Taking Advantage of the Teachable Moment at Initial Diagnosis of Prostate Cancer—Results of a Pilot Randomized Controlled Trial of Supervised Exercise Training
Bibliographic record
Abstract
BACKGROUND: Increased physical activity (PA) levels are associated with improved prostate cancer (PCa) outcomes. Sustainable PA has been linked to improved health-related quality of life (QoL) in cancer patients. The time of diagnosis of PCa may offer a critical time point when patients might be more likely to consider lifestyle changes. This, in turn, may contribute to sustainable PA and its likely benefits. OBJECTIVE: The aims of this study were to determine if a structured PA intervention introduced at the time of diagnosis can (1) lead to sustainable PA and (2) help improve psychosocial and QoL outcomes as compared with usual PA. INTERVENTIONS/METHODS: This was a pilot randomized controlled trial enrolling patients with intermediate-risk PCa into either arm A (supervised 8- to 12-week physical exercise program; n = 10) or control arm B (usual PA; n = 10). Primary outcome was PA at 6 months. Secondary outcomes were QoL, psychological well-being, physical fitness, and functional outcomes postintervention. Change over time was compared using a nonparametric Wilcoxon test. RESULTS: Demographic variables were the same between arms. Comparing parameters at the start and 6 months post-radical prostatectomy, PA significantly improved in arm A (self-reported Godin score 24.7 vs 42.8 units, P < .01, objective number of chair stands [14-19, P < .01]), but not in arm B. There were no significant differences between arms in QoL and psychosocial outcomes. CONCLUSIONS: A preoperative supervised exercise training program increases long-term PA. IMPLICATIONS FOR PRACTICE: Future trials should evaluate PA sustainability beyond 6 months and if this leads to improved psychosocial and QoL outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".