Population-level interventions for coronary heart disease prevention: what have we learned since the North Karelia project?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The prevalence of coronary heart disease (CHD) risk factors in the population necessitates investment in the design and delivery of effective population-level interventions to prevent and enhance the management of CHD. This review examines the approaches that have been central to the design and delivery of previous, seminal population-level CHD prevention programs; it offers recommendations for the design and evaluation of the next generation of population-level CHD prevention trials. RECENT FINDINGS: Almost 50% of the decline in the rates of CHD mortality in the developed world can be attributed to population-level declines in CHD risk factors, including cholesterol, hypertension, and smoking. There is evidence that community-based CHD prevention interventions can have a positive impact on these risk factors within a distinct population. More recent community-based CHD trials have focused on discrete populations including the socioeconomically deprived, ethnic minorities, and rural communities. SUMMARY: There has been large variability in the success experienced by population-level CHD prevention trials. Best practices have emerged which may be used to inform the design of future trials. These include the need for multisectoral partnerships, coordination of multi-level interventions (programs and policy), and delivering a sufficient intervention dose to targeted populations.
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".