Medical Assistance in Dying in Oncology Patients: A Canadian Academic Hospital’s Experience
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAID) was legislatively enacted in Canada in June 2016. Most studies of patients who received MAID grouped patients with cancer and non-cancer diagnoses. Our goal was to analyze the characteristics of oncology patients who received MAID in a Canadian tertiary care hospital. METHODS: We conducted a retrospective review of all patients with cancer who received MAID between June 2016 and July 2020 at London Health Sciences Centre (LHSC). We describe patients' demographics, oncologic characteristics, symptoms, treatments, and palliative care involvement. RESULTS: Ninety-two oncology patients received MAID. The median age was 72. The leading cancer diagnoses among these patients were lung, colorectal, and pancreatic. At the time of MAID request, 68% of patients had metastatic disease. Most patients (90%) had ECOG performance status of 3 or 4 before receiving MAID. Ninety-nine percent of patients had distressing symptoms at time of MAID request, most commonly pain. One-third of patients with metastatic or recurrent cancer received early palliative care. The median time interval between the first MAID assessment and receipt of MAID was 7 days. INTERPRETATION: Most oncology patients who received MAID at LHSC had poor performance status and almost all had distressing symptoms. The median time interval between first MAID assessment and receipt of MAID was shorter than expected. Only one-third of patients with metastatic or recurrent cancer received early palliative care. Improving access to early palliative care is a priority in patients with advanced cancer. STUDY REGISTRATION: We received research approval from Western University's Research Ethics Board (REB) with project ID number 115367, and from Lawson's Research Database Application (ReDA) with study ID number 9579.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".