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Record W4210903244 · doi:10.1177/11786388221076962

Sugar-Free Dark Chocolate Consumption Results in Lower Blood Glucose in Adults With Diabetes

2022· article· en· W4210903244 on OpenAlexaff
Bárbara Oliveira, Kaja Falkenhain, Jonathan P. Little

Bibliographic record

VenueNutrition and Metabolic Insights · 2022
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsDark chocolatePostprandialCrossover studyDiabetes mellitusType 2 diabetesSugarErythritolBlood sugarInulinMedicineArea under the curveFood scienceChemistryEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Diabetes is characterized by an impaired ability to appropriately control blood glucose. Postprandial hyperglycemia, in particular, is associated with complications in people with type 1 diabetes (T1D) and type 2 diabetes (T2D). The objective of this study was to determine how sugar-free dark chocolate sweetened with stevia, erythritol, and inulin impacts postprandial blood glucose levels in individuals with diabetes compared to conventional dark chocolate. In a randomized crossover design, 13 participants consumed 1 bar (34 g) of sugar-free dark chocolate or 1 bar (34 g) of conventional dark chocolate with glucose levels measured before and throughout a 120-min postprandial period. The incremental area under the curve (iAUC) was lower after the consumption of sugar-free dark chocolate (−65%, P = .04) compared to conventional dark chocolate. No significant differences between chocolates were found for peak glucose value above baseline, the total area under the curve, or peak glucose values. Our results suggest that a sugar-free dark chocolate bar sweetened with stevia, erythritol and inulin led to a lower blood glucose iAUC compared to the conventional dark chocolate bar in people with diabetes, whilst longer-term effects on glucose control remain to be determined.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.559

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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