An integrated framework for the study of exercise across the postdiagnosis cancer continuum
Bibliographic record
Abstract
Exercise plays many important roles across the entire cancer continuum that have been described in previous frameworks. These frameworks, however, have generally provided a simplified description of the roles of exercise postdiagnosis. The modern cancer treatment landscape has become complex and often consists of multiple lines of multimodal treatments combined concurrently and/or sequentially and delivered over many months or years. This complexity requires a more multifaceted and targeted approach to the study of exercise after a cancer diagnosis. Here, we propose a new integrated framework-Exercise Across the Postdiagnosis Cancer Continuum (EPiCC)-that highlights the distinct roles of exercise for disease treatment and supportive care from diagnosis until death. We also propose new terminology to clarify the distinct roles of exercise that emerge in the context of the modern cancer treatment landscape. The EPiCC Framework is structured around multiple sequential cancer treatments that highlight six distinct cancer treatment-related time periods for exercise-before treatments, during treatments, between treatments, immediately after successful treatments, during longer term survivorship after successful treatments, and during end of life after unsuccessful treatments. The EPiCC Framework proposes that the specific roles of exercise as a disease treatment and supportive care intervention will vary depending on its positioning within different cancer treatment combinations. As a cancer treatment, exercise may serve as a "priming therapy", primary therapy, neoadjuvant therapy, induction therapy, "bridging therapy", adjuvant therapy, consolidation therapy, maintenance therapy, and/or salvage therapy. As a supportive care intervention, exercise may serve as prehabilitation, intrahabilitation, interhabilitation, rehabilitation, "perihabilitation", health promotion/disease prevention, and/or palliation. To date, exercise has been studied during all of the cancer treatment-related time periods but only in relation to some cancer treatments and combinations. Moreover, fewer studies have examined exercise across multiple cancer treatment-related time periods within any cancer treatment combination. Future research is needed to study exercise as a disease treatment and supportive care intervention within and across the distinct cancer treatment-related time periods contained within different cancer treatment combinations. The aim of the EPiCC Framework is to stimulate a more targeted, integrated, and clinically-informed approach to the study of exercise after 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".