Trends in the Aggressiveness of End-of-Life Cancer Care in the Universal Health Care System of Ontario, Canada
Bibliographic record
Abstract
PURPOSE: To describe trends in the aggressiveness of end-of-life (EOL) cancer care in a universal health care system in Ontario, Canada, between 1993 and 2004, and to compare with findings reported in the United States. METHODS: A population-based, retrospective, cohort study that used administrative data linked to registry data. Aggressiveness of EOL care was defined as the occurrence of at least one of the following indicators: last dose of chemotherapy received within 14 days of death; more than one emergency department (ED) visit within 30 days of death; more than one hospitalization within 30 days of death; or at least one intensive care unit (ICU) admission within 30 days of death. RESULTS: Among 227,161 patients, 22.4% experienced at least one incident of potentially aggressive EOL cancer care. Multivariable analyses showed that with each successive year, patients were significantly more likely to encounter some aggressive intervention (odds ratio, 1.01; 95% CI, 1.01 to 1.02). Multiple emergency department (ED) visits, ICU admissions, and chemotherapy use increased significantly over time, whereas multiple hospital admissions declined (P < .05). Patients were more likely to receive aggressive EOL care if they were men, were younger, lived in rural regions, had a higher level of comorbidity, or had breast, lung, or hematologic malignancies. Chemotherapy and ICU utilization were lower in Ontario than in the United States. CONCLUSION: Aggressiveness of cancer care near the EOL is increasing over time in Ontario, Canada, although overall rates were lower than in the United States. Health system characteristics and patient or physician cultural factors may play a role in the observed differences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".