Can Exercise Adaptations Be Maintained in Men with Prostate Cancer Following Supervised Programmes? Implications to the COVID-19 Landscape of Urology and Clinical Exercise
Bibliographic record
Abstract
In this brief correspondence, we evaluate the potential impact of pivoting from face-to-face supervised to unsupervised home-based exercise programmes to contextualise the coronavirus disease 2019 (COVID-19) pandemic in prostate cancer patients. A meta-analysis was undertaken in fatigue, quality of life, and lean and fat mass outcomes in the four studies included. Our analysis indicates that unsupervised home-based exercise maintains patient-reported outcomes, except for fat mass. In summary, changing to unsupervised exercise is unlikely to provide further benefits on patient-reported and body composition outcomes, but may help maintain initial gains during physical distancing restrictions. PATIENT SUMMARY: We discuss the potential impacts of transitioning from face-to-face supervised to unsupervised home-based exercise programmes in prostate cancer patients during the coronavirus disease 2019 (COVID-19) pandemic. Our analysis suggests that patients are likely to maintain patient-reported and body composition benefits from current nonsupervised programmes; however, evolution of exercise delivery to prostate cancer patients is required to continue health and fitness improvement in this group.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".