Mapping the neuroanatomical impact of very preterm birth across childhood
Bibliographic record
Abstract
Those born very preterm (VPT; <32 weeks gestational age) have an increased risk in developing a wide range of cognitive deficits. In early-to-late childhood, brain structure has been shown to be altered in VPT compared to full-term (FT) children; however, the results are inconsistent. The current study examined subcortical volumes, cortical thickness, and surface area in a large cohort of VPT and FT children aged 4-12 years. Structural magnetic resonance imaging (MRI) was obtained on 120 VPT and 146 FT children who returned up to three times, resulting in 176 VPT and 173 FT unique data points. For each participant, Corticometric Iterative Vertex-based Estimation of Thickness was used to obtain global measurements of total brain, cortical grey and cortical white matter volumes, along with surface-based measurements of cortical thickness and surface area, and Multiple Automatically Generated Templates (MAGeT) brain segmentation tool was used to segment the subcortical structures. To examine group differences and group-age interactions, mixed-effects models were used (controlling for whole-brain volume). We found few differences between the two groups in subcortical volumes. The VPT children showed increased cortical thickness in frontal, occipital and fusiform gyri and inferior pre-post-central areas, while thinning occurred in the midcingulate. Cortical thickness in occipital regions showed more rapid decreases with age in the VPT compared to the FT children. VPT children also showed both regional increases, particularly in the temporal lobe, and decreases in surface area. Our results indicate a delayed maturational trajectory in those born VPT.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".