Outcomes of Neonates With Complex Medical Needs
Bibliographic record
Abstract
BACKGROUND: Children with complex medical needs (CMN) are high healthcare resource utilizers, have varying underlying diagnoses, and experience repeated hospitalizations. Outcomes on neonatal intensive care (NICU) patients with CMN are unknown. PURPOSE: The primary aim is to describe the clinical profile, resource use, prevalence, and both in-hospital and postdischarge outcomes of neonates with CMN. The secondary aim is to assess the feasibility of sustaining the use of the neonatal complex care team (NCCT). METHODS: A retrospective cohort study was conducted after implementing a new model of care for neonates with CMN in the NICU. All neonates born between January 2013 and December 2016 and who met the criteria for CMN and were cared for by the NCCT were included. RESULTS: One hundred forty-seven neonates with a mean (standard deviation) gestational age of 34 (5) weeks were included. The major underlying diagnoses were genetic/chromosomal abnormalities (48%), extreme prematurity (26%), neurological abnormality (12%), and congenital anomalies (11%). Interventions received included mechanical ventilation (69%), parenteral nutrition (68%), and technology dependency at discharge (91%). Mortality was 3% before discharge and 17% after discharge. Postdischarge hospital attendances included emergency department visits (44%) and inpatient admissions (58%), which involved pediatric intensive care unit admissions (26%). IMPLICATIONS FOR PRACTICE: Neonates with CMN have multiple comorbidities, high resource needs, significant postdischarge mortality, and rehospitalization rates. These cohorts of NICU patients can be identified early during their NICU course and serve as targets for implementing innovative care models to meet their unique needs. IMPLICATIONS FOR RESEARCH: Future studies should explore the feasibility of implementing innovative care models and their potential impact on patient outcomes and cost-effectiveness.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".