Medical Assistance in Dying in Patients With Cancer
Bibliographic record
Abstract
PURPOSE: Medical assistance in dying (MAiD) was legalized in Canada in 2016. To date, patients with cancer account for 69% of MAiD deaths, yet little information is available about these patients. We reviewed disease and treatment characteristics of patients with cancer who underwent MAiD to better understand this population and identify gaps in our current system of care. MATERIALS AND METHODS: Patients with cancer who underwent MAiD through the Champlain Regional MAiD Network from June 2016 to November 2020 were reviewed. Baseline demographic, diagnostic, and treatment details were collected by retrospective review. RESULTS: During the study period, 255 patients with cancer underwent MAiD. At the time of MAiD, 201 patients (79%) had metastatic disease. Most prevalent solid organ tumors were gastrointestinal (30%), lung (18%) and genitourinary (14%). MAiD was primarily provided in the home (48%) or an acute inpatient facility (40%). One hundred eighty-nine (74%) patients were evaluated by medical oncology, 23 by gynecology oncology (9%), 11 by hematology oncology (4%), and 177 (69%) by radiation oncology. One hundred fifty-eight (62%) patients were not seen by oncology specialists in the 30 days prior to MAiD. One hundred fifty-nine patients (62%) had at least one line of systemic therapy, 138 patients (54%) received radiotherapy, and 61 patients (24%) did not receive cancer-directed treatment. Palliative care assessed at least 213 patients (84%). Common reasons for pursuing MaiD included disease-related symptoms (33%), fear of future suffering or disability (19%), and the ability to control the time and manner of death (17%). In 36% of cases, the reason was not documented. CONCLUSION: Although formal oncology consultation is not required before MAiD, with an ever-increasing number of novel cancer therapies, oncologists, cancer centers, and MAiD providers should consider collaborating to ensure a streamlined assessment process for patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".