienceGlycemicAppetiteMealIngredientGlycemic indexMedicineInsulinChemistryInternal medicine

Abstract

fetched live from OpenAlex

L.) flour on PPGR and appetite-related sensations, and (ii) to determine whether Revtech heat processing of pea flour alters the postprandial effects. A randomized controlled crossover trial was performed with 24 healthy adults. Participants consumed 50 g available carbohydrate from bread containing 20% pea flour that was untreated (USYP), Revtech processed at 140 °C with no steam (RT0%), Revtech processed at 140 °C with 10% steam (RT10%), or a control bread with 100% white wheat flour (100%W). Blood samples were analyzed for glucose and plasma insulin at 0, 15, 30, 45, 60, 90, and 120 min post-meal. Appetite sensations and product acceptability were measured using visual analogue and 9-point hedonic scales. Results showed no significant difference in the postprandial glucose and insulin responses of different bread treatments. However, pea-containing variants resulted in 18% higher fullness and 16-18% lower hunger, desire to eat, and prospective food consumption ratings compared to 100% W. No differences in the aroma, flavor, color, and overall acceptability of different bread products were observed. This trial supports using pea flour as a value-added ingredient to improve the short-term appetite-related sensations of white pan bread without affecting the overall acceptability.

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)0.001
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 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

Citations10
Published2022
Admission routes2
Has abstractyes

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