Cognitive Outcomes in Children with Mild Traumatic Brain Injury: An Examination Using the National Institutes of Health Toolbox Cognition Battery
Bibliographic record
Abstract
This study is the first to examine cognitive outcomes after pediatric mild TBI using the National Institutes of Health Toolbox Cognition Battery (NIHTB-CB), a computerized cognitive test battery. The NIHTB-CB includes two complex measures of attention and executive function that allow differentiation of accuracy and response speed. We compared performance on the NIHTB-CB among children 8–16 years of age with mild TBI ( n = 143) versus children with orthopedic injuries (OIs; n = 74) recruited in emergency departments and followed for 6 months post-injury. Mixed-model analyses showed that the mild TBI group showed significantly lower Fluid Cognition composite scores than the OI group at 10 days (group intercept, p = 0.018); the magnitude of group differences declined modestly over time (group × time interaction, p = 0.055). Effect sizes were d = 0.34 at 10 days post-injury, d = 0.27 at 3 months, and d = 0.10 at 6 months. No significant effects of group or time were found for the Crystallized Cognition composite. Analyses of Fluid Cognition subtests indicated that children with mild TBI displayed deficits for as long as 3 months on measures of attention and executive function (e.g., cognitive flexibility, inhibitory control), but not on measures of explicit memory, working memory, or processing speed. The poorer performance of the mild TBI group on measures of attention and executive function was attributable largely to slowed reaction time, not decreased accuracy. The findings suggest that children with mild TBI demonstrate persistent deficits in fluid cognition that are most apparent on tasks that combine demands for both speed and executive function.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".