Neonatal end of life care in a tertiary care centre in Canada: a brief report.
Bibliographic record
Abstract
OBJECTIVE: To describe the processes followed by a neonatal team engaging parents with respect to end of life care of babies in whom long term survival was negligible or impossible; and to describe feedback from these parents after death of their child. METHODS: A retrospective review was conducted of health records of neonates who had died receiving palliative care over a period of 5 years at a tertiary neonatal centre. Specific inclusion criteria were determined in advance that identified care given by a dedicated group of caregivers. RESULTS: Thirty infants met eligibility criteria. After excluding one outlier an average of 4 discussions occurred with families before an end of life decision was arrived at. Switching from aggressive care to comfort care was a more common decision-making route than having palliative care from the outset. Ninety per cent of families indicated satisfaction with the decision making process at follow-up and more than half of them returned later to meet with the NICU team. Some concerns were expressed about the availability of neonatologists at weekends. CONCLUSIONS: A compassionate and humane approach to the family with honesty and empathy creates a positive environment for decision-making. An available, experienced team willing to engage families repeatedly is beneficial. Initiating intensive care with subsequent palliative care is acceptable to families and caregivers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".