Legislation and Policy Recommendations on Organ and Tissue Donation and Transplantation From an International Consensus Forum
Bibliographic record
Abstract
There is a shared global commitment to improving baseline donation and transplantation performance metrics in a manner consistent with ethics and local cultural and social factors. The law is one tool that can help improve these metrics. Although legal systems vary across jurisdictions, our objective was to create expert, consensus guidance for law and policymakers on foundational issues underlying organ and tissue donation and transplantation (OTDT) systems around the world. Methods: Using the nominal group technique, a group composed of legal academics, a transplant coordinator/clinician, and a patient partner identified topic areas and recommendations on foundational legal issues. The recommendations were informed by narrative literature reviews conducted by group members based on their areas of expertise, which yielded a range of academic articles, policy documents, and sources of law. Best practices were identified from relevant sources in each subtopic, which formed the basis of the recommendations contained herein. Results: We reached consensus on 12 recommendations grouped into 5 subtopics: (i) legal definitions and legislative scope, (ii) consent requirements for donation' (iii) allocation of organs and tissue' (iv) operation of OTDT systems' and (v) travel for transplant and organ trafficking. We have differentiated between those foundational legal principles for which there is a firm basis of support with those requiring further consideration and resolution. Seven such areas of controversy are identified and discussed alongside relevant recommendations. Conclusions: Our recommendations encompass some principles staunchly enshrined in the OTDT landscape (eg, the dead donor rule), whereas others reflect more recent developments in practice (eg, mandatory referral). Although some principles are widely accepted, there is not always consensus as to how they ought to be implemented. As the OTDT landscape continues to evolve, recommendations must be reconsidered for the law to keep pace with developments in knowledge, technology, and practice.
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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 | 0.158 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.034 | 0.025 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".