Neurological and cardiovascular disease in infant: a systematic review
Bibliographic record
Abstract
Introduction. Children with congenital heart disease (CHD) are at increased risk for neurodevelopmental impairments, but the magnitude and nature of this association remain unclear. Objective. The present study aimed to evaluate the prevalence and types of neurological abnormalities in infants with CHD. Methods. A systematic search was conducted in international databases such as Web of Science, Psyc INFO, PubMed, Scopus, and Google Scholar for articles published up to September 2024. The review followed the PRISMA 2020 guidelines. Out of 48 initially identified articles, 5 met the inclusion criteria and were included in the final analysis. Studies were assessed using the Newcastle-Ottawa Scale. Results. A total of 570 infants who underwent neurological examination between 5 and 12 months of age were included across the five studies analyzed in this review. Overall, an estimated 72% of infants presented abnormalities in at least one neurological domain. The pooled effect size was 72%, with a 95% confidence interval (CI) ranging from 57% to 88%, indicating that the true prevalence of neurological abnormalities in this population is highly likely to fall within this range. The heterogeneity among studies was low (I² = 10.26%), suggesting consistency across the included data sources. Conclusion. Neurological abnormalities are common in infants with CHD, highlighting the importance of routine neurodevelopmental screening in this high-risk population. Early identification and intervention may help reduce long-term morbidity and improve developmental outcomes. This information may be important for clinical management, parental counseling, and the decision-making process regarding follow-up and therapy.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".