Neonatal stabilization in Canada: Updates to acute care of at-risk newborns (ACoRN) practices and programming
Bibliographic record
Abstract
Disparities in preterm birth and neonatal mortality rates persist in Canada, in part as the result of insufficient training in newborn resuscitation and stabilization care, and inconsistent adherence to best practices. The Neonatal Resuscitation Program (NRP) has been the standard of care in all facilities providing perinatal care in Canada since the 1990s, but perinatal care providers and educators have continued to recognize gaps in knowledge and skill when stabilizing newborns post-resuscitation, especially in settings where this care is encountered infrequently. The Acute Care of at-Risk Newborns (ACoRN) program was developed to bridge such gaps. In ACoRN, an initial Primary Survey and systems-based care pathways (Sequences) prioritize and guide the assessment, essential care, and management of at-risk or unwell newborns in the first hours and days of life. This practice point highlights changes to practice and recommendations since 2012, when the ACoRN text and program were last revised. Like NRP, ACoRN is administered in Canada by the Canadian Paediatric Society (CPS). A newly revised and updated textbook and teaching program, both launched in 2021, will standardize care, increase competence and confidence among perinatal care providers, and improve neonatal outcomes in Canada and elsewhere in years to come.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".