12 The potential role of exercise in the supportive care of neurological cancer survivors: delivering effective and appropriate programming through the Alberta cancer exercise (ACE) study
Bibliographic record
Abstract
BACKGROUND: Exercise has been shown to benefit health-related fitness, psychosocial health, and disease outcomes in cancer survivors. PURPOSE: To review the evidence on exercise for individuals diagnosed with Neurological Cancer (NC); present data on NC participants in the ACE pilot and ongoing implementation study; and propose a framework to incorporate exercise into the care of NC survivors in Alberta. METHODS: The ACE program is open to survivors with any cancer diagnosis at any stage of treatment. Exercise programming consists of two training sessions per week, with the pilot and implementation studies being 8 and 12 weeks in duration respectively. Outcomes are assessed at study baseline, post-exercise intervention, and 24-week follow-up, and include recruitment and follow-up rates, health-related fitness, psychosocial outcomes, and cancer symptoms. RESULTS: NC survivors represented 7 of 80 participants in the ACE pilot; however, only 3 of the 7 (43%) completed the study. Findings suggested a need for consideration of supervised exercise for some survivors with NC. To date, 14 NC survivors have enrolled in the ACE implementation study. Participants are screened and then referred to either supervised clinic-based or community-based exercise. Seven of 9 participants have completed the ACE intervention, and 5 of 5 have completed the 24-week follow-up. NC participants improved or maintained health-related physical fitness, and reported reduced symptom burden and fatigue. CONCLUSION: Preliminary results suggest exercise training is feasible and beneficial for NC survivors. To optimize recruitment and outcomes, efforts are needed to better identify, screen, and refer survivors to appropriate exercise programming.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".