Variations in Emergency Service Utilization among Cancer Survivors: Results from the Pan-Canadian Experiences of Cancer Patients in Transition Study Survey
Bibliographic record
Abstract
Purpose: The objective of this study was to examine variations in emergency service utilization (ESU) among cancer survivors during the first year after completing primary cancer treatment. Methods: In 2016, the Canadian Partnership Against Cancer collected survey responses from cancer survivors across Canada about self-reported ESU after completing primary cancer treatment. We included survey respondents diagnosed with nonmetastatic breast, hematologic, colorectal, melanoma, or prostate cancer. Multivariable, multinomial logistic regression analysis was used to examine factors associated with cancer survivors' ESU. Results: Of the 5,774 cancer survivors included in our analysis, 22% reported ESU during the first year after completing their primary cancer treatment, 16% reported ESU one to three times, and 6% reported ESU more than three times. Factors significantly associated with frequent ESU included younger age, colorectal and hematologic cancers, more frequent primary care provider and oncology specialist visits, single or retired status, lower income, and self-reported lower quality of life. Conclusion: Our study identified factors associated with more frequent ESU among cancer survivors in the first year after completing primary cancer treatment. These factors highlight differences in cancer survivors' demographics, their ability to access and need for healthcare services, and the complexity of using ESU as a metric for quality improvement in survivorship care. These variations must be considered in quality improvement initiatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".