Measuring palliative and end-of-life care for cancer patients who die in hospital in Canada.
Bibliographic record
Abstract
58 Background: High acute hospital utilization rates near end-of-life can signal that community-based palliative care may not be suiting patients’ needs. Early integration of comprehensive palliative care can greatly reduce unplanned visits to the emergency department, reduce multiple admissions to hospital, shorten hospital stays, and increase the number of home deaths as well as improving the quality of life of advanced cancer patients. This analysis reports on indicators that describe the current landscape of acute-care hospital utilization at end-of-life and indirectly examines access to palliative care in patients who died of cancer in a hospital. Methods: Data were provided by the Canadian Institute for Health Information. The Discharge Abstract Database was used to extract acute-care cancer death abstracts. Data on ICU admissions include only facilities that report ICU data. Emergency department visit data were obtained from the National Ambulatory Care Reporting System. The analysis was restricted to adults aged 18+ who died in an acute-care hospital in fiscal years 2014/15 and 2015/16 for nine provinces and three territories. Results: A total of 48,987 (43%) cancer patient deaths occurred in an acute-care hospitals, with 70% admitted through the emergency department. Preliminary analysis revealed interprovincial variation in the cumulative length of stay in hospital 6 months prior to death from a median stay of 17 to 25 days. Some variation was also seen in the proportion of patients admitted to hospital two or more times in the last month of life (ranging from 18% to 33%), and the proportion of cancer patients admitted to ICU in the last 14 days of life (ranging from 15% to 6%). Patient demographics (age, sex, place of residence) and clinical factors (cancer type) were often predictors of hospital utilization at end-of-life. Conclusions: This study provides information on the current landscape of acute-care hospital utilization by cancer patients at end-of-life across Canada and identifies interprovincial variations in management of end-of-life care. An area of focus for the Palliative and End-of-Life National Network continue to be developing nationally agreed upon system-wide palliative care indicators.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| 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".