Developing a Consultation Model for Neonatal Neurocritical Care: A 2-Year Experience
Bibliographic record
Abstract
Abstract BACKGROUND: Developing brain has unique pattern of injury and responds to injury differently. Management of neonatal seizures, perinatal asphyxia as well as ischemic-hemorrhagic preterm brain injury requires specific skill sets of neonatology, neurology, neurophysiology and neuroradiology. The impact of neurological conditions extends beyond neonatal intensive care unit stay warranting continuity of care in order to improve outcomes. OBJECTIVES: Establishing a special interest group to run within the existing intensive care facility based on separate subspeciality model of neurocritical care in adults and study the impact of the change on short term outcome of neurological conditions in neonates. DESIGN/METHODS: A multidisciplinary team was formulated including faculties from Neonatology, Pediatric Neurology, Diagnostic Imaging and Neonatal Follow up to form a neonatal neurocritical care (NNCC) core team. Roles to each team members was defined and oncall schedule for round the clock coverage developed. Hypoxic Ischemic Encephalopathy (HIE) was targeted first by standardized policies and procedures to be followed after admission, introduction of continuous video EEG monitoring and series of training sessions for nurses and fellows on bedside application of diagnostic tools. The core team was involved in care of the infant from admission to discharge with a well defined follow up plan and had 24 hour remote access to EEG recorded in any of the 2 Level 3 NICU involved. RESULTS: On comparing management and outcomes prior to and following the start of NNCC we found that the use of anti-epileptic drugs (AED) reduced from 60 to 46%, the use of maintenance dose was halved. Considerable reduction in death or MRI documented brain injury was noted. CONCLUSION: Consultation model of NNCC resulted in improved communication and collaboration between disciplines, better HIE care pathway and short term outcomes, creation of database that will allow assessment of long term outcome. The group is currently targeting Intra-Ventricular hemorrhage and post-hemorrhagic hydrocephalus management. In future the team will focus on real time multimodal brain monitoring.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".