Associations between physician home visits for the dying and place of death: A population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: While most individuals wish to die at home, the reality is that most will die in hospital. AIM: To determine whether receiving a physician home visit near the end-of-life is associated with lower odds of death in a hospital. DESIGN: Observational retrospective cohort study, examining location of death and health care in the last year of life. SETTING/PARTICIPANTS: Population-level study of Ontarians, a Canadian province with over 13 million residents. All decedents from April 1, 2010 to March 31, 2013 (n = 264,754). RESULTS: More than half of 264,754 decedents died in hospital: 45.7% died in an acute care hospital and 7.7% in complex continuing care. After adjustment for multiple factors-including patient illness, home care services, and days of being at home-receiving at least one physician home visit from a non-palliative care physician was associated with a 47% decreased odds (odds-ratio, 0.53; 95%CI: 0.51-0.55) of dying in a hospital. When a palliative care physician specialist was involved, the overall odds declined by 59% (odds ratio, 0.41; 95%CI: 0.39-0.43). The same model, adjusting for physician home visits, showed that receiving palliative home care was associated with a similar reduction (odds ratio, 0.49; 95%CI: 0.47-0.51). CONCLUSION: Location of death is strongly associated with end-of-life health care in the home. Less than one-third of the population, however, received end-of-life home care or a physician visit in their last year of life, revealing large room for improvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".