Commentary: Cardiovascular implications of the epidemiological transition for the developing world: Thailand as a case in point
Bibliographic record
Abstract
Cardiovascular disease (CVD) is currently the leading cause of death and disability in developed nations, and is increasing rapidly in the developing world.1 If demographic trends continue, it is estimated that 90% of the global CVD burden will occur in low and middle-income countries by the year 2025. The rapid increase in CVD rates in developing regions is occurring at a time when infectious and nutritional deficiency diseases are in decline, a phenomenon that has been termed ‘the epidemiologic transition’.2 East Asia, in particular, is expected to suffer some of the largest increases in CVD morbidity and mortality in coming years. The reasons for the epidemiological transition are several-fold. As developing countries undergo economic and social transformation, prevalent diseases shift from those common to the most impoverished societies—namely infectious and nutritional diseases—to more chronic, degenerative conditions, such as cancer, atherosclerosis, and diabetes. The dramatic increase in CVD rates in developing regions also reflects substantial increases in life expectancy in many low-income societies, coupled with the greater vulnerability of middle-aged and elderly individuals to the development of CVD. With urbanization and industrialization, the population burden of vascular risk factors—hypertension, hypercholesterolaemia, diabetes, and obesity, especially—also increases. This results from the uptake of unhealthy dietary patterns which are aggressively marketed to them by the commercial food industry, and sedentary lifestyles due to the increased use of energy-saving devices (e.g. cars).
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.055 | 0.051 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".