Quality of End-of-Life Care in Gastrointestinal Cancers: A 13-Year Population-Based Retrospective Analysis in Ontario, Canada
Bibliographic record
Abstract
Population-based quality indicators of either aggressive or supportive care at end of life (EOL), especially when specific to a cancer type, help to inform quality improvement efforts. This is a population-based, retrospective cohort study of gastrointestinal (GI) cancer decedents in Ontario from 1 January 2006–31 December 2018, using administrative data. Quality indices included hospitalizations, emergency department (ED) use, intensive care unit admissions, receipt of chemotherapy, physician house call, and palliative home care in the last 14–30 days of life. Previously defined aggregate measures of both aggressive and supportive care at end of life were also used. In our population of 69,983 patients who died of a GI malignancy during the study period, the odds of experiencing aggressive care at EOL remained stable, while the odds of experiencing supportive care at EOL increased. Most of our population received palliative care in the last year of life (n = 65,076, 93.0%) and a palliative care home care service in the last 30 days of life (n = 45,327, 70.0%). A significant number of patients also experienced death in an acute care hospital bed (n = 28,721, 41.0%) or had a new hospitalisation in the last 30 days of life (n = 33,283, 51.4%). The majority of patients received palliative care in the last year of life, and a majority received a palliative care home service within the last 30 days of life. The odds of receiving supportive care at EOL have increased over time. Differences in care exist according to income, age, and rurality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".