Exercise as cancer treatment: A clinical oncology framework for exercise oncology research
Bibliographic record
Abstract
Exercise has been proposed as a possible cancer treatment; however, there are an infinite number of clinical oncology settings involving diverse cancer types and treatment protocols in which exercise could be tested as a cancer treatment. The primary purpose of this paper is to propose a conceptual framework to organize and guide research on exercise as a cancer treatment across distinct clinical oncology settings. A secondary purpose is to provide an overview of existing exercise research using the proposed framework. The Exercise as Cancer Treatment (EXACT) framework proposes nine distinct clinical oncology scenarios based on tumor/disease status and treatment status at the time of the proposed exercise treatment. In terms of tumor/disease status, the primary tumor has either been surgically removed (primary goal to treat micrometastases), not surgically removed (primary goal to treat the primary tumor), or metastatic disease is present (primary goal to treat metastatic disease). In terms of treatment status, the extant disease has either not been treated yet (treatment naïve), is currently being treated (active treatment), or has previously been treated. These two key clinical oncology variables-tumor/disease status and treatment status-result in nine distinct clinical oncology scenarios in which exercise could be tested as a new cancer treatment: (a) treatment naïve micrometastases, (b) actively treated micrometastases, (c) previously treated micrometastases, (d) treatment naïve primary tumors, (e) actively treated primary tumors, (f) previously treated primary tumors, (g) treatment naïve metastatic disease, (h) actively treated metastatic disease, and (i) previously treated metastatic disease. To date, most preclinical animal studies have examined the effects of exercise on treatment naïve and actively treated primary tumors. Conversely, most observational human studies have examined the associations between exercise and cancer recurrence/survival in patients actively treated or previously treated for micrometastases. Few clinical trials have been conducted in any of these scenarios. For exercise to be integrated into clinical oncology practice as a cancer treatment, it will need to demonstrate benefit in a specific clinical setting. The EXACT framework provides a simple taxonomy for systematically evaluating exercise as a potential cancer treatment across a diverse range of cancer types and treatment protocols.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".