Extremely low gestational age neonates and resuscitation: survey on perspectives of Canadian neonatologists
Bibliographic record
Abstract
OBJECTIVES: Resuscitation care planning for extremely low gestational age neonates (ELGANs) is complex and ethically charged. Increasing survival at lower gestational ages has had a significant impact on this complexity. It also has an impact on healthcare resource utilization and policy development in Canada. This study sought to determine the current attitudes and practices of neonatologists in Canada, and to assess moral distress associated with resuscitation decisions in the ELGAN population. It also aimed to explore the perspectives of adopting a shared decision-making approach where further data with regard to best interests and prognosis are gathered in an individualized manner after birth. METHODS: Neonatologists in Canadian level III NICUs were surveyed in 2020. RESULTS: Amongst the 65 responses, 78% expressed moral distress when parents request non-resuscitation at 24 weeks. Uncertainty around long-term outcomes in an era with improved chances of morbidity-free survival was the most prominent factor contributing to moral distress. 70% felt less moral distress deciding goals of care after the baby's initial resuscitation and preferred an individualized approach to palliation decisions based on postnatal course and assessment. CONCLUSIONS: While most current guidelines still support the option of non-resuscitation for infants born at less than 25 weeks, we show evidence of moral distress among Canadian neonatologists that suggests the consideration of routine resuscitation from 24 weeks and above is a more ethical approach in the current era of improved outcomes. Canadian neonatologists identified less moral distress when goals of care are developed postnatally, with availability of more evidence for prognostication, instead of antenatally based primarily on gestational age.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".