Bibliographic record
Abstract
OBJECTIVE: In the modern pediatric intensive care unit (PICU) physicians are often faced with the need to interrupt life-sustaining treatment (LST) and to allow children to die when no further treatment options are available. Consequently, the importance of palliative care has been increasing in this context. The goal of this review is to provide intensivists with guidelines to allow PICU patients to have a more dignified and humane death. SOURCE OF DATA: Medline was searched using relevant key-words, emphasizing the topic of death in the PICU. The principles of palliative care medicine were then applied to this context. SUMMARY OF THE FINDINGS: To ensure a dignified death for a child receiving palliative care in the PICU some important measures must be taken, such as: let the family participate in the decision-making process in an open and honest manner; allow family members to perform their religious rites and rituals; offer them moments of complete privacy; effectively manage pain and discomfort, especially at the time of removal of LST; and finally, let the family be present when LST is interrupted, if they so desire. CONCLUSIONS: A child's death following withdrawal of LST in the PICU can be humane and dignified if basic principles of palliative care are followed. This is especially important in an environment that is notorious for the use of complex technology and described by the general public as inhumane.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".