Considerations for delivery of live-remote exercise for people with cancer in research and practice
Bibliographic record
Abstract
Exercise is safe and beneficial for people diagnosed with cancer. The use of live-remote exercise approaches, where exercise trainers deliver exercise programs via a videoconferencing platform, has increased rapidly, greatly expanding the reach of exercise programs. This method retains key elements of supervised exercise, which provide greater benefits than unsupervised programs. However, challenges in adapting in-person supervised exercise programs to remote delivery exist. This article discusses the key considerations for the effective and safe delivery of live-remote exercise, such as technological requirements, exercise professional skills, safety aspects, exercise programming features, social interactions, costs, and legal and ethical considerations. Considerations relevant for the design and execution of exercise oncology clinical trials and for community practice are described. Remaining knowledge gaps are outlined and point to opportunities to further inform evidence-based practice and practice-based evidence.
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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 | 0.158 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".