Withholding and withdrawing treatment : practical applications of ethical principles in end-of-life care
Bibliographic record
Abstract
Many people fear the process of dying rather than the fact of dying. This fear is often associated with interventions that may be undertaken at the end of life as well as with the knowledge that suffering may be a part of dying and that both may be associated with loss of dignity of the individual. The paper discusses the statement that withholding or withdrawing treatment can be considered a sound clinical decision when reached in discussion with the patient (if competent), the family and the clinical care team. This decision is not taken lightly and it may not be easy to reach consensus on the decision. It is therefore important that the discussion and decision making are based on established bioethical principles. In the practical setting of care for patients at the end of life the four principles of ethics articulated by Beauchamp and Childress 1 are a useful guide for the clinical team. It is important to determine the goals of care and to enter into the discussion with these goals in mind. Treatment should then be focused on achieving realistic goals of care within an ethical framework. Beauchamp and Childress articulated four principles of bioethics: respect for autonomy, beneficence, non-maleficence and justice.
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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 | 0.160 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.113 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.041 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 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".