End-of-Life Hospital Care for Cancer Patients: An Update
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada, and the number of new cases is expected to increase as the population ages and grows.This study examined the use of hospital services in the last month of life by adult cancer patients who died in Canadian acute care hospitals in fiscal year 2012-2013.Almost 25,000 Canadian cancer patients -excluding those in Quebec -died in acute care hospitals, representing approximately 45% of the estimated cancer deaths in 2012-2013.The proportion of in-hospital deaths varied across jurisdictions.Twenty-three percent of these patients were admitted to acute care multiple times in their last 28 days of life, with a higher percentage for rural (29%) compared to urban (21%) patients.Relatively few patients used intensive care units or received inpatient chemotherapy in their last 14 days of life.C ancer is the leading cause of death in Canada, and the number of new cases is expected to increase as the population ages and grows (Public Health Agency of Canada 2012).There were an estimated 75,500 cancer deaths in 2013 (Canadian Cancer Society's Advisory Committee on Cancer Statistics 2013).Available data show that a significant proportion of Canadian cancer patients die in acute care hospitals, which are primarily focused on shortterm, curative care.A better understanding of the experiences of cancer patients at the end of their lives is important in improving planning for their care.This article provides an update to the Canadian Institute for Health Information (CIHI)'s 2013 study End-of-Life Hospital Care for Cancer Patients.The original study examined the use
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".