Top 10 Research Questions Related to Physical Activity and Cancer Survivorship
Bibliographic record
Abstract
In the United States, there are more than 14 million cancer survivors. Many of these survivors have been treated with multimodal therapy including surgery, radiation therapy, chemotherapy, and targeted therapies. These therapies improve survival; however, they also cause acute and chronic side effects that can undermine health and quality of life. Physical activity (PA) and cancer survivorship is a rapidly growing field of inquiry that studies the role of PA in people diagnosed with cancer. In this article, we propose the following top 10 research questions for the field of PA and cancer survivorship: (1) Does PA reduce the risk for cancer recurrence and/or improve survival? (2) Does PA influence cancer treatment decisions, completion rates, and/or response? (3) What is the optimal PA prescription for cancer survivors? (4) What is the role of sedentary behavior in cancer survivorship? (5) What are the most effective PA behavior change interventions for cancer survivors? (6) Which cancer variables modify the PA response? (7) What are the safety issues concerning PA in cancer survivors? (8) Which specific cancer symptoms can be managed by PA? (9) Is there a role for PA in advanced cancer? And (10) How do we translate PA research into clinical and community oncology practice? The answers to these questions are critical not only for advancing the field of PA and cancer survivorship, but for improving the lives of the millions of cancer survivors every year who are diagnosed with cancer, going through treatments, recovering after treatments, or coping with advanced disease.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.036 |
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".