Singapore and coronary heart disease: a population laboratory to explore ethnic variations in the epidemiologic transition
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
See doi:10.1053/S1095-668X(02)00423-2for the article to which this editorial refers. The majority of cardiovascular disease (CVD) now occurs in developing countries. This trend will continue. Globally, CVD mortality is projected to double between 1990 and 2020, with the developing countries experiencing approximately 80% of the increase. In this context, recent experiencesin Singapore provide an interesting case study ofa developing country that has experienced rapid economic and social development.1 Since independence in 1965, the economy has grownapproximately 8% per year. Per capita GNP is now among the highest in the region, and the population enjoys a standard of living comparable to that experienced in many developed countries.2 Changes in disease patterns, that are consistent with those described by ‘the epidemiologic transition’,3 have been observed in Singapore over this time period. With economic development, the major causes of death and disability in more advanced societies, have shifted from a predominance of nutritional deficiencies and infectious diseases, to those classified as degenerative [e.g. chronic diseases such as CVD, cancer and diabetes). Few other countries have experienced the level of rapid economic development that Singapore has experienced in recent decades. At the midpoint of the century (1950–1960), Singapore was characterized by a very young population, with those below 20 years of age making up over 50% of the total population.2 By the end of the century, the proportion of …
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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