Interaction between Preterm White Matter Injury and Childhood Thalamic Growth
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine how preterm white matter injury (WMI) and long-term thalamic growth interact to predict 8-year neurodevelopmental outcomes. METHODS: A prospective cohort of 114 children born at 24 to 32 weeks' gestational age (GA) underwent structural and diffusion tensor magnetic resonance imaging early in life (median 32 weeks), at term-equivalent age and at 8 years. Manual segmentation of neonatal WMI was performed on T1-weighted images and thalamic volumes were obtained using the MAGeT brain segmentation pipeline. Cognitive, motor, and visual-motor outcomes were evaluated at 8 years of age. Multivariable regression was used to examine the relationship among neonatal WMI volume, school-age thalamic volume, and neurodevelopmental outcomes. RESULTS: School-age thalamic volumes were predicted by neonatal thalamic growth rate, GA, sex, and neonatal WMI volume (p < 0.0001). After accounting for total cerebral volume, WMI volume remained associated with school-age thalamic volume (β = -0.31, p = 0.005). In thalamocortical tracts, fractional anisotropy (FA) at term-equivalent age interacted with early WMI volume to predict school-age thalamic volumes (all p < 0.02). School-age thalamic volumes and neonatal WMI interacted to predict full-scale IQ (p = 0.002) and adverse motor scores among those with significant WMI (p = 0.01). Visual-motor scores were predicted by thalamic volumes (p = 0.04). INTERPRETATION: In very preterm-born children, neonatal thalamic growth and WMI volume predict school-age thalamic volumes. The emergence at term of an interaction between FA and WMI to impact school-age thalamic volume indicates dysmaturation as a mechanism of thalamic growth failure. Cognition is predicted by the interaction of WMI and thalamic growth, highlighting the need to consider multiple dimensions of brain injury in these children. ANN NEUROL 2021;90:584-594.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".