Postasphyxial Hypoxic-Ischemic Encephalopathy in Neonates
Bibliographic record
Abstract
OBJECTIVES: To construct and validate a model and derive a simple rule that is usable in any birth location for the prediction of outcome of term infants with severe asphyxia. DESIGN: Retrospective cohort study. SETTING: Regional outborn neonatal intensive care unit. PARTICIPANTS: Infants with postintrapartum asphyxial hypoxic-ischemic encephalopathy (n = 375). MAIN EXPOSURES: Clinical and laboratory predictors available at age 4 hours. MAIN OUTCOME MEASURES: A logistic regression model was developed and internally validated (with random sampling and based on the year of birth) for severe adverse outcome, which was defined as death or severe disability (severe cerebral palsy, severe developmental delay, sensorineural deafness, or cortical blindness singly or in combination). A simple prediction rule was derived from 3 variables. RESULTS: Complete data were available for 302 (92%) of the 345 infants with known outcomes (204 infants with severe adverse outcome). Six independent predictors of outcomes were identified. Using the 3 most significant predictors (chest compressions, age at onset of respiration, and base deficit), severe adverse outcome rates were 46% (95% confidence interval, 33%-58%) with none of the 3 predictors, 64% (95% confidence interval, 54%-73%) with any 1 predictor, 76% (95% confidence interval, 66%-85%) with any 2 predictors, and 93% (95% confidence interval, 81%-99%) with all of the 3 predictors present. The internal validations revealed a robust model. CONCLUSIONS: This predictive model for neonatal hypoxic-ischemic encephalopathy provides a sliding scale of probabilities that could be used for prognostication and to design eligibility criteria for decision making including neuroprotective therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".