Impact of legalization of Medical Assistance in Dying on the Use of Palliative Sedation in a Tertiary Care Hospital: A Retrospective Chart Review
Bibliographic record
Abstract
BACKGROUND: Patients approaching end of life may experience intractable symptoms managed with palliative sedation. The legalization of Medical Assistance in Dying (MAiD) in Canada in 2016 offers a new option for relief of intolerable suffering, and there is limited evidence examining how the use of palliative sedation has evolved with the introduction of MAiD. OBJECTIVES: To compare rates of palliative sedation at a tertiary care hospital before and after the legalization of MAiD. METHODS: This study is a retrospective chart analysis of all deaths of patients followed by the palliative care consult team in acute care, or admitted to the palliative care unit. We compared the use of palliative sedation during 1-year periods before and after the legalization of MAiD, and screened charts for MAiD requests during the second time period. RESULTS: 4.7% (n = 25) of patients who died in the palliative care unit pre-legalization of MAiD received palliative sedation compared to 14.6% (n = 82) post-MAiD, with no change in acute care. Post-MAiD, 4.1% of deaths were medically-assisted deaths in the palliative care unit (n = 23) and acute care (n = 14). For patients who requested MAiD but instead received palliative sedation, the primary reason was loss of decisional capacity to consent for MAiD. CONCLUSION: We believe that the mainstream presence of MAiD has resulted in an increased recognition of MAiD and palliative sedation as distinct entities, and rates of palliative sedation increased post-MAiD due to greater awareness about patient choice and increased comfort with end-of-life options.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".