Prevalence and Spectrum of In Utero Structural Brain Abnormalities in Fetuses with Complex Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Brain injury is a major complication in neonates with complex congenital heart disease. Preliminary evidence suggests that fetuses with congenital heart disease are at greater risk for brain abnormalities. However, the nature and frequency of these brain abnormalities detected by conventional fetal MR imaging has not been examined prospectively. Our primary objective was to determine the prevalence and spectrum of brain abnormalities detected on conventional clinical MR imaging in fetuses with complex congenital heart disease and, second, to compare the congenital heart disease cohort with a control group of fetuses from healthy pregnancies. MATERIALS AND METHODS: We prospectively recruited pregnant women with a confirmed fetal congenital heart disease diagnosis and healthy volunteers with normal fetal echocardiogram findings who underwent a fetal MR imaging between 18 and 39 weeks gestational age. RESULTS: A total of 338 fetuses (194 controls; 144 with congenital heart disease) were studied at a mean gestational age of 30.61 ± 4.67 weeks. Brain abnormalities were present in 23% of the congenital heart disease group compared with 1.5% in the control group (P < .001). The most common abnormalities in the congenital heart disease group were mild unilateral ventriculomegaly in 12/33 (36.4%) and increased extra-axial spaces in 10/33 (30.3%). Subgroup analyses comparing the type and frequency of brain abnormalities based on cardiac physiology did not reveal significant associations, suggesting that the brain abnormalities were not limited to those with the most severe congenital heart disease. CONCLUSIONS: This is the first large prospective study reporting conventional MR imaging findings in fetuses with congenital heart disease. Our results suggest that brain abnormalities are prevalent but relatively mild antenatally in fetuses with congenital heart disease. The long-term predictive value of these findings awaits further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".