Post-Acute Cortical Thickness in Children with Mild Traumatic Brain Injury versus Orthopedic Injury
Bibliographic record
Abstract
Studies of brain morphometry may illuminate the effects of pediatric mild traumatic brain injury (TBI; e.g., concussion). However, no published studies have examined cortical thickness in the early injury phases of pediatric mild TBI using an appropriate comparison group. The current study used an automated approach (i.e., FreeSurfer) to determine whether cortical thickness differed in children following a mild TBI or a mild orthopedic injury (OI), and to examine whether post-acute cortical thickness predicted post-acute and chronic post-concussive symptoms (PCS). Children ages 8.00-16.99 years with mild TBI ( n = 136) or OI ( n = 70) were recruited at emergency department visits to two children's hospitals, during which parents rated children's pre-injury symptoms retrospectively. Children completed a post-acute (3–24 days post-injury) assessment, which included a 3 Tesla MRI, and 3- and 6-month post-injury assessments. Parents and children rated PCS at each assessment. Cortical thickness was estimated using FreeSurfer. Linear mixed effects and multi-variable negative binomial regression models were used to test study aims, with false discovery rate (FDR) correction for multiple comparisons. Groups differed significantly on left parietal cortical thickness (TBI > OI) after FDR correction. Cortical thickness also varied by brain subregion and age, but not sex. Groups differed significantly on PCS post-acutely (TBI > OI), but not at 3 or 6 months. Right frontal thickness was positively related to post-acute PCS in both groups. Right cingulum thickness predicted chronic PCS in the OI group only. Results highlight the complexity of predicting outcomes of pediatric mild TBI from post-acute neuroimaging biomarkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".