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Wine and Cardiovascular Health

2017· review· en· W2762628517 on OpenAlexaff
Sohaib Haseeb, Bryce Alexander, Adrián Baranchuk

Bibliographic record

VenueCirculation · 2017
Typereview
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineWineEpidemiologyConsumption (sociology)Environmental healthCoronary heart diseaseHealth benefitsDiseaseFood scienceTraditional medicineCardiologyPathology

Abstract

fetched live from OpenAlex

Alcoholic beverages have been consumed for thousands of years, attracting great human interest for social, personal, and religious occasions. In addition, they have long been debated to confer cardioprotective benefits. The French Paradox is an observation of a low prevalence of ischemic heart disease, with high intakes of saturated fat, a phenomenon accredited to the consumption of red wine. Although many epidemiological investigations have supported this view, others have attributed it to beer or spirits, with many suggesting that the drink type is not important. Although excessive consumption of alcoholic beverages is commonly regarded to be detrimental to cardiovascular health, there is a debate as to whether light-to-moderate intake is cardioprotective. Although there is extensive epidemiological support for this drinking pattern, a consensus has not been reached. On the basis of published work, we describe the composition of wine and the effects of constituent polyphenols on chronic cardiovascular diseases.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.425
GPT teacher head0.497
Teacher spread0.072 · 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 designSystematic review
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

Citations222
Published2017
Admission routes1
Has abstractyes

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