COMPARISON OF A FRAILTY INDEX WITH CARDIOVASCULAR RISK SCORES IN PREDICTING CARDIOVASCULAR DISEASE MORTALITY
Bibliographic record
Abstract
Abstract We compared the predictive and discriminative ability of frailty with traditional cardiovascular risk scores to estimate 10-year cardiovascular disease (CVD) mortality risk. Individuals aged 20-79 years old from the National Health and Nutrition Examination Survey who were free from CVD were included (n= 32,066). A 33-item frailty index (FI) which excluded CVD and diabetes-related variables was calculated. We calculated the Framingham Disease Risk (FDR) Hard Coronary Heart Disease and General CVD risk scores, the American Heart Association/American College of Cardiology (AHA/ACC) atherosclerotic cardiovascular disease risk equation, and the European Systematic Coronary Risk Estimation tool. A total of 322 individuals died (1.0%) from CVD. There was a low correlation between the FI and CVD risk scores (spearman’s r= 0.19-0.33; p<0.0001) and a weak to strong correlation between CVD risk scores (spearman’s r=0.19-0.88; p<0.0001). The competing-risks hazard ratio for CVD mortality for every 1% increase in the FI was 1.040 (95% CI: 1.032-1.048; p<0.0001) in an age and sex-adjusted model. The FI was independently predictive of CVD mortality when the other CVD risk scores were added to the model. The area under the receiving operating characteristic (ROC) curve was 0.800 (95% CI: 0.789-0.808; p<0.0001) for the FI. ROC values for the CVD risk scores ranged from 0.710 (95% CI: 0.700-0.721; p<0.0001) for the AHA/ACC risk score to 0.779 (95% CI: 0.770-0.789; p<0.0001) for the FDR General CVD risk score. An FI calculated with non-CVD and diabetes variables can predict 10-year CVD mortality risk independently of traditional CVD risk scores.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".