Is Computerized Cognitive Testing Useful in Children and Adolescents with Moderate-to-Severe Traumatic Brain Injury?
Bibliographic record
Abstract
OBJECTIVES: Children and adolescents with moderate-to-severe traumatic brain injury (TBI) present with short and long-term neuropsychological deficits following their injury. The aim of this study was to investigate the utility of a brief computerized test battery for evaluating cognitive functioning sub-acutely following a TBI. METHODS: Participants (n=33) sustained a moderate-to-severe TBI, were between 8 and 18 years old, and were assessed using CNS Vital Signs (CNSVS) within 6 months post-injury (median=0.6 month). Participants with TBI were matched to 33 healthy controls based on age, sex, and handedness to compare their cognitive functioning on the CNSVS battery. RESULTS: Children and adolescents with moderate-to-severe TBI had significantly lower scores and large effect sizes on Reaction Time, Complex Attention, and Cognitive Flexibility domains, as well as medium effect sizes on two Visual Memory test scores and one Psychomotor Speed test score. A significantly higher percentage of participants with TBI had cognitive impairment on Reaction Time domain score compared to the control group. Finally, CNSVS domain scores correctly categorized 76% of participants as either group with TBI or control group. CONCLUSIONS: CNSVS may be a useful tool for screening cognitive abilities in children and adolescents who are early in their recovery from a moderate-to-severe TBI, particularly when a rapid screening evaluation can help guide management, interventions, and track recovery. (JINS, 2017, 23, 304-313).
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".