In Defense of Consent and Capacity Boards for End-of-Life Care
Bibliographic record
Abstract
In Cuthbertson v. Rasouli, the Supreme Court of Canada (SCC) found that, in Ontario, it is the Consent and Capacity Board (CCB) and not the courts per se who will resolve conflicts between substitute decision-makers (SDMs) and health practitioners regarding the withdrawal of lifesustaining treatment from incapable patients. This finding was based on the SCC’s interpretation of the Ontario Health Care Consent Act (HCCA). Hawryluck et al. express concern about the SCC’s determination that the CCB is charged with resolving such conflicts since, in their view, this body is ill-equipped to fulfill this role. Instead, they take the position that these disputes should be adjudicated by the courts. We disagree with this position and, for the reasons set out in this editorial, take the position that provincial and territorial legislators across the country should follow the lead of the Ontario legislature, revise their health care consent legislation to clarify the law with respect to the unilateral withholding and withdrawal of potentially life-sustaining treatment, and establish open and transparent consent and capacity tribunals to deal with irreconcilable conflicts in this context.
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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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.034 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".