Legal Briefing: Voluntarily Stopping Eating and Drinking
Bibliographic record
Abstract
This issue’s “Legal Briefing” column covers recent legal developments involving voluntarily stopping eating and drinking (VSED). Over the past decade, clinicians and bioethicists have increasingly recognized VSED as a medically and ethically appropriate means to hasten death. Most recently, in September 2013, the National Hospice and Palliative Care Organization (NHPCO) called on its 2,000 member hospices to develop policies and guidelines addressing VSED. And VSED is getting more attention not only in healthcare communities, but also in the general public. For example, VSED was recently highlighted on the front page of the New York Times and in other national and local media. Nevertheless, despite the growing interest in VSED, there remains little onpoint legal authority and only sparse bioethics literature analyzing its legality. This article aims to fill this gap. Specifically, we focus on new legislative, regulatory, and judicial acts that clarify the permissibility of VSED. We categorize these legal developments into the following seven categories: 1. Definition of VSED2. Uncertainty Whether Oral Nutrition and Hydration Are Medical Treatment3. Uncertainty Regarding Providers’ Obligations to Patients Who Choose VSED4. Judicial Guidance from Australia5. Judicial Guidance from the United Kingdom6. Judicial Guidance from Canada7. Case of Margot Bentley
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".