Surviving Long Enough to Die? An Analysis of Incomplete Assessments for Medical Assistance in Dying
Bibliographic record
Abstract
Background: Medical assistance in dying (MAiD) was legalized in Canada on June 17, 2016, yet many who request MAiD do not complete the assessment process and instead experience a natural death. This analysis of patients who made a formal request for MAiD aims to clarify timelines and factors associated with completion of the MAiD assessment process, and factors associated with completion or noncompletion of MAiD once eligible. Materials and Methods: This retrospective cohort study included all patients in Nova Scotia who requested MAiD between January 1, 2018 and December 31, 2018, were deceased at the time of analysis, did not withdraw their request, and were not formally deemed ineligible for the procedure (n = 218). Descriptive statistics, Kaplan–Meier curves, and logistic regression were used in data analysis. Results: Of 218 patients, 48 did not complete the MAiD assessment process. Of the 170 patients who completed the assessment process and were deemed eligible for MAiD, 79.4% (n = 135) completed the procedure. Those with an incomplete assessment had a median survival from request to death of 8.0 days (interquartile range [IQR] = 11.5), whereas for those deemed eligible, median survival from request to determination of MAiD eligibility was also 8.0 days (IQR = 16.0). Interpretation: Proximity to natural death and poor performance status at the time of MAiD request may drive incomplete MAiD assessments. The majority of patients deemed eligible for MAiD complete the procedure, and as such, patients who did not complete the MAiD assessment process may not have experienced their preferred mode of death.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".