Hippocampus, amygdala and global brain changes 10 years after childhood traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) in children results in damage to the developing brain, particularly in severely injured individuals. Little is known, however, of the long-term structural aspects of the brain following childhood TBI. This study investigated the integrity of the brain 10 years post-TBI using magnetic resonance imaging volumetrics in a sample of 49 participants with mild, moderate and severe TBI, evaluated against a normative sample of 20 individuals from a pediatric database with comparable age and gender distribution. Structural integrity was investigated in gray and white matter, and by manually segmenting two regions of interest (hippocampus, amygdala), potentially vulnerable to the effects of childhood TBI. The results indicate that more severe injuries caused a reduction in gray and white brain matter, while all TBI severity levels resulted in increased volumes of cerebrospinal fluid and smaller hippocampal volumes. In addition, enlarged amygdala volumes were detected in severely injured patients compared to their mild and moderate counterparts, suggesting that childhood TBI may disrupt the development of certain brain regions through diffuse pathological changes. The findings highlight the lasting impact of childhood TBI on the brain and the importance of monitoring brain structure in the long-term after early injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".