Variations in nurse involvement in end-of-life practices in intensive care units worldwide (ETHICUS-2): a prospective observational study.
Bibliographic record
Abstract
Abstract Purpose explore global ICU nurse involvement in end of life decisions, ETHICUS-2 prospective multinational, observational study was performed. Methods This is a secondary analysis of data obtained from the ETHICUS-2 study. Patients were followed until ICU discharge, death, or 2 months from the first decision to limit life-prolonging therapies. Data were collected by an ICU clinician recording end of life decisions typically at the time of first limitation. Nursing related questions included: were withholding or withdrawing treatment discussed with nurses? Who initiated the end of life discussion? Was a nurse involved in making the end of life decision? Was there agreement between physicians and nurses. Global regions were compared related to these four questions. Results A significant difference was found between regions, (p< .001) with Northern Europe and Australia/New Zealand having the most discussion with nurses and Latin America, Africa, Asia and North America the least. The percentages of nurses who were involved end of life decision ranging between 3-44%, the differences were statistically significant. Agreement between physicians and nurses related to decisions resulted in wide range of responses (27-86%) (p < .001). The percentages of EOL decisions in which agreement between physicians and nurses was not applicable included a wide range of responses from 0-41% Conclusion A large variability in nurse involvement in end of life care in the ICU was found. Perhaps due to perception of physicians and nurses view of themselves. Varying levels of expertise and experience of clinicians.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".