The Impact of Country and Culture on End-of-Life Care for Injured Patients: Results From an International Survey
Bibliographic record
Abstract
BACKGROUND: Up to 20% of all trauma patients admitted to an intensive care unit die from their injuries. End-of-life decision making is a variable process that involves prognosis, predicted functional outcomes, personal beliefs, institutional resources, societal norms, and clinician experience. The goal of this study was to better understand end-of-life processes after major injury by comparing clinician viewpoints from various countries and cultures. METHODS: A clinician-based, 38-question international survey was used to characterize the impacts of medical, religious, social, and system factors on end-of-life care after trauma. RESULTS: A total of 419 clinicians from the United States (49%), Canada (19%), South Africa (11%), Europe (9%), Asia (8%), and Australasia (4%) completed the survey. In America, the admitting surgeon guided most end-of-life decisions (51%), when compared with all other countries (0-27%). The practice structure of American respondents also varied from other regions. Formal medical futility laws are rarely available (14-38%). Ethical consultation services are often accessible (29-98%), but rarely used (0-29%), and typically unhelpful (<30%). End-of-life decision making for patients with traumatic brain injuries varied extensively across regions with regard to the impact of patient age, Glasgow Coma Scale score, and clinician philosophy. Similar differences were observed for spinal cord injuries (age and functional level). The availability and use of "donation after cardiac death" also varied substantially between countries. CONCLUSIONS: In this unique study, geographic differences in religion, practice composition, decision-maker viewpoint, and institutional resources resulted in significant variation in end-of-life care after injury. These disparities reflect competing concepts (patient autonomy, distributive justice, and religion).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".