End-of-Life Decision Making in the Seriously Ill Hospitalized Patient: An Organizing Framework and Results of a Preliminary Study
Bibliographic record
Abstract
Recent studies of patient/family satisfaction with end-of-life care suggest that improvements in communication and decision making are likely to have the greatest impact on improving the quality of end-of-life care. The apparent failure of recent studies specifically designed to improve decision making strongly suggest that there are powerful determinants of the decision making process that are not completely understood. In this paper, we present an organizing framework that describes the decision making process and breaks it into three analytic steps: information exchange, deliberation, and making the decision. In addition, we report the results of a preliminary study of end-of-life decision making that incorporates aspects of this organizing framework. Thirty-seven seriously ill hospitalized patients were interviewed. The majority wanted to share decisional responsibility with physicians. We demonstrated the feasibility of measuring certain aspects of the decision making process in such patients. By providing and using a framework related to end-of-life decision making, we hope to better understand the complex interaction and processes between dying patients, caregivers, and physicians.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".