Predicting the Risk of Cardiovascular Comorbidities in Adult Cancer Survivors
Bibliographic record
Abstract
OBJECTIVES: Data on how to identify cancer survivors (css) at the greatest risk for cardiovascular conditions are limited. We aimed to characterize the clinical factors associated with ischemic heart disease (ihd) and congestive heart failure (chf) in css and to develop a stratification schema for predicting the risk of cardiovascular comorbidities in css. METHODS: Cancer survivors and non-cancer controls (nccs) were identified from the U.S. National Health and Nutrition Examination Survey. Independent factors associated with increased relative risk (rr) for cardiovascular conditions were determined. A risk stratification schema was devised that correlated risk score with the prevalence of cardiovascular comorbidities in cs. RESULTS: Baseline characteristics were similar for the 1869 css and 24,337 nccs included in the study. Compared with nccs, css were more likely to report ihd (13.7% vs. 5.2%), chf (7.9% vs. 2.1%), or both (4.2% vs. 1.2%; all p < 0.01). Based on multivariate analyses, risk factors for cardiovascular problems included ages 40-60 years (rr: 3.66; 95% ci: 1.87 to 7.17), 60-80 years (rr: 14.18; 95% ci: 7.65 to 26.30), and 80 years or older (rr: 25.34; 95% ci: 13.16 to 48.78); male sex (rr: 2.25; 95% ci: 1.72 to 2.94); U.S. citizenship (rr: 2.10; 95% ci: 1.08 to 4.08); annual incomes of $20,000-$45,000 (rr: 1.81; 95% ci: 1.21 to 2.70) and less than $20,000 (rr: 3.05; 95% ci: 1.81 to 5.14); comorbid diabetes mellitus (rr: 2.97; 95% ci: 2.05 to 4.32); and physical inactivity (rr: 1.98; 95% ci: 1.41 to 2.79). CONCLUSIONS: Independent risk factors for ihd and chf in css were identified. The risk stratification schema presented here may be helpful in developing a risk-based approach to preventive cardiovascular strategies for css.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".