Reasons for requesting medical assistance in dying.
Bibliographic record
Abstract
OBJECTIVE: To review the charts of people who requested medical assistance in dying (MAID) to examine their reasons for the request. DESIGN: Retrospective chart survey. SETTING: British Columbia. PARTICIPANTS: Patients who requested an assisted death and were assessed by 1 of 6 physicians in British Columbia during 2016. MAIN OUTCOME MEASURES: Patients' diagnoses and reasons for requesting MAID. RESULTS: Data were collected from 250 assessments for MAID: 112 of the patients had assisted deaths, 11 had natural deaths, 35 were assessed as not eligible for MAID, and most of the rest were not ready. For people who had assisted deaths, disease-related symptoms were given as the first or second most important reason for requesting assisted death by 67 people (59.8%), while 59 (52.7%) gave loss of autonomy, 55 (49.1%) gave loss of ability to enjoy activities, and 27 (24.1%) gave fear of future suffering. People who were assessed as eligible but who had not received assisted deaths were more likely to list fear of future suffering (33.7% vs 7.1%) and less likely to list disease-related symptoms (17.4% vs 40.2%) than those who received MAID were. There was a difference in reasons for MAID given by people with different diagnoses; disease-related symptoms were given as the most important reason by 39.0% of patients with malignancies, 6.8% of patients with neurological diseases, and 28.9% of patients with end-organ failure. Loss of autonomy was given as the most important reason by 16.0% of patients with malignancies, 36.4% of patients with neurological diseases, and 23.7% of patients with end-organ failure. CONCLUSION: This study shows that the reasons patients give for requesting an assisted death are similar to those reported in other jurisdictions with similar laws, but in different proportions. Loss of autonomy and loss of ability to enjoy activities were less common reasons among patients in this study compared with other jurisdictions. This might be related to the method of data collection, as in this study, the patients' reasons were recorded by physicians.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".