Sofia declaration for prevention of cardiovascular diseases and Type 2 diabetes mellitus: a scientific statement of the International College of Cardiology and International College of Nutrition
Bibliographic record
Abstract
Background: There has been persistent emphasis from various health agencies including United Nations on the prevention of cardiovascular diseases (CVDs) and other chronic diseases. This review focusses on the emergence of CVDs and other chronic diseases as well as on modern strategies for their prevention. Methods: A systematic and narrative review was conducted using such reference databases as MEDLINE (PubMed), Web of Science and EBSCO, with additional secondary sources and grey literature searching. Opinions of experts were also sought and discussions followed. Results: The prevalence of primary risk factors for most chronic diseases is rapidly increasing in low and middle income populations due to the on-going economic development and progress. There is a decrease in such risk factors in the developed countries as due to education and adoption of preventive strategies result in a reduction in CVD mortality. Hypertension (5-10%), type 2 diabetes (3- 5%) and CAD (3-4%) are very low in the adult rural populations of India, China, and in the African subcontinent which has less economic development. It seems that it is not poverty, but the lack of health education, possibly due to ineffective policies of national and local governments. In urban and immigrant populations of India and China, which are economically better off, NCDs are significantly higher than they are in some of the highincome populations. Health education and promotion of healthier lifestyle and behaviour appear to be important for prevention in such countries. Conclusion: These findings may require modification of the existing American and European guidelines, proposed for the prevention of CVDs and other chronic diseases, in highincome 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".