5 Extremely Low Gestational Age Neonates and Resuscitation: Perspectives of Canadian Neonatologists
Bibliographic record
Abstract
Abstract Primary Subject area Neonatal-Perinatal Medicine Background Resuscitation care planning for extremely low gestational age neonates (ELGANs) continues to be one of the most complex, ethically charged areas within paediatrics. Objectives This study sought to determine the current attitudes and practices of neonatologists in Canada, and to assess the moral distress associated with resuscitation decisions in the ELGAN population and current practices in an era of improved neonatal outcomes. 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. Design/Methods This was a descriptive study employing an electronic survey constructed in REDCap®. The survey was distributed to neonatologists working in Level III NICUs across Canada and responses were collected from March to May 2020. Results 65 total survey responses were received. 78% of neonatologists expressed at least some moral distress when parents request non-resuscitation between 24 weeks 0 days and 24 weeks 6 days. Prognostic uncertainty with regard to individualized long-term outcome in an era with increased chances of morbidity-free survival was the most prominent factor identified as contributing to moral distress. 70% felt they would feel less moral distress deciding about goals of care after assessing the baby’s response to an initial trial of resuscitation at birth, and preferred an individualized approach to palliation decisions based on postnatal course and assessment, rather than making the decision based primarily on gestational age. Conclusion While most current practices support the option of non-resuscitation for infants born at less than 25 weeks, there is growing evidence of moral distress among Canadian neonatologists that suggests the consideration of resuscitation at 24 weeks and above is a more realistic approach in the current era of improved outcomes. Furthermore, our results suggest that Canadian neonatologists are ethically more comfortable developing plans for care postnatally, with more evidence to support prognostication, instead of antenatally, based solely 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 | 0.004 | 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 | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".