Discussing Death as a Possible Outcome of PICU Care
Bibliographic record
Abstract
OBJECTIVES: To describe practical considerations related to discussions about death or possible death of a critically ill child. DATA SOURCES: Personal experience and reflection. Published English language literature. STUDY SELECTION: Selected illustrative studies. DATA EXTRACTION: Not available. DATA SYNTHESIS: Narrative and experiential review were used to describe the following areas benefits and potential adverse consequences of conversations about risk of death and the timing of, preparation for, and conduct of conversations about risk of death. CONCLUSIONS: Timely conversations about death as a possible outcome of PICU care are an important part of high-quality ICU care. Not all patients "require" these conversations; however, identifying patients for whom conversations are indicated should be an active process. Informed conversations require preparation to provide the best available objective information. Information should include distillation of local experience, incorporate the patients' clinical trajectory, the potential impact(s) of alternate treatments, describe possible modes of death, and acknowledge the extent of uncertainty. We suggest the more factual understanding of risk of death should be initially separated from the more inherent value-laden treatment recommendations and decisions. Gathering and sharing of collective knowledge, conduct of additional investigations, and time can increase the factual content of risk of death discussions. Timely and sensitive delivery of this best available knowledge then provides foundation for high-quality treatment recommendations and decision-making.
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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 | 0.026 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".