Quality of End-of-Life Cancer Care in Canada: A Retrospective Four-Province Study Using Administrative Health Care Data
Bibliographic record
Abstract
BACKGROUND: The quality of data comparing care at the end of life (eol) in cancer patients across Canada is poor. This project used identical cohorts and definitions to evaluate quality indicators for eol care in British Columbia, Alberta, Ontario, and Nova Scotia. METHODS: This retrospective cohort study of cancer decedents during fiscal years 2004-2009 used administrative health care data to examine health service quality indicators commonly used and previously identified as important to quality eol care: emergency department use, hospitalizations, intensive care unit admissions, chemotherapy, physician house calls, and home care visits near the eol, as well as death in hospital. Crude and standardized rates were calculated. In each province, two separate multivariable logistic regression models examined factors associated with receiving aggressive or supportive care. RESULTS: Overall, among the identified 200,285 cancer patients who died of their disease, 54% died in a hospital, with British Columbia having the lowest standardized rate of such deaths (50.2%). Emergency department use at eol ranged from 30.7% in Nova Scotia to 47.9% in Ontario. Of all patients, 8.7% received aggressive care (similar across all provinces), and 46.3% received supportive care (range: 41.2% in Nova Scotia to 61.8% in British Columbia). Lower neighbourhood income was consistently associated with a decreased likelihood of supportive care receipt. INTERPRETATION: We successfully used administrative health care data from four Canadian provinces to create identical cohorts with commonly defined indicators. This work is an important step toward maturing the field of eol care in Canada. Future work in this arena would be facilitated by national-level data-sharing arrangements.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".