Brief Report: Medical Assistance in Dying in Patients With Lung Cancer
Bibliographic record
Abstract
INTRODUCTION: Medical assistance in dying (MAiD) was legalized in Canada in 2016. Cancer accounts for 60% to 65% of MAiD cases. Lung cancer, the most common cause of cancer death, is expected to makeup a large number of MAiD cases. Lung cancer treatment has advanced in recent years; however, involvement of oncology specialists and use of systemic therapy in patients who receive MAiD are unknown. METHODS: All patients with lung cancer referred to the Champlain Regional MAiD Program from June 17, 2016, to November 30, 2020, were reviewed. Baseline demographics, diagnostic, referral, and treatment details were collected by retrospective review. Coprimary end points were the proportion of patients who met a medical oncologist or who received systemic therapy. RESULTS: During the study period, 255 patients with cancer underwent MAiD. Of these, 45 (17.6%) had lung cancer, comprising our final study population. Baseline characteristics: median age 72 years, 64% female, 85% former or current smoking history, 82% non-small cell, 4% small cell, and 13% clinical diagnosis without biopsy. Most patients (78%) were seen by a medical oncologist, though only 16 (36%) received systemic therapy for advanced disease. In subpopulations of interest, 45% of patients with programmed death-ligand 1 greater than or equal to 50% received immunotherapy and 75% with an oncogenic driver mutation received targeted therapy. There were 26 patients (58%) who had a documented discussion with their oncologist regarding the transition to best supportive care. CONCLUSIONS: Most patients with lung cancer are assessed by an oncology specialist before MAiD, though less than half received systemic therapy. Among patients with more treatable forms of lung cancer, many patients still undergo MAiD without accessing, or in some cases being assessed for, these treatment options.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".