Describing settings of care in the last 100 days of life for cancer decedents: a population‐based descriptive study
Bibliographic record
Abstract
BACKGROUND: Few studies have described the settings cancer decedents spend their end-of-life stage, with none considering homecare specifically. We describe the different settings of care experienced in the last 100 days of life by individuals with cancer and how settings of care change as they approached death. METHODS: A retrospective cohort study from January 2013 to December 2017, of decedents whose primary cause of death was cancer, using linked population-level health administrative datasets in Ontario, Canada. RESULTS: Decedents 125,755 were included in our cohort. The average age at death was 73, 46% were female, and 14% resided in rural regions. And 24% died of lung cancer, 7% breast, 7% colorectal, 7% pancreatic, 5% prostate, and 50% other cancers. In the last 100 days of life, decedents spent 25.9 days in institutions, 25.8 days receiving care in the community, and 48.3 days at home without any care. Individuals who died of lung and pancreatic cancers spent the most days at home without any care (52.1 and 52.6 days), while individuals who died of prostate and breast cancer spent the least days at home without any care (41.6 and 45.1 days). Regardless of cancer type, decedents spent fewer days at home and more days in institutions as they approached death, despite established patient preferences for an end-of-life experience at home. CONCLUSIONS: In the last 100 days of life, cancer decedents spent most of their time in either institutions or at home without any care. Improving homecare services during the end-of-life may provide people dying of cancer with a preferred dying experience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".