Bibliographic record
Abstract
With the gap between the number of transplantable organs and the number of people needing transplants widening, many have argued for moving from an opt-in to an opt-out system of deceased organ donation. In the first system, individuals must register their willingness to become donors after they die. In the second system, it is assumed that individuals wish to become donors unless they have registered an objection to donation. Opting out has also been described as presumed consent. Spain has had the most successful presumed consent policy, resulting in a substantial increase in the donation rate.1 Despite support for an opt-out system from the British Medical Association and other groups since 1999, the report of a task force delivered in November 2008 recommended that the current opt-in system in the UK be retained.2 The USA has also retained an opt-in system.3 Two prominent American bioethicists, Tom Beauchamp and James Childress, claim that an opt-out policy “would not likely be adopted in the United States, and if it were adopted, it probably would not increase the number of organs for transplantation because so many citizens would opt out.”4 Offering financial incentives such as paying funeral expenses for a deceased donor’s family is one way of increasing the number of organs. A regulated market in organs is another.5 In the UK at least, there is broader support for presumed consent. Among the alternatives to opting in, this system has the best prospect of eventually being adopted. Presumed consent would not obviate but retain the basic idea of consent, because individuals would still have the right to opt out and choose not to donate.6 One argument for presumed consent is that it would reduce the incidence of free riding. For a free rider, it is not rational …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".