Bibliographic record
Abstract
This paper argues that there is little difference between opt-in and opt-out organ donation systems for increasing donor numbers when used in isolation. Independently diverting to an opt-out system confers no obvious advantage and can harm efforts to bolster donations. Rather, it is essential to address barriers to organ donation on several levels along with a switch in system. Moreover, for many countries, it may be more beneficial to adequately capacitate the donation system already in place, rather than entertain a significant change with its attendant resource requirements. For decades, the international transplant community has been involved in vigorous debate as to the merits of moving from default opt-in systems to opt-out policies to grow organ donor numbers and better meet the ever-increasing demand for lifesaving transplants. Opt-out is certainly en vogue, with Wales, England and Nova Scotia recently switching over, Scotland due to become opt-out in March 2021 and Northern Ireland and Canada seriously considering a similar move. Thanks to several countries making the switch from opt-in to opt-out over the last 20-30 years, there are sets of robust longitudinal data that aid in analysing the efficacy of donation systems. However, these data are often contradictory and largely inconclusive, suggesting other factors may be in play. This paper reviews some emerging trends in opt-in versus opt-out organ donation policies and considers recent data that elucidates some of the main contentions across each. Ethical frameworks underpinning donation systems, such as informed consent, trust and transparency, are discussed in detail. Substantial time is also devoted to opt-in vs opt-out systems in developing countries, which tend to be excluded from many analyses, and where the challenges faced are magnified by socio-economic constraints. This constitutes a major gap in recently published literature, as developing countries often lag far behind their developed counterparts in donor and transplant numbers.
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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 | 0.065 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".