Examining brain white matter after pediatric mild traumatic brain injury using neurite orientation dispersion and density imaging: An A-CAP study
Bibliographic record
Abstract
BACKGROUND: Pediatric mild traumatic brain injury (mTBI) affects millions of children annually. Diffusion tensor imaging (DTI) is sensitive to axonal injuries and white matter microstructure and has been used to characterize the brain changes associated with mild traumatic brain injury (mTBI). Neurite orientation dispersion and density imaging (NODDI) is a diffusion model that can provide additional insight beyond traditional DTI metrics, but has not been examined in pediatric mTBI. The goal of this study was to employ DTI and NODDI to gain added insight into white matter alterations in children with mTBI compared to children with mild orthopedic injury (OI). METHODS: Children (mTBI n = 320, OI n = 176) aged 8-16.99 years (12.39 ± 2.32 years) were recruited from emergency departments at five hospitals across Canada and underwent 3 T MRI on average 11 days post-injury. DTI and NODDI metrics were calculated for seven major white matter tracts and compared between groups using univariate analysis of covariance controlling for age, sex, and scanner type. False discovery rate (FDR) was used to correct for multiple comparisons. RESULTS: Univariate analysis revealed no significant group main effects or interactions in DTI or NODDI metrics. Fractional anisotropy and neurite density index in all tracts exhibited a significant positive association with age and mean diffusivity in all tracts exhibited a significant negative association with age in the whole sample. CONCLUSIONS: Overall, there were no significant differences between mTBI and OI groups in brain white matter microstructure from either DTI or NODDI in the seven tracts. This indicates that mTBI is associated with relatively minor white matter differences, if any, at the post-acute stage. Brain differences may evolve at later stages of injury, so longitudinal studies with long-term follow-up are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".