Gifts of the Future: The Legal and Moral Implications of a Testator Devising a Cryopreserved Anatomical Gift to a Beneficiary Who Does Not Yet Need the Gift
Bibliographic record
Abstract
Addresses some of the legal and moral implications that arise when a testator devises a cryopreserved anatomical gift to a beneficiary who does not yet need the gift. In Part I, the institutions that handle anatomical gifts are presented to lay the foundation for this article for three main reasons: first, to look into the process of how individuals may donate their organs; second, to discuss the current methods surgeons use to harvest an organ; and, third, explore the feasibility of cryopreserving an organ. Part II explores the future outlook and moral implications that arise if cryopreserved anatomical gifts can be devised to a beneficiary. Part III addresses the current laws that make anatomical gifts possible. Lastly, Part IV proposes some limitations to a testator's ability to devise a cryopreserved anatomical gift to a beneficiary who does not yet need the gift. The research and assertions within this comment investigate whether or not the legal world is ready to deal with the ramifications this may entail. Explores what the legal world must do to be fully prepared for when science collides with reality.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".