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Record W3204823323

CHOCOLATE: HEALTH BENEFITS

2019· article· en· W3204823323 on OpenAlexvenueno aff
Ayat Tahir

Bibliographic record

VenueAdvanced Food and Nutritional Sciences · 2019
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsnot available
Fundersnot available
KeywordsDark chocolateFood scienceIngredientPolyphenolHealth benefitsBlood pressureHuman nutritionObesityFood productsMedicineChemistryTraditional medicineBiochemistryInternal medicineAntioxidant
DOInot available

Abstract

fetched live from OpenAlex

Cacao, the quintessential ingredient in all true chocolate and cocoa products is a highly complex food source. The three most common applications of chocolate had been a) inducing weight in emaciated patients b) stimulating the nervous system and c) improving digestion and elimination. The specific effects of chocolate may be attributable to specific constituents such as flavonoids. Over the past decade, there has been increased interest in the potential health benefits related to dietary flavonoids and in particular flavanol consumption. There have been many observational studies to date that have reported a positive correlation between the consumption of dietary flavonoids including flavanols and a reduced risk for cardiovascular disease. The consumption of cocoa flavanol containing food products have been shown to improve endothelial function, insulin sensitivity, and reduce blood pressure. Regular consumption of flavanol containing chocolate bar with added PS (plant sterol) as part of a low-fat diet can significantly lower blood cholesterol levels and also reduce the systolic blood pressure without affecting the body's weight adversely supporting the concept that inclusion of these types of specially formulated foods into a balanced diet may help to support cardiovascular health. An added benefit being the various polyphenols present in chocolate, influence the numerous cytokines and enzyme systems in human body.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.381

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 head0.030
GPT teacher head0.332
Teacher spread0.302 · 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 designOther design
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

Citations0
Published2019
Admission routes1
Has abstractyes

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