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Record W2581570171 · doi:10.1093/nutrit/nuw057

Chlorogenic acid from coffee beans: evaluating the evidence for a blood pressure–regulating health claim

2016· review· en· W2581570171 on OpenAlexaffabout
Tara B. Loader, Carla G. Taylor, Peter Zahradka, Peter J.H. Jones

Bibliographic record

VenueNutrition Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChlorogenic acidContext (archaeology)Environmental healthHealth claims on food labelsMedicineCardiovascular healthBlood pressureHealth benefitsPsychological interventionDiseaseGerontologyFood scienceTraditional medicineBiologyEndocrinologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

The consumption of coffee has been associated with a number of health benefits, including a reduced risk of cardiovascular disease. Hypertension is an important risk factor for adverse cardiovascular events. Coffee may help reduce blood pressure (BP) in humans, which might be attributable to its polyphenolic compound, chlorogenic acid. The high incidence of hypertension among Canadians underscores the need for new and effective strategies to reduce BP. Dietary interventions may constitute such a strategy, but consumers need to be informed about which foods are most effective for regulating BP. To guide healthy eating, Health Canada permits the use of health claims on the labels of foods that confer health benefits. Currently, there is only one health claim for BP regulation. Additional health claims for foods that assist in BP regulation are therefore warranted. This review provides background information on chlorogenic acid and examines the evidence regarding the use of chlorogenic acid for BP regulation in the context of Health Canada's health claims framework.

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)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.489
GPT teacher head0.551
Teacher spread0.062 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations38
Published2016
Admission routes2
Has abstractyes

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