Translating<scp>2019 ACSM</scp>Cancer Exercise Recommendations for a Physiatric Practice: Derived Recommendations from an International Expert Panel
Bibliographic record
Abstract
In 2018, the American College of Sports Medicine (ACSM) reconvened an international, multi-disciplinary group of professionals to review pertinent published literature on exercise for people with cancer. The 2018 roundtable resulted in the publication of three articles in 2019. The three articles serve as an important update to the original ACSM Roundtable on Cancer, which convened in 2010. Although the focus of the three 2019 articles is on exercise, which is only one part of comprehensive cancer rehabilitation, the evidence presented in the 2019 ACSM articles has direct implications for physiatrists and other rehabilitation professionals who care for people with cancer. As such, the narrative review presented here has two primary objectives. First, we summarize the evidence within the three ACSM articles and interpret it within a familiar rehabilitation framework, namely the Dietz model of Cancer Rehabilitation, in order to facilitate implementation broadly within rehabilitation practice. Second, via expert consensus, we have tabulated relevant exercise recommendations for specific cancer populations at different points in the cancer care continuum and translated them into text, tables, and figures for ease of reference. Notably, the authors of this article are members of the Cancer Rehabilitation Physician Consortium (CRPC), a group of physicians who subspecialize in cancer rehabilitation medicine (CRM).
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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 | 0.041 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".