Interplay between impaired brain structure and function in term newborns with congenital heart disease
Bibliographic record
Abstract
Background: Term neonates with congenital heart disease (CHD) demonstrate a high incidence of white matter injury (WMI), together with increased average diffusivity and decreased fractional anisotropy (FA) on MR diffusion tensor imaging. EEG background activity is a robust measure of functional brain maturation that becomes less discontinuous, contains more fast activity and shows higher complexity of EEG patterns with increasing age. We sought to determine the association between structural brain abnormalities and functional brain maturation in term neonates with CHD. Methods: Thirteen term newborns with CHD underwent pre-operative MR imaging and continuous EEG recordings (cEEG). The proportion of cEEG with discontinuous vs. continuous background activity was quantified by visual analysis. During continuous epochs, we differentiated between two states: wakefulness/active sleep vs. quiet/transitional sleep, and applied algorithms to measure spectral power and EEG complexity. Results: Three patients had multifocal WMI which was associated with greater EEG background discontinuity (P<0.05). Moreover, lower white matter diffusivity was associated with higher power of fast activity (P<0.05 for wakefulness/active sleep EEG pattern), while higher white matter FA showed a trend toward being associated with increased EEG complexity (P<0.1 for quiet/transitional sleep pattern). Conclusions: In this series of term neonates with CHD, structural and microstructural white matter abnormalities are associated with impaired maturation of brain function.
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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 | 0.000 | 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".