B.01 Neonatal Neuro-Critical Care (NNCC) program associated with improved short term outcomes in neonates significant Hypoxic Ischemic Encephalopathy (HIE)
Bibliographic record
Abstract
Background: Despite advances in neonatal care, neonates with moderate to severe HIE are at high risk of mortality and morbidity. we report the impact of a dedicated NNCC team on short term mortality and morbidities. Methods: A retrospective cohort study on neonates with moderate to serve HIE between July 1st 2008 and December 31st 2017. primary outcome : a composite of death and/or brain injury on MRI. Secondary outcomes: rate of cooling, length of hospital stay, anti-seizure medication burden, and use of inotropes. A regression analysis was done adjusting for gestational age, birth weight, gender, out-born status, Apgar score at 10 minutes, cord blood pH, and HIE clinical staging Results: 216 neonates were included, 109 before NNCC implementation, and 107 thereafter. NNCC program resulted in reduction in the primary outcome (AOR: 0.28, CI: 0.14-0.54, p<0.001) and brain injury (AOR: 0.28, CI: 0.14-0.55, p<0.001). It decreased average length of stay/infants by 5 days (p=0.03), improved cooling rate (73% compared to 93% , p <0.001), reduced: seizure misdiagnosis (71% compared to 23%, P <0.001), anti-seizure medication burden (P = 0.001), and inotrope use (34% compared to 53%, p=0.004) Conclusions: NNCC program decreased mortality and brain injury , shortened the length of hospital stay and improved care of neonates with significant HIE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".