When a Child Dies in the PICU Despite Ongoing Life Support
Bibliographic record
Abstract
OBJECTIVES: To examine the circumstance of death in the PICU in the setting of ongoing curative or life-prolonging goals. DATA SOURCES: Multidisciplinary author group, international expert opinion, and use of current literature. DATA SYNTHESIS: We describe three common clinical scenarios when curative or life-prolonging goals of care are pursued despite a high likelihood of death. We explore the challenges to providing high-quality end-of-life care in this setting. We describe possible perspectives of families and ICU clinicians facing these circumstances to aid in our understanding of these complex deaths. Finally, we offer suggestions of how PICU clinicians might improve the care of children at the end of life in this setting. CONCLUSIONS: Merging curative interventions and optimal end-of-life care is possible, important, and can be enabled when clinicians use creativity, explore possibilities, remain open minded, and maintain flexibility in the provision of critical care medicine. When faced with real and perceived barriers in providing optimal end-of-life care, particularly when curative goals of care are prioritized despite a very poor prognosis, tensions and conflict may arise. Through an intentional exploration of self and others' perspectives, values, and goals, and working toward finding commonality in order to align with each other, conflict in end-of-life care may lessen, allowing the central focus to remain on providing optimal support for the dying child and their family.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".