Updated systematic review of the effects of exercise on understudied health outcomes in cancer survivors
Bibliographic record
Abstract
INTRODUCTION: The American College of Sports Medicine provided guidelines for exercise prescriptions in cancer survivors for specific cancer- and treatment-related health outcomes. However, there was insufficient evidence to generate exercise prescriptions for 10 health outcomes of cancer treatment. We sought to update the state of evidence. METHODS: We conducted a systematic review of these 10 understudied health outcomes (bone health, sleep, cardiovascular function, chemotherapy-induced peripheral neuropathy (CIPN), cognitive function, falls and balance, nausea, pain, sexual function, and treatment tolerance) and provided an update of evidence. RESULTS: While the evidence base for each outcome has increased, there remains insufficient evidence to generate exercise prescriptions. Common limitations observed across outcomes included: variability in type and quality of outcome measurement tools, variability in definitions of the health outcomes, a lack of phase III trials, and a majority of trials investigating breast or prostate cancer survivors only. CONCLUSION: We identified progress in the field of exercise oncology for several understudied cancer- and treatment-related health outcomes. However, we were not able to generate exercise prescriptions due to continued insufficient evidence base. More work is needed to prescribe exercise as medicine for these understudied health outcomes, and our review highlights several strategies to aid in research acceleration within these areas of exercise oncology.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".