VR-based cognitive rehabilitation for children with traumatic brain injuries: Feasibility and safety.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Traumatic brain injury (TBI) is a leading cause of acquired disability in children, who are at risk of significant impairment in executive function (EF). Virtual reality technology provides a novel strategy to offer rich and immersive training content that is both appealing to children and of potential value in improving their daily functioning. The present study aimed to evaluate the feasibility and safety of implementing an innovative VR-based interactive cognitive training (VICT) system for EF rehabilitation designed to meet the developmental and clinical needs of children with TBI. RESEARCH METHOD/DESIGN: A parallel-group random-block randomized controlled trial was conducted among 26 children 7-17 years with TBI, who completed baseline, postintervention, and 2-month follow-up visits. Feasibility was assessed for recruiting children, measuring outcomes, and implementing the intervention. VR satisfaction was assessed via 5-point Likert scales. Safety outcomes included simulator sickness (0-4) and physical exertion (6-20). Preliminary efficacy was assessed by NIH Toolbox Cognitive Battery tasks. RESULTS: = .19). Preliminary evidence supported potential efficacy of the intervention, particularly for moderate and severe TBIs. CONCLUSION/IMPLICATIONS: The present study found high feasibility, safety, and preliminary efficacy of the VICT system. Further research is required to fully examine the intervention's efficacy as a possible rehabilitation tool for children with TBI. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".