Top 10 Research Questions Related to Physical Activity and Cancer Survivorship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it