Cigarette Smoking and Cardiovascular Disease: Lessons from Framingham
Bibliographic record
Abstract
Cigarette smoking, causing acute and chronic diseases, is a serious threat to the health of the public. The association of smoking with lung cancer was recognized first, but the relationship of smoking to cardiovascular disease was debated into the 1960s and early data from the Framingham cohort found no association. However, in 1962, an analysis combining the data from the Framingham men with the Albany, New York, male cohort found cigarette smoking predicted myocardial infarction, coronary heart disease mortality, and all-cause mortality. The same year, Framingham investigators wrote that smoking was a cardiovascular risk factor independent of other characteristics, such as blood pressure, cholesterol, and smoking cessation, and should be included in any prevention program. The first surgeon general's report was released in 1964 and Framingham investigators were participants in the report's development and provided important data on the association of cigarettes with cardiovascular disease. Subsequent analyses confirmed the early findings on the benefits of quitting for primary prevention of cardiovascular disease and secondary prevention after myocardial infarction. The Framingham investigators and cohort data played a crucial role in the current understanding of the dangers of cigarettes and the subsequent decline of smoking in industrialized countries.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".