Association between cardiovascular risk and coronary artery disease in Masters athletes
Bibliographic record
Abstract
Background: Studies have shown that coronary artery disease (CAD) is more prevalent in those with a lifelong exercise history compared to the general population and that cardiovascular risk scores (i.e., Framingham Risk Score (FRS)) underestimate the presence of CAD in Masters athletes (> 35 years old). Purpose: To establish whether physical activity intensity and volume in a highly active population contributes to the risk of CAD. Methods: Masters athletes (n=799) underwent yearly cardiovascular screening for five years, including, anthropometrics, blood pressure, blood lipids (to determine FRS), and a health survey. Participants with an abnormal screen underwent further evaluations. All variables of interest (age, sex, FRS, body mass index, LDL cholesterol, HDL cholesterol, family history, physical activity volume, lifetime training hours, history of hypertension) were aggregated up to the first diagnosis for those that were diagnosed and aggregated over the entire time period for those who were not diagnosed. Logistic regression analysis assessed the relationship between cardiovascular risk factors and CAD. Results: 81 (10%) Masters athletes were diagnosed with CAD over the study period. Increasing age (OR=1.05, 95%CI 1.00-1.09; p=0.038), FRS (%) (OR=1.09, 95%CI 1.03-1.16; p=0.003), and LDL cholesterol (mmol/L) (OR=1.71, 95% CI 1.22-2.40; p=0.002) were statistically significant in predicting the presence of CAD, whereas physical activity intensity and volume were not. Conclusions: These results support the utility of the cardiovascular risk score (FRS) in predicting CAD in Masters athletes, whereas physical activity/exercise participation does not. Funding: Canadian Institutes of Health Research (FRN: 157930), Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-04613), and MITACs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".