Legal assistance in dying for people with brain tumors
Bibliographic record
Abstract
The number of countries and states that have legalized assistance in dying under various names (Medical Assistance in Dying, Death with Dignity, etc.) has continued to grow in recent years, allowing this option for more patients. Most of these laws include restrictions for eligibility based on a terminal diagnosis and estimated prognosis, as well as asking certifying providers to attest to the cognitive and psychiatric competence and capacity of patients requesting access. Some laws also require that patients must be able to 'self-administer' the regimen, though details vary. Such determinations can be vague and difficult to clearly apply to patients with neurologic conditions and primary or metastatic brain tumors. There is currently a lack of rigorous studies guiding providers on how to apply these important legal criteria to this special and common patient population. As access to legal assistance in dying expands, more research is needed on how to ethically apply the laws and guide patients, families and providers through the process.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".