Pre-Mortem Interventions for the Purpose of Organ Donation: Legal Approaches to Consent
Bibliographic record
Abstract
The administration of Pre-Mortem Interventions (PMIs) to preserve the opportunity to donate, to assess the eligibility to donate, or to optimize the outcomes of donation and transplantation are controversial as they offer no direct medical benefit and include at least the possibility of harm to the still-living patient. In this article, we describe the legal analysis surrounding consent to PMIs, drawing on existing legal commentary and identifying key legal problems. We provide an overview of the approaches in several jurisdictions that have chosen to explicitly address PMIs within codified law. We then provide, as an example, a detailed exploration of how PMIs are likely to be addressed in one jurisdiction where general medical consent law applies because there is no specific legislation addressing PMIs - the province of Ontario in Canada.
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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 | 0.066 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".