A Case Control Study Examining the Patterns and Predictors of Referral to Cancer Rehabilitation at Canada's Largest Comprehensive Cancer Centre
Bibliographic record
Abstract
BACKGROUND: Cancer rehabilitation has become increasingly relevant as the number of cancer survivors grows, coupled with the high-documented rates of adverse effects and related disability. Cancer rehabilitation can reduce functional limitations among cancer survivors and enhance their well-being. However, only a small proportion of individuals are referred to rehabilitation services. To identify and address disparities and foster access, it is essential to develop a better understanding of the factors that drive referral to cancer rehabilitation services. METHODS: The purpose of this study was to: (1) describe the sociodemographic and clinical characteristics and symptom burden of patients who were referred to the Princess Margaret Cancer Rehabilitation and Survivorship (CRS) Program between 2017 and 2019 and (2) Compare these variables between patients who were referred to CRS (n = 2783) and matched cases who were not referred over this period (n = 18,434). A retrospective secondary analysis of data extracted from the Princess Margaret (PM) Cancer Registry, electronic patient records, and patient-reported outcome data (PROMs) (including ESAS-r and ECOG status) was performed. Summary statistics were used to describe the patients referred to the CRS program. Multivariable logistic regression modelling was used to identify factors associated with likelihood of referral. RESULTS: Most referred patients were female (74%), English speakers (93%) and half lived within 15 km of the referred hospital. The most common reasons for referral were musculoskeletal impairment (26%) and lymphedema (25.4%). Many patients (45%) had multiple reasons for referral. Several key predictors of referral were identified including closer distance to hospital, lower age (< 65 years), cancer site, and completion of PROMs. For those who completed PROMs, patient reported function status and pain scores were related to referral. CONCLUSION: The findings can be helpful in optimizing the referral processes and addressing disparities regarding access to cancer rehabilitation. Solutions are likely multifaceted including health care provider and patient education and systemic changes to address barriers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".