Sex-based differences in premature first myocardial infarction caused by smoking: twice as many years lost by women as by men
Bibliographic record
Abstract
BACKGROUND: It has been debated whether smoking increases the risk of heart disease relatively more in women than in men. It is not known whether there are sex differences with regard to how many years prematurely smoking causes acute myocardial infarction (AMI) to occur. We aimed to determine how smoking affects the age of onset of first myocardial infarction in both the sexes. DESIGN: Clinical data were consecutively entered into a database and were analysed with a multivariate regression technique. METHODS: In the years 1998-2005, data on 1784 consecutive patients (38.3% women) who were discharged from or died in a district general hospital with a diagnosis of first myocardial infarction were included in the study. Age at first AMI was analysed. RESULTS: Unadjusted mean ages were 76.2 years for women and 69.8 years for men, a difference of 6.4 years (P<0.001). Mean age within the various groups was: women nonsmokers 80.7 years, women smokers 66.2 years, difference 14.4 years (P<0.001); men nonsmokers 72.2 years, men smokers 63.9 years, difference 8.3 years (P<0.001). After adjustment for risk factors (hypertension, cholesterol levels, diabetes) and patient characteristics (history of angina, history of stroke) 13.7 years of the age difference in women were attributed to smoking; the corresponding figure in men was 6.2 years (P<0.001). CONCLUSION: First AMI occurred significantly more prematurely in women than in men smokers, implying that twice as many years were lost by women as by men smokers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".