Impact of Congenital Heart Defects on the Developing Brain
Bibliographic record
Abstract
Objectives Congenital heart defects (CHD) are responsible for neurodevelopmental delays that were initially attributed to brain injury resulting from cardiac surgery. However, prenatal imaging have shown that brain anomalies are present at birth. The aim of this study was to assess in utero brain injuries before birth in fetuses/neonates with congenital cardiopathies. Methods A complete autopsy evaluation with detailed study of the cardiopathy and neuropathological study was performed in 40 fetuses/neonates. Syndromic congenital cardiopathies were excluded because of the potential other causes of brain injury. The patients were classified into two groups according to their term at death. Results Statistical analyses indicated the mean brain weight was not significantly different between subjects with different morphological types of congenital cardiopathies. However, the brain weight was at or below the fifth percentile in most third-trimester subjects compared to normal brain weight in second-trimester subjects. Low brain weight in third-trimester subjects was also associated with frequent lesions similar to those described in preterm infants, with a particular involvement of white matter and its components. Conclusions These observations allowed us to establish the timing and impact of prenatal neuropathological lesions on brain development, and to correlate them with imaging data reported in the literature.
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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 | 0.001 | 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".