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Record W4414751352 · doi:10.1016/j.amjcard.2025.09.035

Alcohol and Cardiovascular Disease

2025· article· en· W4414751352 on OpenAlexaff
Shyla Gupta, Nilah Ahimsadasan, Kavi Dalsania, Hamza Waraich, Kavi Gupta, Margo Kaminska, Saad Balamane, Sebastián García-Zamora, Andrés F. Miranda‐Arboleda, Juan Farina, Adrián Baranchuk

Bibliographic record

VenueThe American Journal of Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsKingston General HospitalQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsAtrial fibrillationAlcoholic cardiomyopathyDiseaseBinge drinkingDiabetes mellitusHeart failureCoronary artery diseaseHeart diseaseMyocardial infarctionCardiomyopathy

Abstract

fetched live from OpenAlex

Alcohol's impact on cardiovascular health is biphasic: low-to-moderate intake may appear protective, but excessive or binge drinking causes significant harm. This review examines mechanisms linking overconsumption to cardiovascular disease. Acute heavy drinking can trigger "holiday heart syndrome," a transient atrial arrhythmia from electrophysiological instability, autonomic imbalance, and electrolyte shifts. Chronic excess contributes to alcoholic cardiomyopathy via oxidative stress, mitochondrial dysfunction, and impaired calcium handling. Alcohol also promotes atrial fibrillation and hypertension by inducing atrial fibrosis, neurohormonal dysregulation, and endothelial injury. Excessive intake accelerates coronary artery disease and type 2 diabetes through dyslipidemia, vascular inflammation, and insulin resistance, raising risks of stroke, heart failure, and myocardial infarction. While moderate consumption was once thought cardioprotective, emerging evidence-especially for atrial fibrillation-suggests risks may outweigh benefits. In conclusion, public health guidance increasingly emphasizes moderation, individualized assessment, and avoiding binge patterns, particularly for those with underlying cardiovascular vulnerabilities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.359
Teacher spread0.315 · 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 designObservational
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

Citations12
Published2025
Admission routes1
Has abstractyes

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