Pilot study of EEG in neonates born to mothers with gestational diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: The goal was to evaluate whether there was neurodevelopmental deficits in newborns born to mothers with gestational diabetes mellitus (GDM) compared to control newborns born to healthy mothers. METHODS: Forty-six pregnant women (21 controls and 25 GDM) were recruited. Electroencephalogram (EEG) was recorded in the newborns within 48 h after birth. The EEG signal was quantitatively analyzed using power spectral density (PSD); coherence between hemispheres was calculated in paired channels of frontal, temporal, central and occipital regions. RESULTS: The left centro-occipital PSD in control newborns was 12% higher than in GDM newborns (p = 0.036) but was not significant after adjustment for gestational age. While coherence was higher in the frontal regions compared to the occipital regions (p < 0.001), there was no difference between the groups for the fronto-temporal, frontal-central, centro-occipital and tempo-occipital regions. CONCLUSION: Our results support that EEG differences between groups were mainly modified by gestational age and less by GDM status of the mothers. However, there is a need to confirm this result with a higher number of mother-newborns. Quantitative EEG in GDM newborns within 48 h after birth is feasible. This study emphasizes the importance of controlling blood glucose during GDM to protect infant brain development.
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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 | 0.001 |
| 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) | 0.001 | 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".