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Alcohol Consumption and Acute Myocardial Infarction: A Benefit of Alcohol Consumed With Meals?

2004· article· en· W2043793032 on OpenAlexaff
Livia S. A. Augustin, Silvano Gallus, Alessandra Tavani, Cristina Bosetti, Eva Negri, Carlo La Vecchia

Bibliographic record

VenueEpidemiology · 2004
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMyocardial infarctionAlcohol consumptionAlcoholMedicineConsumption (sociology)Environmental healthFood scienceInternal medicineCardiologyChemistryBiochemistryArt

Abstract

fetched live from OpenAlex

BACKGROUND: The apparent favorable effect of alcohol on the risk of acute myocardial infarction (MI) may be related to its hypoinsulinemic effect when consumed with meals. We studied how the timing of alcohol consumption in relation to meals might affect the risk of MI in a population with relatively high regular alcohol consumption. METHODS: We conducted a case-control study between 1995 and 1999 in Milan, Italy. Cases were 507 subjects with a first episode of nonfatal acute MI, and controls were 478 patients admitted to hospitals for other acute diseases. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by multiple logistic regression models. RESULTS: Compared with nondrinkers, an inverse trend in risk was observed when alcohol was consumed during meals only (for > or =3 drinks per day: OR = 0.50; 95% CI = 0.30-0.82). In contrast, no consistent trend in risk was found for subjects drinking outside of meals (for > or =3 drinks per day: 0.98; 0.49-1.96). The pattern of risk was similar when we considered people who drank only wine. CONCLUSIONS: Alcohol drinking during meals was inversely related with risk of acute MI, whereas alcohol drinking outside meals only was unrelated to risk.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.138
GPT teacher head0.409
Teacher spread0.271 · 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

Citations21
Published2004
Admission routes1
Has abstractyes

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