Recovery of White Matter following Pediatric Traumatic Brain Injury Depends on Injury Severity
Bibliographic record
Abstract
Previous studies in pediatric traumatic brain injury (TBI) have been variable in describing the effects of injury severity on white-matter development. The present study used diffusion tensor imaging to investigate prospective sub-acute and longitudinal relationships between early clinical indicators of injury severity, diffusion metrics, and neuropsychological outcomes. Pediatric patients with TBI underwent magnetic resonance imaging (MRI) (n = 78, mean [M] = 10.56, standard deviation [SD] = 2.21 years) at the sub-acute stage after injury (M = 5.55, SD = 3.05 weeks), and typically developing children were also included and imaged (n = 30, M = 10.60, SD = 2.88 years). A sub-set of the patients with TBI (n = 15) was followed up with MRI 2 years post-injury. Diffusion MRI images were acquired at sub-acute and 2-year follow-up time points and analyzed using Tract-Based Spatial Statistics. At the sub-acute stage, mean diffusivity and axial diffusivity were significantly higher in the TBI group compared with matched controls (p < 0.05). TBI severity significantly predicted diffusion profiles at the sub-acute and 2-year post-injury MRI. Patients with more severe TBI also exhibited poorer information processing speed at 6-months post-injury, which in turn correlated with their diffusion metrics. These findings highlight that the severity of the injury not only has an impact on white-matter microstructure, it also impacts its recovery over time. Moreover, findings suggest that sub-acute microstructural changes may represent a useful prognostic marker to identify children at elevated risk for longer term deficits.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".