Altered white matter connectivity in children with congenital heart disease with single ventricle physiology
Bibliographic record
Abstract
Children born with congenital heart disease (CHD) have seen a dramatic decrease in mortality thanks to surgical innovations. However, there are numerous risk factors associated with CHD that can disrupt neurodevelopment. Recent studies have found that psychological deficits and structural brain abnormalities persist into adulthood. The goal of the current study was to investigate white matter connectivity in early school-age children (6-11 years), born with complex cyanotic CHD (single ventricle physiology), who have undergone Fontan palliation, compared to a group of heart-healthy, typically developing controls (TPC). Additionally, we investigated associations between white matter tract connectivity and measures on a comprehensive neuropsychological battery within each group. Our results suggest CHD patients exhibit widespread decreases in white matter connectivity, and the extent of these decreases is related to performance in several cognitive domains. Analysis of network topology showed that hub distribution was more extensive and bilateral in the TPC group. Our results are consistent with previous studies suggesting perinatal ischemia leads to white matter lesions and delayed maturation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".