Cardiovascular Risk Factors and Prevention: A Perspective From Developing Countries
Bibliographic record
Abstract
By the beginning of the 21st century, cardiovascular disease (CVD) had become the leading cause of premature mortality and morbidity worldwide, with 80% originating from less developed lower-income countries in line with societal and economic developments. Extensive research on causes and risk factors have been carried out since the mid-20th century and have established individual factors such as smoking, hypertension, diabetes, and dyslipidemia as CVD risk factors, followed by others. Two recent major case-control studies have summarized the role of common major CVD risk factors in determining the risk of myocardial infarction (INTERHEART study) and stroke (INTERSTROKE study). They showed that 9 and 10 common risk factors accounted for > 90% of the risk of myocardial infarction and stroke, respectively, and established the focus in prevention of these common CVDs. The efficacy of lowering blood pressure, blood glucose, and lipid-lowering therapies has been shown to reduce subsequent morbidity and mortality. Leading international health organizations have published guidelines that are updated regularly to set the standards for providing guidance for implementation and management of risk factors. Interventions can also be costly and long-term adherence, essential to be effective in reducing risks, tends to decrease drastically with time. Dietary recommendations have been incorporated into national and professional guidelines for CVD prevention since the 1960s. On the basis of new research, some existing dietary recommendation might be outdated and should be reviewed, and revised, if necessary. A perspective of CVD prevention and treatment in developing countries is highlighted.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".