Evaluating cognitive performance using the National Institutes of Health Toolbox Cognitive Battery in children with traumatic brain injury
Bibliographic record
Abstract
Abstract Objective: We examined cognitive performance in children with complicated mild-severe traumatic brain injury (TBI) versus orthopedic injury (OI) using the National Institutes of Health Toolbox Cognitive Battery (NIH TB-CB). Method: We recruited children ages 3–18, hospitalized with complicated mild-severe TBI ( n = 231) or orthopedic injury (OI, n = 146). Cognition was assessed using the NIH TB-CB at six and twelve months post-injury. We used linear mixed models to assess associations of injury group (TBI versus OI), timepoint (six versus twelve months), and the interaction of injury group and timepoint with NIH TB-CB Total Cognition, Fluid Cognition, and Crystallized Cognition composites, adjusted for sex and socioeconomic status (SES), with Bonferroni correction. We evaluated differences in cognition stratified by injury severity (complicated mild–moderate TBI vs severe TBI) using ANCOVA, adjusting for sex and SES. Results: Neither injury group nor the interaction of group and timepoint were associated with Total (group: p = 0.50; timepoint*group: p = 0.185), Fluid (group: p = 0.297; timepoint*group: p = 0.842), or Crystallized Cognition (group: p = 0.039; timepoint*group: p = 0.017). However, children with severe TBI performed significantly worse on Fluid and Total Cognition than children with complicated mild–moderate TBI at six months (Fluid: p = 0.004, partial η 2 = 0.06, moderate effect, Total: p = 0.012 partial η 2 = 0.03, small–moderate effect) and twelve months post-injury (Fluid: p < 0.001, partial η 2 = 0.11, moderate–large effect, Total: p = 0.002, partial η 2 = 0.06, moderate effect). Conclusions: The NIH TB-CB detects worse cognitive functioning in children with severe TBI six-twelve months post-injury, largely driven by differences in Fluid Cognition. Our findings suggest the NIH TB-CB may be suitable for monitoring cognition in children with TBI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".