Virtual Reality–Based Executive Function Rehabilitation System for Children With Traumatic Brain Injury: Design and Usability Study
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) poses a significant threat to children's health. Cognitive rehabilitation for pediatric TBI has the potential to improve the quality of life following the injury. Virtual reality (VR) can provide enriched cognitive training in a life-like but safe environment. However, existing VR applications for pediatric TBIs have primarily focused on physical rehabilitation. OBJECTIVE: This study aims to design and develop an integrative hardware and software VR system to provide rehabilitation of executive functions (EF) for children with TBI, particularly in 3 core EF: inhibitory control, working memory, and cognitive flexibility. METHODS: The VR training system was developed by an interdisciplinary team with expertise in best practices of VR design, developmental psychology, and pediatric TBI rehabilitation. Pilot usability testing of this novel system was conducted among 10 healthy children and 4 children with TBIs. RESULTS: Our VR-based interactive cognitive training system was developed to provide assistive training on core EF following pediatric TBI. Pilot usability testing showed adequate user satisfaction ratings for both the hardware and software components of the VR system. CONCLUSIONS: This project designed and tested a novel VR-based system for executive function rehabilitation that is specifically adapted to children following 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".