Logistics of Withdrawal of Life-Sustaining Therapies in PICU
Bibliographic record
Abstract
OBJECTIVES: To describe practical considerations and approaches to best practices for end-of-life care for critically ill children and families in the PICU. DATA SOURCES: Literature review, personal experience, and expert opinion. STUDY SELECTION: A sampling of the foundational and current evidence related to the withdrawal of life-sustaining therapies in the context of childhood critical illness and injury was accessed. DATA EXTRACTION: Moderated by the authors and supported by lived experience. DATA SYNTHESIS: Narrative review and experiential reflection. CONCLUSIONS: Consequences of childhood death in the PICU extend beyond the events of dying and death. In the context of withdrawal of life-sustaining therapies, achieving a quality death is impactful both in the immediate and in the longer term for family and for the team. An individualized approach to withdrawal of life-sustaining therapies that is informed by empiric and practical knowledge will ensure best care of the child and support the emotional well-being of child, family, and the team. Adherence to the principles of holistic and compassionate end-of-life care and an ongoing commitment to provide the best possible experience for withdrawal of life-sustaining therapies can achieve optimal end-of-life care in the most challenging of circumstances.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".