Feasibility and Implementation of an Oncology Rehabilitation Triage Clinic: Assessing Rehabilitation, Exercise Need, and Triage Pathways within the Alberta Cancer Exercise–Neuro-Oncology Study
Bibliographic record
Abstract
Individuals living with and beyond cancer face physical impairment and inactivity in survivorship. Neuro-oncology populations have especially high rates of sedentary behaviour and functional deficits, including impaired balance, motor skills, and cognition. Our purpose was to assess the rehabilitation and exercise needs of patients with brain tumours while examining the feasibility of a rehabilitation triage clinic as a part of the Alberta Cancer Exercise-Neuro-Oncology study, where patients were referred to a triage clinic, where health, neurologic, and functional status was assessed, followed by a referral to one or multiple resources, including exercise, physiotherapy, occupational therapy, or physiatry. Qualitative perception of the triage clinic was collected. Overall, the triage clinic was feasible and safe for participants, facilitating referral into rehabilitation and exercise resources. Pre-determined enrollment and attendance rates were met, but referral rates to the triage clinic were not met. Oncology clinic staff reported forgetting to refer patients or uncertainty of who was appropriate for rehabilitation as barriers. Oncology clinic-based screening may improve the identification of patients who are sedentary or have a physical impairment. A proposed screening tool, the Cancer Rehabilitation and Exercise Screening Tool (CREST), is presented within our Cancer Rehabilitation and Exercise Pathways Model. The CREST can identify patients who are sedentary or have a functional impairment, facilitating referral to appropriate rehabilitation resources and ultimately improving patient recovery and functioning.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".