Abstract 11798: Comparison of Echocardiographic Measurements and Cardiac Biomarkers Between Neonates With Neonatal Encephalopathy Who Developed Brain Injury or Not During Therapeutic Hypothermia
Bibliographic record
Abstract
Background: Despite therapeutic hypothermia (TH), many newborns with neonatal encephalopathy (NE) develop brain injury (BI). We hypothesis that newborns with NE have with brain injury have different echocardiographic (ECHO) parameters to those with no brain injury. Aims: To compare the ECHO parameters on day of life (DOL) 2 between neonates with NE undergoing TH developing or not brain injury. Methods: Prospective cohort study in a single tertiary unit enrolling neonate (born ≥36weeks and ≥1800 grams) undergoing TH for NE, between 2016 and 2020. All neonates presented on admission with moderate to severe NE by amplitude-integrated electroencephalogram (aEEG) profile and underwent a brain magnetic resonance (MRI) on DOL 2. ECHO was performed on the MRI day and data were extracted offline. Multiple logistic regression analyses were constructed to determine which parameters best predicted brain injury or not. Results: A total of 34 neonates were recruited, of which 19 (56%) had brain injury. Those with BI had a higher gestational age (GA) at birth (39.6±1.6 vs 38.2±1.5, p=0.01) and more severe pattern on aEEG (p=0.007) (Table 1). Mortality was also more frequent. Left and right ventricular performance were similar between groups by standard ECHO measures, strain and tissue Doppler imaging (Table 2). Although TAPSE was increased in those with brain injury (p=0.04), the Z-Score accounting for GA was similar. Initial aEEG pattern was the best predictor of brain injury, when accounting for GA and ECHO parameters/biomarkers. Conclusion: Initial aEEG pattern and not hemodynamic profile was most predictive of brain injury on MRI.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".