Checklist to Meet Ethical and Legal Obligations to Critically Ill Patients at the End of Life
Bibliographic record
Abstract
Despite improvements in communication, errors in end-of-life care continue to be made. For example, healthcare professionals may take direction from the wrong substitute decision-maker, or from family members when the patient is capable; permit families to propose treatment plans; conflate values and beliefs with prior expressed wishes or fail to inquire about prior expressed wishes. Sometimes healthcare professionals know what prior expressed wishes are but do not respect them; others do not believe they have enough time to have an end-of-life discussion or lack the confidence, willingness and skills to manage one. As has been shown in initiatives to improve in surgical safety, the use of a checklist presents opportunities to potentially minimize common mistakes and errors. When engaging in end-of-life care, a checklist can help focus on what needs to be communicated rather than how it needs to be communicated. We propose a checklist to support healthcare professionals in meeting their ethical and legal obligations to patients at the end of life. The checklist should minimize common mistakes, and in situations where irreconcilable conflict is unavoidable, it will ensure that both healthcare teams and family members are informed and prepared.
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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 | 0.022 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".