Electroencephalography and optical neuromonitoring predict short-term outcomes in neonates undergoing therapeutic hypothermia for hypoxic-ischemic encephalopathy
Bibliographic record
Abstract
Electroencephalography (EEG) and optical neuromonitoring were used to predict short-term outcomes in neonates undergoing therapeutic hypothermia (TH) for hypoxic-ischemic encephalopathy (HIE). Fifty-two neonates undergoing TH for HIE were prospectively recruited. Continuous EEG monitoring was initiated within 24 h of life and a quantitative discontinuity index was calculated. Combined frequency-domain near infrared spectroscopy (FDNIRS) and diffuse correlation spectroscopy (DCS) were initiated within 48 h of life and used to measure cerebral hemoglobin oxygen saturation (SO 2 ) and a cerebral blood flow index. Using these parameters and hemoglobin concentration measurements, cerebral oxygen extraction fraction (OEF), indices of cerebral oxygen delivery and metabolism (CMRO 2i ) as well as cerebral oxygen reserve (CRO 2 ) were derived. Short-term outcome was classified based on brain injury pattern on magnetic resonance imaging and/or death; as normal-mild, moderate or severe outcome. Results showed that EEG discontinuity index, SO 2 and CRO 2 were higher and OEF lower in neonates with severe compared to normal-mild and moderate outcomes during TH. EEG discontinuity index was the most accurate and earliest parameter to identify moderate vs. severe outcomes while CMRO 2i identified normal-mild vs. moderate outcomes as early as day 2 of TH. Combining EEG and FDNIRS-DCS parameters improved area-under-the-curve, sensitivity and specificity for most of the predictive models.
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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 | 0.000 |
| Research integrity | 0.001 | 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".