Patient and healthcare professional factors influencing end-of-life decision-making during critical illness: A systematic review*
Bibliographic record
Abstract
OBJECTIVES: The need for better understanding of end-of-life care has never been greater. Debate about recent U.S. healthcare system reforms has highlighted that end-of-life decision-making is contentious. Providing compassionate end-of-life care that is appropriate and in accordance with patient wishes is an essential component of critical care. Because discord can undermine optimal end-of-life care, knowledge of factors that influence decision-making is important. We performed a systematic review to determine which factors are known to influence end-of-life decision-making among patients and healthcare providers. DATA SOURCES, SELECTION, AND ABSTRACTION: We conducted a structured search of Ovid Medline for interventional and observational research articles incorporating critical care and end-of-life decision-making terms. DATA SYNTHESIS: Of 6259 publications, 102 were relevant to our review question. Patient factors predicting less intensive end-of-life care include increasing age, comorbidity, and limited functional status; these factors appear to be influential for both clinicians and patients. Patient and clinician race, ethnicity, and nationality also appear to influence the technological intensity of end-of-life care. In general, white patients and those in North America and Northern Europe may be less likely to desire intensive end-of-life care than others. Physicians of similar geo-ethnic origin to patients appear less likely to prescribe such therapy. Physicians with more clinical experience and those routinely working in the intensive care unit are less likely than other physicians to recommend technologically intense care for critically ill patients at the end-of-life. CONCLUSIONS: Patients and clinicians may approach end-of-life discussions with different expectations and preferences, influenced by religion, race, culture, and geography. Appreciation of those factors associated with more and less technologically intense care may raise awareness, aid communication, and guide clinicians in end-of-life discussions.
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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 | 0.013 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".