Accumulation of Non-Traditional Risk Factors for Coronary Heart Disease Is Associated with Incident Coronary Heart Disease Hospitalization and Death
Bibliographic record
Abstract
BACKGROUND: Assessing multiple traditional risk factors improves prediction for late-life diseases, including coronary heart disease (CHD). It appears that non-traditional risk factors can also predict risk. The objective was to investigate contributions of non-traditional risk factors to coronary heart disease risk using a deficit accumulation approach. METHODS: Community-dwelling adults with no known history of CHD (n = 2195, mean age 46.9±18.7 years, 51.8% women) participated in the 1995 Nova Scotia Health Survey. Three risk factor indices were constructed to quantify the proportion of deficits present in individuals: 1) a 17-item Non-Traditional Risk Factor Index (e.g. sinusitis, arthritis); 2) a 9-item Traditional Risk Factor Index (e.g. hypertension, diabetes); and 3) a frailty index (25 items combined from the other two index measures). Ten-year risks of CHD events (defined as CHD-related hospitalization and CHD-related mortality) were evaluated. RESULTS: The Non-Traditional Risk Factor Index, made up of health deficits unrelated to CHD, was independently associated with incident CHD events over 10 years after controlling for age, sex, and the Traditional Risk Factor Index [adjusted {adj.} Hazard Ratio {HR} = 1.31; Confidence Interval {CI} 1.14-1.51]. When all health deficits, both those related and unrelated to CHD, were included in a frailty index the corresponding adjusted hazard ratio was 1.61; CI 1.40-1.85. CONCLUSION: Both traditional and non-traditional risk factor indices are independently associated with incident CHD events. CHD risk assessment may benefit from consideration of general health information as well as from traditional risk factors.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".