Ethics of End-of-Life Care: Alzheimer's and Assisted Dying
Bibliographic record
Abstract
Assisted dying and euthanasia are hotly contested end-of-life care options that present pressing dilemmas concerning patient autonomy, relief of suffering, and the misapplication of these delicate medical procedures. Access to and prohibition of these options is predicated on years of legal history, including continuous adjustment, and is actively contested in many places. Each country where at least one of these options is accessible, such as the United States, Canada, and the Netherlands, possesses largely similar restrictions and guidelines, with notable exceptions. Through the examination of data in countries that allow access to assisted dying, euthanasia, or both, it is possible to gather evidence on the impact of these laws and to explore whether current United States law should be adjusted to be more permissive or restrictive. Data showing extremely low rates of prescription and substantial rates of non-ingestion in the US suggests that current laws and restrictions err on the side of caution – perhaps prohibiting access to patients who would otherwise qualify for assisted dying. In contrast, high rates of administration of assisted dying procedures and low rates of withdrawal from the process in the Netherlands and Canada suggest that these locations allow access at too great a rate – perhaps in relation to socialized medicine and policies that allow access to non-terminal patients. Across all locations, lawmakers are faced with the question of how and whether assisted dying can be ethically provided – as laws must make decisive rulings on medical situations that are highly personal and may vary wildly, even between two people diagnosed with the same condition at the same time. Laws allowing assisted dying seek to provide a controlled choice to end one’s own suffering in patients who are experiencing unbearable pain, and often cite concerns such as the fear of future decline and the loss of quality of life by implementing restrictions that necessitate in-depth discussions with multiple physicians, alongside other location-specific requirements. Through the examination of the role of patient autonomy and the options afforded to patients experiencing these extreme end-of-life situations, the ability of assisted dying to relieve suffering in terminal patients in a controlled and painless manner, and a discussion of current data, this work will posit that the current structure of United States laws on assisted dying is extremely restrictive – but that this restrictive approach adequately protects terminal patients and prevents the US from experiencing already high rates of deaths by assisted dying overseas climbing at the steep rates that can be seen in the Netherlands and Canada, and should not be adjusted to be more permissive.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".