Novel therapeutic concepts * The epidemic of cardiovascular disease in the developing world: global implications
Bibliographic record
Abstract
The epidemic of cardiovascular disease (CVD) is a global phenomenon, and the magnitude of its increase in incidence and prevalence in low- and middle-income countries (LIMIC) has potentially major implications for those high-income countries that characterize much of the developed world. Cardiovascular disease remains the leading cause of death in the world and approximately 80% of all cardiovascular-related deaths occur in LIMIC and at a younger age in comparison to high-income countries. The economic impact in regard to loss of productive years of life and the need to divert scarce resources to tertiary care is substantial. The 'epidemiologic transition' provides a useful framework for understanding changes in the patterns of disease as a result of societal and socioeconomic developments in different countries and regions of the world. A burning but as yet unanswered question is whether gains made over the last four decades in reducing cardiovascular mortality in high-income countries will be offset by changes in risk factor profiles, and in particular obesity and diabetes. Much of the population attributable risk of myocardial infarction is accountable on the basis of nine modifiable traditional risk factors, irrespective of geography. Developing societies are faced with a hostile cardiovascular environment, characterized by changes in diet, exercise, the effects of tobacco, socioeconomic stressors, and economic constraints at both the national and personal level in addition to exposure to potential novel risk factors and perhaps a genetic or programmed foetal vulnerability to CVD in later life. There are major challenges for primary and secondary prevention including lack of data, limited national resources, and the lack of prediction models in certain populations. There are two major approaches to prevention: public health/community-based strategies and clinic-based with a targeted approach to high-risk patients and combinations of these. There are concerns that in comparison with communicable diseases, cardiovascular and chronic diseases have a relatively low priority in the global health agenda and that this requires additional emphasis. The human race has had long experience and a fine tradition in surviving adversity, but we now face a task for which we have little experience, the task of surviving prosperity Alan Gregg 1890-1957, Rockefeller Foundation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".