Neurophysiological changes associated with dysglycemia in term neonates with neonatal encephalopathy
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between hypo/hyperglycemia and EEG measures in term neonates with neonatal encephalopathy (NE) treated with therapeutic hypothermia (TH). METHODS: Ninety-six neonates underwent concurrent continuous glucose monitoring (CGM) and EEG for 48 h within the first 72 h of life. Five-minute epochs of hypoglycemia (≤2.6 mmol/L; ≤47 mg/dL) and hyperglycemia (>10.1 mmol/L; >182 mg/dL) were identified. Continuous EEG data was segmented into 5-min epochs corresponding to 5-min CGM data. Visual EEG background score and 22 computational EEG measures were estimated and their relationship to dysglycemia was assessed after adjusting for hypoxia-ischemia severity and multiple testing. RESULTS: Among the 96 neonates, 12 had hypoglycemia, 16 had hyperglycemia, including 1 with both during the recording period. In the adjusted analyses, hyperglycemic epochs were associated with worse visual background scores (1.2, 95 %CI 0.51-1.89, q = 0.003) and 11 computational EEG measures. In contrast, hypoglycemic epochs were not associated with a significant change in visual background scores (-0.28, 95 %CI -0.66 - -0.10, q = 0.549), and were only associated with one computational EEG measure. CONCLUSIONS: Hyperglycemia is temporally associated with changes in brain function in term neonates with NE treated with TH. SIGNIFICANCE: This temporal relationship suggests high glucose levels may contribute directly to brain injury after NE.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".