Medications and dosages used in medical assistance in dying: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: There is little evidence describing the technical aspects of medical assistance in dying (MAiD) in Canada, such as medications, dosages and complications. Our objective was to describe clinical practice in providing MAiD in Ontario and Vancouver, Canada, and explore relations between medications used, time until death and complications. METHODS: We conducted a retrospective cohort study of a sample of adult (age ≥ 18 yr) patients who received MAiD in Ontario between 2016 and 2018, and patients who received MAiD in 1 of 3 Canadian academic hospitals (in Hamilton and Ottawa, Ontario, and Vancouver, British Colombia) between 2019 and 2020. We used de-identified data for 2016-2018 from the Office of the Chief Coroner for Ontario MAiD Database and chart review data for 2019-2020 from the 3 centres. We used multivariable parametric survival analysis to identify relations between medications, dosages and time from procedure start until death. RESULTS: The sample included 3557 patients (1786 men [50.2%] and 1770 women [49.8%] with a mean age of 74 [standard deviation 13] yr). The majority of patients (2519 [70.8%]) had a diagnosis of cancer. The medications most often used were propofol (3504 cases [98.5%]), midazolam (3251 [91.4%]) and rocuronium (3228 [90.8%]). The median time from the first injection until death was 9 (interquartile range 6) minutes. Standard-dose lidocaine (40-60 mg) and high-dose propofol (> 1000 mg) were associated with prolonged time until death (prolonged by a median of 1 min and 3 min, respectively). Complications occurred in 41 cases (1.2%), mostly related to venous access or need for administration of a second medication. INTERPRETATION: In a large sample of patients who died with medical assistance, certain medications were associated with small differences in time from injection to death, and complications were rare. More research is needed to identify the medication protocols that predict outcomes consistent with patient and family expectations for a medically assisted death.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 teacher head, 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".