Death determination by neurologic criteria—what do families understand?
Bibliographic record
Abstract
PURPOSE: Currently, there is little empirical data on family understanding about brain death and death determination. The purpose of this study was to describe family members' (FMs') understanding of brain death and the process of determining death in the context of organ donation in Canadian intensive care units (ICUs). METHODS: We conducted a qualitative study using semistructured, in-depth interviews with FMs who were asked to make an organ donation decision on behalf of adult or pediatric patients with death determination by neurologic criteria (DNC) in Canadian ICUs. RESULTS: From interviews with 179 FMs, six main themes emerged: 1) state of mind, 2) communication, 3) DNC may be counterintuitive, 4) preparation for the DNC clinical assessment, 5) DNC clinical assessment, and 6) time of death. Recommendations on how clinicians can help FMs to understand and accept DNC through communication at key moments were described including preparing FMs for death determination, allowing FMs to be present, and explaining the legal time of death, combined with multimodal strategies. For many FMs, understanding of DNC unfolded over time, facilitated with repeated encounters and explanation, rather than during a single meeting. CONCLUSION: Family members' understanding of brain death and death determination represented a journey that they reported in sequential meeting with health care providers, most notably physicians. Modifiable factors to improve communication and bereavement outcomes during DNC include attention to the state of mind of the family, pacing and repeating discussions according to families' expressed understanding, and preparing and inviting families to be present for the clinical determination including apnea testing. We have provided family-generated recommendations that are pragmatic and can be easily implemented.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".