Bibliographic record
Abstract
Cancer is a common disease that affects over 150 000 Canadians every year. About 78% of all cancers are diagnosed in adults aged 60 years and older. Improved survival rates for cancer survivors have brought lifestyle and quality of life issues to the forefront. In other chronic disease populations, exercise is considered a foundational health behavior; however, the benefits of exercise in cancer survivors are only beginning to be described. Moreover, what little is known about exercise in cancer survivors has been derived largely from research on middle-aged survivors. In the present article, we review the literature on exercise, aging, and cancer. Our review shows that very few studies have examined exercise in older cancer survivors or have approached the topic from an aging perspective. The limited research that is available suggests that, compared with middle-aged cancer survivors, older cancer survivors: (i) derive similar benefits from exercise, (ii) have lower exercise participation rates, (iii) have more difficulty adhering to an exercise program, and (iv) have different determinants of exercise motivation and behavior. We end by offering some future research directions that may help generate important new exercise knowledge in this underserved cancer survivor population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".