Using the concept of ideal cardiovascular health to measure population health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it