Age-Dependent Increased Odds of Cardiovascular Risk Factors in Cancer Survivors: Canadian Longitudinal Study on Aging Cohort
Bibliographic record
Abstract
Background: This study compared the odds of self-reported and objectively measured cardiovascular (cv) risk factors in a sample of Canadian cancer survivors and individuals without cancer. Methods: = 44,051). Results: The most prevalent risk factors in cancer survivors were all self-reported or easily measured in clinic: overweight or obesity (68.0%), former smoking (62.9%), fewer than 5 daily servings of fruits and vegetables (59.8%), hypertension (43.7%), and high waist circumference (47.0%). After adjustment for sex and education, the odds ratios of several cv risk factors varied by age in cancer survivors and the non-cancer controls. At ages 50 and 60, cancer survivors have increased odds of overweight or obesity, former smoking, hypertension, high waist circumference and truncal fat, diabetes, lung disease, and heart rate greater than 80 bpm compared with non-cancer controls. At age 70, odds did not differ for many risk factors; at age 80, no differences were evident. Without modification by age, low physical activity was more prevalent in cancer survivors (odds ratio: 1.27; 95% confidence interval: 1.17 to 1.39). There were no differences in the odds of cv risk factors measured by specialized equipment, including electrocardiography, carotid ultrasonography, spirometry, and dual-energy X-ray absorptiometry. Conclusions: The odds of several easy-to-assess cv disease risk factors are higher among middle-aged, but not older, cancer survivors relative to the general Canadian population. Initial assessment of cv risk for middle-aged adults in the survivorship setting could be quickly and inexpensively performed using self-reported and easily measured metrics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".