Why Withdrawing Life-Sustaining Treatment Should Not Require 'Rasouli Consent'
Bibliographic record
Abstract
Technology allows us to keep patients alive despite very poor prognoses and quality of life. We must therefore confront questions of when medical intervention should cease, and who should be allowed to make that decision.Until recently it was unclear whether doctors or patients have the ultimate say in whether to withhold or withdraw life-sustaining treatment. In v Sunnybrook Health Sciences Centre, the Ontario Court of Appeal held that doctors may only withdraw certain life-sustaining with the con- sent of patients or their substitute decision makers. It reasoned that withdrawing certain is treatment for which consent is required under Ontario’s Health Care Act. This effectively gives the patient an entitlement to continued life support.I argue that the law of informed consent should not dictate who may decide whether is withheld. When consent is applied to create de facto entitlements to medical treatment, as Rasouli Consent does, interests other than those of the patient become relevant, such as physicians’ interest in not having to provide non-beneficial and the public interest in not having to fund of little or no medical value. Yet the law of informed consent is exclusively patient-centered and does not allow these factors to be considered; neither the and Capacity Board nor the courts may give weight to competing interests.This is not to say that physicians should have the right unilaterally to withhold life-sustaining treatment. However, any entitlement to should flow from laws other than the law of informed consent, such as the Charter, or ideally a new law that explicitly addresses the issue.
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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 | 0.084 | 0.208 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".