Diabetes and cardiovascular disease in South Asians: A global perspective
Bibliographic record
Abstract
South Asians (Indians, Pakistanis, Bangladeshis, and Sri Lankans), have very high incidence of metabolic diseases, such as hypertension, abdominal obesity, metabolic syndrome, type-2 diabetes, and vascular disease. To create awareness, develop educational and preventive strategies, we started a professional society, South Asian Society on Atherosclerosis and Thrombosis (SASAT) in 1993, at the University of Minnesota. Since that time, we have organized fifteen international conferences in India and published several monographs on this topic. In our conferences, we have discussed all aspects of epidemiology, risk factors, and excess burden of these diseases in this ethnic group in India and abroad. In general, South Asians seem to have excess incidence of diabetes and coronary artery disease, no matter which country they live. There are speculations about the reasons for this excess; however, no definite risk factor or a cluster of risks have been attributed to be responsible for this excess disease burden. National health programs in various countries, such as the UK, and Canada, with large number of South Asian Immigrants, have developed ethnic-specific preventive measures. The World Health Organization has issued special guidelines about the BMI cutoff, for this ethnic group. During the tenure of the President William Clinton, recognizing the important role the South Asian community has played in the USA, he recommended some studies related to their health. Again in 2009, President Barack Obama signed an executive order, calling for strategies to improve the health of Asian Americans. In a recent issue of the Journal of Circulation, the American Heart Association has published a scientific statement about the atherosclerotic disease in the South Asians living in the USA. The Vice chair of one of the councils, Dr Latha Palaniappan also has published a companion report called, “Call to Action”: A science advisory from the AHA. In this overview, we will discuss briefly the work of SASAT, and present our views with a global perspective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".