Tissue and Cell Donation: Recommendations From an International Consensus Forum
Bibliographic record
Abstract
Organ, tissue, and cell donation and transplantation legislation and policies vary substantially worldwide, as do performance outcomes in various jurisdictions. Our objective was to create expert, consensus guidance that links evidence and ethical concepts to legislative and policy reform for tissue and cell donation and transplantation systems. Methods: We identified topic areas and recommendations through consensus, using nominal group technique. The proposed framework was informed by narrative literature reviews and vetted by the project's scientific committee. The framework was presented publicly at a hybrid virtual and in-person meeting in October 2021 in Montréal, Canada, where feedback provided by the broader Forum participants was incorporated into the final manuscript. Results: This report has 13 recommendations regarding critical aspects affecting the donation and use of human tissues and cells that need to be addressed internationally to protect donors and recipients. They address measures to foster self-sufficiency, ensure the respect of robust ethical principles, guarantee the quality and safety of tissues and cells for human use, and encourage the development of safe and effective innovative therapeutic options in not-for-profit settings. Conclusions: The implementation of these recommendations, in total or in part, by legislators and governments would benefit tissue transplantation programs by ensuring access to safe, effective, and ethical tissue- and cell-based therapies for all patients in need.
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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 | 0.353 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.012 | 0.020 |
| Research integrity | 0.033 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".