From Neonatal Intensive Care to Neurocritical Care: Is It Still a Mirage? The Sicilian Multicenter Project
Bibliographic record
Abstract
BACKGROUND: Neonatal brain injury (NBI) can lead to a significant neurological disability or even death. After decades of intense efforts to improve neonatal intensive care and survival of critically ill newborns, the focus today is an improved long-term neurological outcome through brain-focused care. The goal of neuroprotection in the neonatal intensive care unit (NICU) is the prevention of new or worsening NBI in premature and term newborns. As a result, the neonatal neurocritical care unit (NNCU) has been emerging as a model of care to decrease NBI and improve the long-term neurodevelopment in critically ill neonates. PURPOSE: Neurocritical care (NCC) Sicilian project includes three academic sites with NICU in Sicily (Catania, Messina, and Palermo), and its primary goal is to develop neurocritical neonatal care unit (NNCU). METHODS: In 2018, the three NICUs created a dedicated space for neonates with primary neurological diagnosis or at risk for neurological injuries-NNCU. Admission criteria for eligible patients and treatment protocols were created. Contact with parents, environmental protection, basic monitoring, brain monitoring, pharmacological therapy, and organization of the staff were protocolized. RESULTS: Evaluation of the efforts to establish NNCU within existing NICU, current protocols, and encountered problems are shown. Implications for Practice. Our outcome confirmed the need for dedicated NNCU for neuroprotection of critically ill neonates at risk for a neurological injury. Although the literature on neonatal neurocritical care is still scarce, we see the value of such targeted approach to newborn brain protection and therefore we will continue developing our NNCU, even though there have been problems encountered. The project of building NNCU will continue to be closely monitored. CONCLUSIONS: The development of our neonatal neurocritical model of care is far from being completed. Although it is currently limited to the Sicilian area only, the goal of this paper is to share the development of this multicenter interdisciplinary project focused on a newborn brain protection. After evaluating our outcome, we strongly believe that a combined expertise in neonatal neurology and neonatal critical care can lead to an improved neurodevelopmental outcome for critically ill neonates, from the extremely preterm to those with brain injuries.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".