Using the concept of ideal cardiovascular health to measure population health
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article describes the recent literature from January 2014 to March 2015 examining the cardiovascular health of populations using the concept of ideal cardiovascular health with a particular focus on cardiovascular health in different countries and the association with subclinical markers of cardiovascular disease (CVD). RECENT FINDINGS: The relatively new concept of ideal cardiovascular health, based on the presence of seven healthy behaviours and factors including nonsmoking, active physical activity, healthy diet, low body mass index, low blood pressure, glucose, and cholesterol, can be used to assess a population's health status and develop an understanding of how cardiovascular health is associated with biological disease processes and clinical outcomes such as CVD incidence and mortality. Recent studies have adapted the American Heart Association definition of ideal cardiovascular health to fit the available data in different countries and have shown that the prevalence of ideal cardiovascular health is low in populations worldwide, including North America, Europe, Asia, and the Middle East. Recent studies have also uncovered strong associations between ideal cardiovascular health metrics and subclinical markers for CVD such as coronary artery calcification, carotid intima-media thickness, and pulse wave velocity. SUMMARY: A number of studies have demonstrated the low prevalence of ideal cardiovascular health in several countries and a strong relationship with subclinical CVD and biomarkers. The association with subclinical markers for CVD provides some evidence of the intermediary biological pathways through which ideal cardiovascular health results in a lower incidence of CVD and highlights the importance of improving cardiovascular health metrics in the general population.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".