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Record W2091553138 · doi:10.1080/09595230600741057

Is alcohol good or bad for Canadian hearts? A time‐series analysis of the link between alcohol consumption and IHD mortality

2006· article· en· W2091553138 on OpenAlexaboutno aff
Mats Ramstedt

Bibliographic record

VenueDrug and Alcohol Review · 2006
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsnot available
FundersSocialdepartementet
KeywordsMedicinePer capitaDemographyAlcohol consumptionMortality rateConsumption (sociology)PopulationAlcoholEnvironmental healthCigarette smokingGerontologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.116
GPT teacher head0.400
Teacher spread0.285 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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