Days at Home in the Last 6 Months of Life: A Patient-Determined Quality Indicator for Cancer Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Quality end-of-life care (EoLC) is a key aspect of oncology. Days at home in the last 6 months of life represents a novel, patient-driven quality indicator of EoLC. We measured days at home in a large population of patients with cancer in Ontario, Canada. Trends over time and predictors of more or less time at home were also determined. METHODS: We conducted a population-based retrospective study using health administrative data linked by unique, encoded identifiers and analyzed at the ICES. Quantile regression was used to determine significant predictors of more or less time at home. RESULTS: Of 72,987 patients who died of cancer in Ontario, Canada and met our inclusion criteria, the median number of days spent at home in the last 6 months of life was 164 (interquartile range [IQR], 144 to 175 days) of a possible 180 days. Patients with hematologic cancers spent significantly fewer days at home (156; IQR, 134 to 170 days). The strongest predictors of more time at home were male sex (+2.87 days relative to female sex; CI, 2.43 to 3.31 days) and receipt of palliative care before the last 6 months of life (+2.38 days; CI, 1.95 to 2.08 days). Additional predictors included income, age, cancer type, comorbidity burden, and health region. The majority of patients (69.7%) did not die at home. CONCLUSION: Days at home in the last 6 months of life, obtained from administrative data, can be used as a measure of quality EoLC. Predictors of days at home may prove valuable targets for future policy intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it