Seeking Worldwide Professional Consensus on the Principles of End-of-Life Care for the Critically Ill. The Consensus for Worldwide End-of-Life Practice for Patients in Intensive Care Units (WELPICUS) Study
Bibliographic record
Abstract
Great differences in end-of-life practices in treating the critically ill around the world warrant agreement regarding the major ethical principles. This analysis determines the extent of worldwide consensus for end-of-life practices, delineates where there is and is not consensus, and analyzes reasons for lack of consensus. Critical care societies worldwide were invited to participate. Country coordinators were identified and draft statements were developed for major end-of-life issues and translated into six languages. Multidisciplinary responses using a web-based survey assessed agreement or disagreement with definitions and statements linked to anonymous demographic information. Consensus was prospectively defined as >80% agreement. Definitions and statements not obtaining consensus were revised based on comments of respondents, and then translated and redistributed. Of the initial 1,283 responses from 32 countries, consensus was found for 66 (81%) of the 81 definitions and statements; 26 (32%) had >90% agreement. With 83 additional responses to the original questionnaire (1,366 total) and 604 responses to the revised statements, consensus could be obtained for another 11 of the 15 statements. Consensus was obtained for informed consent, withholding and withdrawing life-sustaining treatment, legal requirements, intensive care unit therapies, cardiopulmonary resuscitation, shared decision making, medical and nursing consensus, brain death, and palliative care. Consensus was obtained for 77 of 81 (95%) statements. Worldwide consensus could be developed for the majority of definitions and statements about end-of-life practices. Statements achieving consensus provide standards of practice for end-of-life care; statements without consensus identify important areas for future research.
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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 | 0.156 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".