Evidence-based risk assessment and recommendations for physical activity clearance: cancer<sup>1</sup>This paper is one of a selection of papers published in this Special Issue, entitled Evidence-based risk assessment and recommendations for physical activity clearance, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Physical activity is becoming increasingly acknowledged as an integral component of in the multidisciplinary management of cancer patients. Intensive inquiry in this area is likely to increase further over the next decade; however, cancer-specific, evidence-based risk assessment and recommendations for physical activity are not available. A systematic literature review was performed of all studies conducting an exercise training intervention and (or) any form of objective exercise test among adults diagnosed with cancer. Studies were assessed according to evaluation criteria developed by a panel of experts. A total of 118 studies involving 5529 patients were deemed eligible. Overall, the results suggest that exercise training and maximal and submaximal exercise testing are relatively safe procedures with a total nonlife-threatening adverse event rate of <2%. There was only 1 exercise training-related death. However, the quality of exercise testing methodology and data reporting is less than optimal. Thus, whether the low incidence of events reflects the true safety of exercise training and exercise testing in cancer patients or less than optimal methodology and (or) data reporting remains to be determined. Evidence-based absolute and relative contraindications to physical activity and exercise training and testing are provided as well as probing decision-trees to optimize the adoption and safety of physical activity in persons diagnosed with cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.325 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".