The Burden of Cardiovascular Events According to Cardiovascular Risk Profile in Adults from High-, Middle- and Low-Income Countries
Bibliographic record
Abstract
Background Current strategies to prevent adverse cardiovascular outcomes focus on secondary prevention and primary prevention in high-risk groups. The proportion of events occurring in lower-risk groups globally is unknown. Methods We prospectively documented fatal or non-fatal myocardial infarction, stroke, heart failure, or any other fatal cardiovascular event stratified by history of cardiovascular disease (CVD), and by the INTERHEART and the Framingham risk score in those without prior CVD, in 189,097 adults from 26 high-, middle- and low-income countries. Results Participants mean±SD age was 51±10 years and 59% were women. We observed 14,829 outcome events affecting 8% of the cohort during a median 12.4 years follow-up. Overall, 44% of outcome events occurred in CVD-naive participants at low or intermediate INTERHEART risk and 56% occurred in in CVD-naive participants at non-high Framingham risk. The proportion of adverse cardiovascular outcomes occurring in these lower risk groups was inversely related to country income level and was higher in women (55%) than in men (35%). Conclusions To achieve a substantial population-level reduction in CVD, preventive strategies for CVD are essential in those considered low- or intermediate-risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".