Rehabilitation and Exercise Oncology Program: Translating Research into a Model of Care
Bibliographic record
Abstract
INTRODUCTION: The Rehabilitation and Exercise Oncology model of care (ActivOnco) was established to optimize cancer survivorship through exercise prescription and active lifestyle promotion, providing a transition of care from hospital to community. Patients having any cancer diagnosis, stage of disease, and treatment were eligible for evaluation and exercise prescription upon deterioration of performance status. The team of professionals included hospital-based physiotherapists proactively screening for rehabilitation needs, loss of functional independence, and exercise eligibility, plus exercise specialists in a community-based Wellness Centre to provide follow-up or direct access for post-treatment or non-complex patients. METHODS: From January 2011 to December 2015, the hospital team assessed 1635 patients representing all major cancer sites, and the Wellness Centre team evaluated and prescribed exercise for 1066 participants. Primary interventions provided were education about fatigue management, physical activity promotion, exercise prescription, fracture risk reduction, referral to specialized follow-up services (for example, occupational therapy, lymphedema clinic), and coordination for mobility aids and paratransit services. RESULTS AND CONCLUSIONS: Implementation of the ActivOnco model of care showed that exercise alone is not a panacea for all functional deterioration associated with the cancer trajectory and its treatment. However, screening to identify rehabilitation needs combined with exercise prescription can effectively improve the quality of survivorship in cancer patients. Program developments are limited by the cost of human resources, lack of hospital-based physical resources, and lack of public funding, all of which significantly limit the scope and development of appropriate services.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".