The Process of End-of-Life Decision-Making in Pediatrics: A National Survey in the Netherlands
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to investigate how Dutch pediatric specialists reach end-of-life decisions, how they involve parents, and how they address conflicts. METHODS: We conducted a national cross-sectional survey among pediatric intensivists, oncologists, neurologists, neurosurgeons, and metabolic pediatricians practicing in the 8 Dutch university hospitals. We collected information on respondents' overall opinions and their clinical practice. RESULTS: Of the 185 eligible pediatric specialists, 74% returned the questionnaire. All responding physicians generally discuss an end-of-life decision with colleagues before discussing it with parents. In half of the reported cases, respondents informed parents about the intended decision and asked their permission. In one-quarter of the cases, respondents informed parents without asking for their permission. In the remaining one-quarter of the cases, respondents advised parents and consequently allowed them to have the decisive voice. The chosen approach is highly influenced by type of decision and type and duration of treatment. Conflicts within medical teams arose as a result of uncertainties about prognosis and treatment options. Most conflicts with parents arose because parents had a more positive view of the prognosis or had religious objections to treatment discontinuation. All conflicts were eventually resolved by a combination of strategies. In 66% of all cases, pain and symptom management were intensified before the child's death. CONCLUSIONS: Within Dutch pediatrics, end-of-life decisions are team decisions. Pediatric specialists differ considerably in how they involve parents in end-of-life decision-making, ranging from benevolent paternalism to parental autonomy. Main conflict-solving strategies are taking more time and extending discussions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".