Bibliographic record
Abstract
"Terminal sedation" refers to the use of sedation as palliation in dying patients with a terminal diagnosis. Although terminal sedation has received widespread legal and ethical justification, the practice remains ethically contentious, particularly as some hold that it foreseeably hastens death. It has been proposed that empirical studies show that terminal sedation does not hasten death, or that even if it may hasten death it does not do so in a foreseeable way. Nonetheless, it is clear that providing terminal sedation in combination with the withholding or withdrawing of life-prolonging treatments such as fluid and nutrition can foreseeably hasten death significantly-what is here called early terminal sedation (ETS). There are ethical justifications for the use of sedation in palliative care and thus it would seem that ETS is an ethically and legally acceptable practice. However, what emerges from the literature is the repeated assertion that terminal sedation must be restricted to use in imminently dying patients--the "imminence condition"--and that therefore ETS is unacceptable. This restriction has taken on greater significance with the trend of palliative care to include the care of patients who are not imminently dying. This paper proposes to show that although there is widespread intuitive support for the imminence condition, it does not follow from the justifications for sedation as palliation, and that explicit arguments for the imminence condition are needed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| 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".