Cardiovascular Diseases in the Americas
Bibliographic record
Abstract
As elsewhere in the world, chronic non-communicable diseases (NCD) are rampant in the Americas. Population aging, smoking, unhealthy diet and physical inactivity, in the context of globalization and unregulated urbanization, explain the high prevalences of hypertension, hypercholesterolemia and diabetes in the region, making cardiovascular diseases (CVD) the main cause of death.[1,2] Recent studies in seven Latin American countries found prevalences in adults of 18 % for hypertension, 14 % for high cholesterol, 7 % for diabetes, 23 % for obesity and 30 % for smoking.[3] Given the region’s epidemiologic profi le, risk of a cardiovascular event in the next ten years is high in the adult population aged <70 years: up to 41 % in men and 18% in women in countries with very low infant and adult mortality (such as Canada, Cuba and the United States); up to 25 % and 17 % in men and women respectively, in countries with low infant and adult mortality (such as Argentina, Barbados and Chile) and up to 8 % and 6 % in men and women respectively, in countries with very high infant and adult mortality (such as Bolivia and Ecuador).[4] In the Americas in 2007, circulatory diseases (ICD-10, I00-I99) were responsible for approximately 30 % of deaths from all causes: 1,498,645 deaths. Four conditions were responsible for 87 % of these: ischemic heart disease (IHD) (ICD-10, I20-I25), cerebrovascular disease (ICD-10, I60-I69), cardiac insufficiency (ICD-10, I50) and hypertension (ICD-10,
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".