The Cost-Effectiveness of Exercise Training for the Primary and Secondary Prevention of Cardiovascular Disease
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Although exercise training improves cardiovascular disease (CVD) risk factors, few studies have evaluated its potential long-term cost-effectiveness. METHODS: Using the Cardiovascular Disease Life Expectancy Model, a validated disease simulation model, we calculated the life expectancy of average 35- to 74-year-old Canadians found in the 1992 Canadian Heart Health Survey. The impacts of exercise training on cardiovascular risk factors were estimated as a 4% decrease in low-density lipoprotein (LDL) cholesterol, a 5% increase in high-density lipoprotein (HDL) cholesterol, and a 6 mm Hg decrease in both systolic and diastolic blood pressure. Exercise adherence was estimated at 50% for the first year and 30% for all additional years. Costs for a supervised exercise program determined from Canadian sources and converted to US dollars were estimated at $605 for the first year (medical evaluation, stress test, exercise prescription, and program costs) and $367 for all additional years (program costs). For an unsupervised program, the costs were estimated at $311 for the first year and $73 for all additional years. RESULTS: The cost-effectiveness (CE) of an unsupervised exercise program (1996 U.S. dollars) was less than $12,000 per year of life saved (YOLS) for all individuals. The CE of a supervised exercise program was less than $15,000/YOLS for men with CVD, and between $12,000 and $43,000 for women with CVD and men without CVD. CONCLUSIONS: Given the relatively few risks, substantial long-term benefits, and modest costs, an unsupervised exercise training program represents good value for all. A more expensive supervised exercise program is also cost-effective for most individuals with CVD.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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