A framework for prescription in exercise‐oncology research
Bibliographic record
Abstract
The field of exercise-oncology has increased dramatically over the past two decades, with close to 100 published studies investigating the efficacy of structured exercise training interventions in patients with cancer. Of interest, despite considerable differences in study population and primary study end point, the vast majority of studies have tested the efficacy of an exercise prescription that adhered to traditional guidelines consisting of either supervised or home-based endurance (aerobic) training or endurance training combined with resistance training, prescribed at a moderate intensity (50-75% of a predetermined physiological parameter, typically age-predicted heart rate maximum or reserve), for two to three sessions per week, for 10 to 60 min per exercise session, for 12 to 15 weeks. The use of generic exercise prescriptions may, however, be masking the full therapeutic potential of exercise treatment in the oncology setting. Against this background, this opinion paper provides an overview of the fundamental tenets of human exercise physiology known as the principles of training, with specific application of these principles in the design and conduct of clinical trials in exercise-oncology research. We contend that the application of these guidelines will ensure continued progress in the field while optimizing the safety and efficacy of exercise treatment following a cancer diagnosis.
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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 | 0.045 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.029 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".