Hospital policy on appropriate use of life-sustaining treatment
Bibliographic record
Abstract
OBJECTIVE: To describe the issues faced, and how they were addressed, by the University of Toronto Critical Care Medicine Program/Joint Centre for Bioethics Task Force on Appropriate Use of Life-Sustaining Treatment. The clinical problem addressed by the Task Force was dealing with requests by patients or substitute decision makers for life-sustaining treatment that their healthcare providers believe is inappropriate. DESIGN: Case study. SETTING: The University of Toronto Joint Centre for Bioethics/Critical Care Medicine Program Task Force on Appropriate Use of Life-Sustaining Treatment. PARTICIPANTS: The 24-member Task Force included physician and nursing leaders from five critical care units, bioethicists, a legal scholar, a health administration expert, a social worker, and a hospital public relations professional. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Our specific lessons learned include a) a policy focus on process; b) use of a negotiation and mediation model, rather than a hospital ethics committee model, for this process; and c) the policy development process is itself a negotiation, so we recommend equal involvement of interested groups including patients, families, and the public. CONCLUSIONS: This article describes the key issues faced by the Task Force while developing its policy. It will provide a useful starting point for other groups developing policy on appropriate use of life-sustaining treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".