Advance commitment: an alternative approach to the family veto problem in organ procurement
Bibliographic record
Abstract
This article tackles the current deficit in the supply of cadaveric organs by addressing the family veto in organ donation. The authors believe that the family veto matters-ethically as well as practically-and that policies that completely disregard the views of the family in this decision are likely to be counterproductive. Instead, this paper proposes to engage directly with the most important reasons why families often object to the removal of the organs of a loved one who has signed up to the donor registry-notably a failure to understand fully and deliberate on the information and a reluctance to deal with this sort of decision at an emotionally distressing time. To accommodate these concerns it is proposed to separate radically the process of information, deliberation and agreement about the harvesting of a potential donor's organs from the event of death and bereavement through a scheme of advance commitment. This paper briefly sets out the proposal and discusses in some detail its design as well as what is believed to be the main advantages compared with the leading alternatives.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".