Comparative Analysis of Individual Case Reviews in Assisted Dying Oversight Systems Internationally
Bibliographic record
Abstract
Abstract As more jurisdictions consider introducing assisted dying laws, questions are raised as to optimal oversight mechanisms. There have been a variety of approaches taken internationally to oversight of individual cases of assisted dying. All jurisdictions examined in this article require clinical prospective review, where a second practitioner assesses the person’s eligibility. Jurisdictions also uniformly permit some kind of state retrospective review, whereby a patient’s death is examined after they have died although the nature of these reviews varies. Some jurisdictions have introduced state prospective review, where approval is required before assisted dying can occur, and Québec has a system of clinical retrospective review. Some recent approaches challenge the traditional dichotomy of prospective and retrospective review by also introducing the capability of undertaking some form of contemporaneous review. This article undertakes a detailed examination and comparison of the various international approaches to oversight of individual cases of assisted dying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".