International Donation and Transplantation Legislative and Policy Forum: Methods and Purpose
Bibliographic record
Abstract
Organ and tissue donation and transplantation (OTDT) legislation and policies vary around the world, and this variability contributes to discrepancies in system performance. This article describes the purpose and methodology of an international forum that was organized to create consensus recommendations related to key legal and policy attributes of an ideal OTDT system. The intent is to create guidance for legislators, regulators, and other system stakeholders who aim to create or reform OTDT legislation and policy. Methods: This Forum was initiated by Transplant Québec and cohosted by the Canadian Donation and Transplantation Program partnered with multiple national and international donation and transplantation organizations. Seven domains were identified by the scientific committee' and domain working groups identified specific topics for recommendations: Baseline Ethical Principles, Legal Foundations, Consent Model and Emerging Legal Issues, Donation System Architecture, Living Donation, Tissue Donation, and Research and Innovation Systems and Emerging Issues. Patient, family, and donor partners were integrated into every stage of the planning and execution of the Forum. Sixty-one participants from 13 countries contributed to recommendation generation. Topic identification and recommendation consensus was completed over a series of virtual meetings from March to September 2021. Consensus was achieved by applying the nominal group technique informed by literature reviews performed by participants. Recommendations were presented at a hybrid in-person and virtual forum in Montreal, Canada, in October 2021. Output: Ninety-four recommendations (9-33 per domain) and an ethical framework for evaluating new policies were developed during the Forum proceedings. The accompanying articles include the recommendations from each domain and justifications that link the consensus to existing literature and ethical or legal concepts. Conclusions: Although the recommendations could not account for the vast global diversity of populations, healthcare infrastructure, and resources available to OTDT systems, they were written to be as widely applicable as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.176 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".