Cardiovascular Health Metrics Differ Between Individuals With and Without Cancer
Bibliographic record
Abstract
Background Although individuals with cancer experience high rates of cardiovascular morbidity, there are limited data on the potential differences in cardiovascular health (CVH) metrics between individuals with and without cancer. Methods and Results The National Health and Nutrition Examination Survey between 2015 and 2020 was queried to evaluate the prevalence of health metrics that comprise the American Heart Association Life's Essential 8 construct of cardiovascular health among adult individuals with and without cancer in the United States. Health metric scores were also evaluated according to important patient demographics including age, sex, race and ethnicity, and socioeconomic status. Among 4370 participants representing >180 million US adults, 9.4% had a history of cancer. Individuals with cancer had lower overall cardiovascular health scores (67.1 versus 69.1, P <0.001) compared with individuals without cancer. Among individual components of the cardiovascular health score, those with cancer had better health scores on key behaviors including physical activity, diet, and sleep compared with those without cancer, although variation was noted based on age. Higher scores on these modifiable health behaviors among those with cancer compared with those without cancer were noted in older individuals, in White individuals compared with other races and ethnicities, and in individuals with higher socioeconomic status. Conclusions We highlight important variations in simple cardiovascular health metrics among individuals with cancer compared with individuals without cancer and demonstrate differences among health metrics based on age, race and ethnicity, and socioeconomic status. These findings may explain ongoing racial, ethnic, and socioeconomic status disparities in the cancer population and provide a framework for optimizing cardiovascular health among individuals with cancer.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".