Relationship between alcohol consumption and myocardial infarction among ageing men using a marginal structural model
Bibliographic record
Abstract
BACKGROUND: Studies on the association between alcohol consumption and myocardial infarction (MI) have typically used baseline data on alcohol consumption and potential confounders. This study aimed at investigating the association between alcohol consumption and MI considering time-varying alcohol consumption and time-varying confounders. METHODS: Data were available for 1030 males participating in the Kuopio Ischaemic Heart Disease Risk Factor Study (Finland). Baseline data for the present study were collected in 1991-93. MIs were ascertained from national registries until December 2005. Alcohol consumption was categorized into four groups. Data were analysed using conventional discrete-time hazard and marginal structural models (MSMs). Time-invariant covariates were age, working status, diabetes and cigarette-years. Time-varying covariates in the MSM were prior alcohol consumption, smoking, history of cardiovascular diseases, body mass index, high-density lipoprotein cholesterol, systolic blood pressure, insulin and fibrinogen. RESULTS: An insignificant increase of MI risk among the heaviest alcohol consumers (≥168 g week(-1)) compared with the reference group (12-83 g week(-1)) was observed when using a conventional model including baseline alcohol consumption and confounders measured prior to baseline [relative risk (RR) = 1.20, 95% confidence interval (95% CI) = 0.68-2.12]. When using a conventional model with time-varying alcohol consumption and adjusting for prior confounders, an increased risk of MI among the heaviest alcohol consumers was revealed (RR = 1.71, 95% CI = 1.03-2.85). There was also a trend towards increased risk among the heaviest consumers using the MSM (RR = 1.59, 95% CI = 0.93-2.72). CONCLUSION: Our findings suggest that standard methods using only baseline data on alcohol consumption and confounders may lead to biased estimates on the association between alcohol consumption and MI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".