Withholding or withdrawing life support versus physician-assisted death
Bibliographic record
Abstract
PURPOSE OF REVIEW: Withholding or withdrawing life-sustaining therapy is generally differentiated from physician-assisted suicide or euthanasia based on the distinction between intention and foresight. We reviewed the literature surrounding the validity of this distinction. RECENT FINDINGS: Many physicians from different specialties express a perceived distinction between intention and foresight. The distinction between intention and foresight differs from the morally irrelevant distinction between doing and allowing. Intention and foresight may be distinguished by their opposing directions of fit between world and mind. Intention is held to be of greater moral significance than foresight because it guides and constrains our actions, determines the moral quality of our actions, and expresses the moral character of the agent. Opponents of the distinction argue that it undermines moral accountability for foreseen consequences of our actions and is overly concerned with the physician's state of mind rather than the patient's experience. They also argue that intentions may be vague and difficult to express or ascertain. SUMMARY: Several reasons may be given in favor of the distinction between intention and foresight. Given this distinction, the moral permissibility of withholding or withdrawing life-sustaining therapy does not necessarily entail the moral permissibility of physician-assisted suicide or euthanasia.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".