Is alcohol good or bad for Canadian hearts? A time‐series analysis of the link between alcohol consumption and IHD mortality
Bibliographic record
Abstract
The objective of this study was to analyse the population level association between alcohol consumption and ischaemic heart disease (IHD) mortality in Canada. Yearly changes in IHD mortality rates from 1950 to 1998 were analysed in relation to yearly changes in alcohol consumption, employing the Box & Jenkins technique for time-series analyses. All models controlled for cigarette smoking and one analysis with focus on men also included female IHD mortality as an indicator of other risk factors for IHD. A 1-litre increase in per capita alcohol consumption was associated with an increase in overall IHD mortality as well as among men and women with fully 1%, but no estimate reached statistical significance. A positive and significant relationship between smoking and IHD mortality was demonstrated in all models. According to the model with focus on male IHD mortality, an increase in per capita consumption by 1 litre was related significantly to a 1% increase in male IHD mortality. No significant effects were found in different male age groups. The idea that alcohol saves more IHD deaths than it causes in Canada is not in accordance with these findings. An increase in overall alcohol consumption is more likely to cause an increase in IHD mortality than to lower the number of IHD deaths, at least among men.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".