Fetal brain issues in congenital heart disease
Bibliographic record
Abstract
Following the improvements in the clinical management of patients with congenital heart disease (CHD) and their increased survival, neurodevelopmental outcome has become an emerging priority in pediatric cardiology. Large-scale efforts have been made to protect the brain during the postnatal, surgical, and postoperative period; however, the presence of brain immaturity and injury at birth suggests in utero and peripartum disturbances. Over the past decade, there has been considerable interest and investigations on fetal brain growth in the setting of CHD. Advancements in fetal brain imaging have identified abnormal brain development in fetuses with CHD from the macrostructural (brain volumes and cortical folding) down to the microstructural (biochemistry and water diffusivity) scale, with more severe forms of CHD showing worse disturbances and brain abnormalities starting as early as the first trimester. Anomalies in common genetic developmental pathways and diminished cerebral substrate delivery secondary to altered cardiovascular physiology are the forefront hypotheses, but other factors such as impaired placental function and maternal psychological stress have surfaced as important contributors to fetal brain immaturity in CHD. The characterization and timing of fetal brain disturbances and their associated mechanisms are important steps for determining preventative prenatal interventions, which may provide a stronger foundation for the developing brain during childhood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".