Symptom Burden and Complexity in the Last 12 Months of Life among Cancer Patients Choosing Medical Assistance in Dying (MAID) in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: In 2019, cancer patients comprised over 65% of all individuals who requested and received Medical Assistance in Dying (MAID) in Canada. This descriptive study sought to understand the self-reported symptom burden and complexity of cancer patients in the 12 months prior to receiving MAID in Alberta. METHODS: Between July 2017 and January 2019, 337 cancer patients received MAID in Alberta. Patient characteristics were descriptively analyzed. As such, 193 patients (57.3%) completed at least one routine symptom-reporting questionnaire in their last year of life. Mixed effects models and generalized estimating equations were utilized to examine the trajectories of individual symptoms and overall symptom complexity within the cohort over this time. RESULTS: The results revealed that all nine self-reported symptoms, and the overall symptom complexity of the cohort, increased as patients' MAID provision date approached, particularly in the last 3 months of life. While less than 20% of patients experienced high symptom complexity 12 months prior to MAID, this increased to 60% in the month of MAID provision. CONCLUSIONS: Cancer patients in this cohort experienced increased symptom burden and complexity leading up to their death. These findings could serve as a flag to clinicians to closely monitor advanced cancer patients' symptoms, and provide appropriate support and interventions as needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".