ACE-Neuro: A Tailored Exercise Oncology Program for Neuro-Oncology Patients – Study Protocol
Bibliographic record
Abstract
Abstract Background Patients with primary brain tumours lack access to exercise oncology and wellness resources. The purpose of the Alberta Cancer Exercise – Neuro-Oncology (ACE-Neuro) study is to assess the feasibility of a tailored neuro-oncology exercise program for patients across Alberta, Canada. The primary outcome is to assess the feasibility of ACE-Neuro. The secondary outcome is to examine preliminary effectiveness of ACE-Neuro on patient-reported outcomes and functional fitness. Methods Neuro-oncology patients with a malignant or benign primary brain tumour that are pre, on, or completed treatment, are >18 years, and able to consent in English are eligible to participate in the study. Following referral from the clinical team to cancer rehabilitation and the study team, participants are triaged to determine their appropriateness for ACE-Neuro or other cancer rehabilitation or physiatry resources. In ACE-Neuro, participants complete a tailored 12-week exercise program with pre-post assessments of patient-reported outcomes, functional fitness, and physical activity. ACE-Neuro includes individual and group-based exercise sessions, as well as health coaching. Conclusion We are supporting ACE-Neuro implementation into clinical cancer care, with assessment of needs enabling a tailored exercise prescription.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".