Microstructural properties of white matter in the corpus callosum in neonates and children born preterm and full-term: Quantitative meta-analyses
Bibliographic record
Abstract
Abstract Nearly 15 million infants are born preterm every year. Research shows that compared to their full-term peers, individuals born preterm are more likely to have lower academic scores and be less wealthy later in life. Some suggest that brain connectivity as measured by diffusion tensor imaging (DTI) may underly these results. Critically, these differences have not been substantiated quantitatively. We performed quantitative meta-analyses to evaluate overarching patterns from articles that reported DTI metrics from the corpus callosum (CC), the largest white matter structure in the brain, in neonates and children/adolescents who were born pre-term and full-term. The DTI literature was reviewed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two meta-analyses, considering publication bias and study heterogeneity, were performed onfractional anisotropy scores from the CC from eligible articles that scanned individuals at 39.9±1.1 weeks (i.e., neonates) and 14±4.13 years of age (i.e., children/adolescents). Meta-analyses for neonates and children/adolescents revealed no significant differences between those born preterm and full-term in the CC. Children/adolescents samples were characterized by significant publication bias and high interstudy heterogeneity, whereas the neonate samples were not. The findings challenge the current state of understanding of white matter microstructure in the CC related to preterm birth. Results highlight the need for further research to better understand the dynamics among factors that can influence neural development in preterm children.
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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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.052 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".