Efficacy and safety of drugs used for ‘assisted dying’
Bibliographic record
Abstract
BACKGROUND: 'Assisted dying' is practiced in some European countries and US states. Legislation suggests that there exists an easily prescribed drug which consistently brings about death quickly and painlessly. Evidence from jurisdictions where 'assisted dying' is practiced, however, reveals that hastening patient death is not so simple. SOURCES OF DATA: This report is a collation of assisted suicide and euthanasia drug protocols published by the Canadian Association of MAiD Assessors and Providers and the Royal Dutch Medical Association, annual data reports from the USA and Canada and relevant academic publications pertaining to methods of 'assisted dying' in the USA, Belgium, Canada and Switzerland. AREAS OF AGREEMENT: A wide variety of lethal drug combinations are used for people who want their life ended, and the prevalence of complications and failures in intentionally ending life suggest that 'assisted dying' applicants are at risk of distressing deaths. AREAS OF CONTROVERSY: The efficacy and safety of 'assisted dying' drugs are currently difficult to assess, as clinician reporting is often very low. GROWING POINTS: The findings from this report reveal that little attention has been given to the problem of unmonitored prescribing and administering of lethal drug combinations, whose mode of action is unclear. AREAS TIMELY FOR DEVELOPING RESEARCH: In order to properly assess the efficacy and safety of 'assisted dying', a more thorough means of data collection regarding the drugs used must be implemented and research is urgently needed into their mode of action.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".