Quality of palliative and end-of-life care: a quantitative study of temporal trends and differences according to illness trajectories in Quebec (Canada)
Bibliographic record
Abstract
BACKGROUND: Our aim was to assess temporal trends and compare quality indicators related to Palliative and End-of-Life Care (PEoLC) experienced by people dying of cancer (trajectory I), organ-failure (Trajectory II), and frailty/dementia (trajectory III) in Quebec (Canada) between 2002 and 2016. METHODS: This descriptive population-based study focused on the last month of life of decedents who, based on the principal cause of death, would have been likely to benefit from palliative care. Five PEoLC indicators were assessed: home deaths (1), deaths in acute care beds with no PEoLC services (2), at least one Emergency Room (ER) visit in the last 14 days of life (3), ER visits on the day of death (4) and at least one Intensive Care Unit (ICU) admission in the last month of life (5). Data were obtained from Quebec's Integrated Chronic Disease Surveillance System (QICDSS). RESULTS: The annual percentage of home deaths increased slightly between 2002 and 2016 in Quebec, rising from 7.7 to 9.1%, while the percentage of death during a hospitalization in acute care without palliative care decreased from 39.6% in 2002 to 21.4% in 2016. Patients with organ failure were more likely to visit the ER on the day of death (20.9%) than patients dying of cancer and dementia/frailty with percentages of 12.0% and 6.4% respectively. Similar discrepancies were observed for ICU visits in the last month and ER visits in the last 14 days. CONCLUSION: PEoLC indicators showed more aggressiveness of care for patients with organ failure and highlight the need for more equitable access to quality PEoLC between malignant and non-malignant illness trajectories. These results underline the challenges of providing timely and optimal PEoLC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".