VR-based cognitive rehabilitation for children with traumatic brain injuries: Feasibility and safety.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it