Prediction of Outcome in Neonates with Hypoxic-Ischemic Encephalopathy II: Role of Amplitude-Integrated Electroencephalography and Cerebral Oxygen Saturation Measured by Near-Infrared Spectroscopy
Bibliographic record
Abstract
BACKGROUND: Few data have been published on the combined use of amplitude-integrated electroencephalography (aEEG) and near-infrared spectroscopy (NIRS) for outcome prediction in neonates cooled for hypoxic-ischemic encephalopathy (HIE). OBJECTIVE: Our aim was to evaluate the predictive values and the most powerful predictive combinations of single aEEG and NIRS parameters and the respective cut-off values with regard to short-term outcomes in HIE II. METHODS: aEEG and NIRS were prospectively studied at the Medical University of Vienna in the first 102 h of life with regard to magnetic resonance imaging (MRI). Thirty-two neonates diagnosed with HIE II treated with hypothermia were investigated. The measurement period was divided into 6-h epochs. According to MRI, 2 outcome groups were defined and predictive values of aEEG parameters, regional cerebral oxygen saturation (rScO2), and the additional value of both methods combined were studied. Receiver operating curves (ROC) were obtained and area under the curve (AUC) values were calculated. ROC were then used to detect the optimal cut-off points, sensitivity, specificity, positive predictive values, and negative predictive values. RESULTS: At all time epochs, combined parameter scores were more predictive than single parameter scores. The highest AUC were observed between 18 and 60 h of cooling for the aEEG summation score (0.72-0.84) and for (background pattern + seizures) × rScO2 (0.79-0.85). At 42-60 h sensitivity was similar between those 2 scores (87.5-90.0%), but the addition of NIRS to aEEG led to an increase in specificity (from 52.4-59.1% to 72.7-90.5%). CONCLUSIONS: In HIE II, aEEG and NIRS are important predictors of short-term outcome. The combination of both methods improves prognostication. The highest predictive abilities were observed between 18 and 60 h of cooling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".