Opting in to an Opt-out System: Presumed Consent as a Valid Policy Choice for Ontario's Cadaveric Organ Shortage
Bibliographic record
Abstract
Established within the context of a severe shortage of organs and tissues for transplantation, this thesis explores whether presumed consent for cadaveric organ donation is a legitimate policy choice for Ontario. The medical, legal and social reasons underlying organ scarcity and increased demand for transplantation are examined, and the shortcomings of Ontario’s current express consent system are analyzed. The various criticisms of presumed consent are also explored, including concerns with respect to its effectiveness, level of public support and implications for personal autonomy. Although the Citizens Panel on Increasing Organ Donations recommended against enacting presumed consent legislation, it is argued that the Panel was too dismissive of this concept given a perceived lack of public support. It is concluded that presumed consent can meet the concerns of critics, and that as part of a broader strategy could significantly increase the number of cadaveric organ and tissue donors in the province.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".