Exploring the link among injury severity, white matter connectivity and psychosocial outcomes in pediatric TBI: a probabilistic tractography approach
Bibliographic record
Abstract
Aim We examined associations among injury severity, white matter structural connectivity within functionally defined brain networks and psychosocial/adaptive outcomes in children with traumatic brain injury (TBI).Method Participants included 58 youths (39 male) with complicated-mild TBI (cmTBI; n = 12, age = 12.6 ± 2.0), moderate/severe TBI (msTBI; n = 16, age = 11.4 ± 2.9) and a comparison group with orthopedic injury (OI; n = 24, age = 11.7 ± 2.1), at least 1 year post-injury. Participants underwent diffusion tensor imaging and parents rated children’s behavioral and adaptive function on the CBCL and ABAS-3, respectively. Probabilistic tractography quantified streamline density. Group differences were analyzed for structural connectivity and behavioral outcomes.Results Groups differed in structural connectivity within regions of the default mode and central executive networks (ps < .05, FDR corrected). The msTBI group displayed decreased connectivity relative to cmTBI and OI, whereas the cmTBI group displayed increased connectivity relative to msTBI and OI. Similar patterns emerged in several behavioral domains. Ordinary least squares path analyses showed that structural connectivity mediated the relationship between injury severity and multiple parent-reported outcomes for msTBI.Interpretation White matter structural connectivity may explain unique variance in long-term psychosocial and adaptive outcome in children with TBI, particularly in cases of moderate-to-severe 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".