Determinants of Home Death in Cancer Patients, a Population-Based Study in Ontario, Canada
Bibliographic record
Abstract
Background: In developed countries, the majority of cancer deaths occur in institutions, while most patients would prefer to die at home. The goal of this study is to assess the association between death at home and patients' neighbourhood income and rural-urban residence. Materials and methods: This is a retrospective cohort study of Ontario cancer decedents using linked administrative health data. Adults who died of cancer between 2003 and 2010 were included. A multivariable logistic regression model was used to evaluate factors associated with home death including age, sex, cancer type, region of patients' residence, neighbourhood income quintile for urban areas, rurality, comorbidity and year of death. Results: 193,783 deaths were analysed, 9.1% of which occurred at home. In urban areas, patients living in richer neighbourhoods were significantly more likely to die at home (OR 1.55, 95% CI 1.49-1.60, for highest neighbourhood income quintile compared to lowest). Odds of home death for patients residing in a rural area was not significantly different from residents of lowest income urban neighbourhoods (OR 1.02 CI 0.98-1.06). Other variables associated with lower odds of home death were: higher age, higher comorbidity index, living in certain regions, and hematologic cancers. Conclusion: The likelihood of dying at home for cancer patients significantly decreases with living in lower-income neighbourhoods or with rural residence. These findings underline the importance of targeting these populations for public support at the end of life. Open Access Abstract
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".