Examining Neuropsychological Outcomes and Neural Correlates of Children Diagnosed with Congenital Heart Disease and Children Born Very Preterm
Bibliographic record
Abstract
Congenital heart disease (CHD) and very preterm (VP) birth are two distinct and high-risk neonatal conditions that independently affect a significant number of newborns each year. Both conditions share common risks for altered brain development and long-term neuropsychological challenges. CHD is one of the most common congenital defects worldwide, affecting ~1 in 80-100 newborns in Canada each year, and approximately 11% of infants are born preterm. Despite medical advances that have improved survival rates in both populations, children remain vulnerable to a range of neurodevelopmental difficulties. Past research has shown that newborns with CHD and VP newborns without CHD have similar brain anomalies. To date, few studies have compared infants born with CHD to infants born VP and there is little research examining the association between neuropsychological outcomes and brain metabolites in these populations. This dissertation examined cross-sectional and long-term neurodevelopmental outcomes of infants with CHD and infants born VP, and examined associations with neurological, medical, and psychosocial characteristics. Using a large clinical sample, a series of 3 studies were conducted: 1) comparison of 18-month neurodevelopmental outcomes of children with CHD to children born VP; 2) examination of longitudinal neurodevelopmental outcome trajectories among these groups between 18-to-36 months of age; and 3) exploration of the relationship between neurometabolic concentrations of these groups and neurodevelopmental outcomes. This dissertation is one of the few to examine short and longer-term neurodevelopmental outcomes of children with CHD as compared to children born VP. Findings from the current dissertation may help inform clinical practice and direct surveillance and intervention initiatives to optimize neuropsychological outcomes.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".