Navigating disagreement and conflict in the context of a brain-based definition of death
Bibliographic record
Abstract
In this paper, we discuss situations in which disagreement or conflict arises in the critical care setting in relation to the determination of death by neurologic criteria, including the removal of ventilation and other somatic support. Given the significance of declaring a person dead for all involved, an overarching goal is to resolve disagreement or conflict in ways that are respectful and, if possible, relationship preserving. We describe four different categories of reasons for these disagreements or conflicts: 1) grief, unexpected events, and needing time to process these events; 2) misunderstanding; 3) loss of trust; and 4) religious, spiritual, or philosophical differences. Relevant aspects of the critical care setting are also identified and discussed. We propose several strategies for navigating these situations, appreciating that these may be tailored for a given care context and that multiple strategies may be helpfully used. We recommend that health institutions develop policies that outline the process and steps involved in addressing situations where there is ongoing or escalating conflict. These policies should include input from a broad range of stakeholders, including patients and families, as part of their development and review.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".