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Record W2119055170 · doi:10.1152/ajpheart.00868.2004

Antiatherogenic potential of red wine: clinician update

2005· review· en· W2119055170 on OpenAlexaff
Paul E. Szmitko, Subodh Verma

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2005
Typereview
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsWineMedicineEpidemiologyDiseaseDiabetes mellitusEnvironmental healthAlcohol intakeIntensive care medicineAlcoholInternal medicineFood scienceEndocrinologyBiology

Abstract

fetched live from OpenAlex

Complications of atherosclerosis remain the leading cause of morbidity and mortality in industrialized countries. Epidemiological studies have repeatedly demonstrated that moderate alcohol intake has a beneficial effect on cardiovascular disease. The purpose of this review is to examine the epidemiological and biological evidence supporting the intake of red wine as a means of reducing atherosclerosis. On the basis of epidemiological studies, moderate intake of alcoholic beverages, including red wine, reduces the risk of cardiovascular, cerebrovascular, and peripheral vascular disease in populations. In addition to the favorable biological effects of alcohol on the lipid profile, on hemostatic factors, and in reducing insulin resistance, the phenolic compounds in red wine appear to interfere with the molecular processes underlying the initiation, progression, and rupture of atherosclerotic plaques. Whether red wine is more beneficial than other types of alcohol remains unclear. Definitive data from a large-scale, randomized clinical end-point trial of red wine intake would be required before physicians can advise patients to use wine as part of preventative or medical therapies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.383
Teacher spread0.326 · 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 designNot applicable
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

Citations66
Published2005
Admission routes1
Has abstractyes

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