Decision Making and End-of-Life Care in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVES: 1) To comment on the medical literature on decision making regarding end-of-life therapy, 2) to analyze the data on disagreement about such therapy, including palliative care, and withholding and withdrawal practices for critically ill children in the pediatric intensive care unit (PICU), and 3) to make some general recommendations. DATA SOURCES AND STUDY SELECTION: All papers published in peer-reviewed journals, and all chapters on end-of-life therapy, or on conflict between parents and caregivers about end-of-life decisions in the PICU were retrieved. RESULTS: We found three case series, three systematic descriptive studies, two qualitative studies, four surveys, and many legal opinions, editorials, reviews, guidelines, and book chapters. The main determinants of end-of-life decisions are the child's age, premorbid cognitive condition and functional status, pain or discomfort, probability of survival, and quality of life. Risk factors in persistent conflict between parents and caregivers about end-of-life care include a grave underlying condition or an unexpected and severe event. CONCLUSION: Making decisions about end-of-life care is a frequent event in the PICU. Children may need both intensive care and palliative care concurrently at different stages of their illness. Disagreements are more likely to be resolved if the root cause of the conflict is better understood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".