Predictive Models of Neurodevelopmental Outcomes After Neonatal Hypoxic-Ischemic Encephalopathy
Bibliographic record
Abstract
OBJECTIVES: To develop predictive models for death or neurodevelopmental impairment (NDI) after neonatal hypoxic-ischemic encephalopathy (HIE) from data readily available at the time of NICU admission (“early”) or discharge (“cumulative”). METHODS: In this retrospective cohort analysis, we used data from the Children’s Hospitals Neonatal Consortium Database (2010–2016). Infants born at ≥35 weeks’ gestation and treated with therapeutic hypothermia for HIE at 11 participating sites were included; infants without Bayley Scales of Infant Development scores documented after 11 months of age were excluded. The primary outcome was death or NDI. Multivariable models were generated with 80% of the cohort; validation was performed in the remaining 20%. RESULTS: The primary outcome occurred in 242 of 486 infants; 180 died and 62 infants surviving to follow-up had NDI. HIE severity, epinephrine administration in the delivery room, and respiratory support and fraction of inspired oxygen of 0.21 at admission were significant in the early model. Severity of EEG findings was combined with HIE severity for the cumulative model, and additional significant variables included the use of steroids for blood pressure management and significant brain injury on MRI. Discovery models revealed areas under the curve of 0.852 for the early model and of 0.861 for the cumulative model, and both models performed well in the validation cohort (goodness-of-fit χ2: P = .24 and .06, respectively). CONCLUSIONS: Establishing reliable predictive models will enable clinicians to more accurately evaluate HIE severity and may allow for more targeted early therapies for those at highest risk of death or NDI.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".