The impact of clinical and population strategies on coronary heart disease mortality: an assessment of Rose’s big idea
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD), the leading cause of death worldwide, has declined in many affluent countries but it continues to rise in industrializing countries. OBJECTIVE: To quantify the relative contribution of the clinical and population strategies to the decline in CHD mortality in affluent countries. DESIGN: Meta-analysis of cross-sectional and prospective studies. DATA SOURCES: PubMed and Web of Science from January 1, 1970 to December 31, 2019. METHOD: We combined and analyzed data from 22 cross-sectional and prospective studies, representing 500 million people, to quantify the relative decline in CHD mortality attributable to the clinical strategy and population strategy. RESULT: The population strategy accounted for 48% (range = 19 to 73%) of the decline in CHD deaths and the clinical strategy accounted for 42% (range = 25 to 56%), with moderate inconsistency of results across studies. CONCLUSION: Since 1970, a larger fraction of the decline in CHD deaths in industrialized countries was attributable to reduction in CHD risk factors than medical care. Population strategies, which are more cost-effective than clinical strategies, are under-utilized.
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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 | 0.079 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".