Determinants of Home Death in Patients With Cancer
Bibliographic record
Abstract
AIM: To determine factors associated with home death in patients with cancer in Ontario, particularly to assess the association between death at home and (1) patients' rural/urban residence and (2) neighborhood income in urban areas. MATERIALS AND METHODS: We conducted a retrospective cross-sectional study in Ontario (2003-2010) using linked administrative databases. In order to account for clustering phenomenon, multivariable generalized estimating equation model was used to evaluate factors associated with home death. Analysis was performed in both rural and urban areas. For urban areas, neighborhood income was tested as a determinant of the place of death. RESULTS: A total of 193 783 deaths were analyzed, 9.1% of which occurred at home. In urban areas, home death was more likely for patients living in richer neighborhoods (odds ratio 1.69 for the highest compared to lowest neighborhood income quintile, 95% confidence interval: 1.54-1.86). The odds of dying at home when living in a rural area were no different from those living in the poorest urban neighborhood. Other variables associated with lower odds of home death were comorbidity index, certain cancers, and year of death. CONCLUSION: The likelihood of dying at home significantly increases with living in higher-income urban neighborhoods and decreases with rural residence. Urban neighborhoods with lowest income have odds of home death similar to rural areas. These findings underline the importance of targeting proper populations for public support at the end of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".