Predicting Cardiovascular Disease Mortality in Men using Cardiorespiratory Fitness and other Risk Factor Categories
Bibliographic record
Abstract
0927 Although cardiorespiratory fitness (CRF) is an independent predictor of cardiovascular disease (CVD), the current algorithms and score sheets that are used to predict CVD in the clinical setting do not include CRF. PURPOSE: To develop a CVD mortality prediction model in men using age, blood pressure, cholesterol, diabetes, obesity, smoking status, parental CVD history, and CRF categories. METHODS: The sample included 19,221 male participants (20–83 year olds) from the Aerobics Center Longitudinal Study in Dallas, TX. All participants underwent a clinical examination including a maximal treadmill test from which CRF level (low, moderate, high) was calculated. Blood pressure, LDL- and HDL-cholesterol, diabetes, and overweight/obesity were categorized according to current clinical guidelines. A point scoring system was developed to predict CVD mortality from the beta-coefficients of Cox proportional hazards models. A total of 477 deaths occurred over an average of 10.2 years of follow-up, of which 160 were from CVD. RESULTS: After simultaneously adjusting for year of examination and each of the other variables in the model, older age, stage I and II hypertension, very high LDL-cholesterol, diabetes, current smoking, parental CVD history, severe obesity (BMI ≥35 kg/m2), and low CRF were significant predictors of CVD mortality. For the scoring system, the most points were assigned for higher age (e.g., 15 points for 60–69 year olds), followed by diabetes (5 points), low CRF (4 points), severe obesity (3 points), very high LDL-cholesterol (3 points), current smoking (3 points), parental CVD history (2 points), and stage I or II hypertension (2 points). CONCLUSION: CRF level was an independent predictor of CVD mortality, and contributed as much or more to the clinical CVD scoring system as traditional risk factors. Supported by grants AG06945 from the National Institute on Aging and T4945 from the Heart and Stroke Foundation of Ontario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".