Profiles of Executive Function Across Children with Distinct Brain Disorders: Traumatic Brain Injury, Stroke, and Brain Tumor
Bibliographic record
Abstract
OBJECTIVES: This study examined whether children with distinct brain disorders show different profiles of strengths and weaknesses in executive functions, and differ from children without brain disorder. METHODS: Participants were children with traumatic brain injury (N=82; 8-13 years of age), arterial ischemic stroke (N=36; 6-16 years of age), and brain tumor (N=74; 9-18 years of age), each with a corresponding matched comparison group consisting of children with orthopedic injury (N=61), asthma (N=15), and classmates without medical illness (N=68), respectively. Shifting, inhibition, and working memory were assessed, respectively, using three Test of Everyday Attention: Children's Version (TEA-Ch) subtests: Creature Counting, Walk-Don't-Walk, and Code Transmission. Comparison groups did not differ in TEA-Ch performance and were merged into a single control group. Profile analysis was used to examine group differences in TEA-Ch subtest scaled scores after controlling for maternal education and age. RESULTS: As a whole, children with brain disorder performed more poorly than controls on measures of executive function. Relative to controls, the three brain injury groups showed significantly different profiles of executive functions. Importantly, post hoc tests revealed that performance on TEA-Ch subtests differed among the brain disorder groups. CONCLUSIONS: Results suggest that different childhood brain disorders result in distinct patterns of executive function deficits that differ from children without brain disorder. Implications for clinical practice and future research are discussed. (JINS, 2017, 23, 529-538).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".