End of life decisions for newborns: an ethical and compassionate process?
Bibliographic record
Abstract
In their report on neonatal death from Canada, Hellmann and colleagues1 indicate a propensity for neonatologists to employ a consensus deriving process that engages both families and the neonatal intensive care unit (NICU) interdisciplinary team. This is certainly in alignment with the process of shared decision-making, a strategy espoused by the American Academy of Pediatrics, the Nuffield Council and others. To their credit, the authors excluded stillbirths and infants <500 g and/or <23 completed weeks of gestation, but included infants admitted to the NICU, who may have died elsewhere in the hospital or at home receiving palliative care. Within Hellmann et al's report, three things stand out. First, certainty in a poor prognosis was reported to be from moderate to absolute by 88% of physicians. Clinicians may occasionally be confronted by colleagues, staff members or families who ask, ‘How certain are you?’ The degree of certainty need not be absolute in order to make recommendations for implementing, or withdrawing, care.2 There is an often missed, or misread, notion of certainty that characterises physicians in the intensive care unit. We expect of ourselves relative certainty—as best discernible by available evidence—in a scientific sense as we go about diagnosing and treating critically ill newborns. Yet, our practice involves a practical application of knowledge coloured by our best understanding of evidence and outcome expectations based on our experiences and those shared by others. Can we prognosticate with certainty? No, not absolutely, but our Canadian colleagues may be more accepting of this reality than what is …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.022 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".