The Effect of Long-Term Use of Virtual Reality Training on Cognitive Deficits in Post-Traumatic Brain Injury (TBI) Patients – Case Report
Bibliographic record
Abstract
Traumatic brain injury (TBI) can result in significant impairments in executive function, memory, and attention, adversely affecting daily functioning and rehabilitation outcomes.Cognitive rehabilitation after TBI is a challenging process often requiring training in daily living skills.While traditional rehabilitation methods benefit TBI patients, modern innovations like virtual reality (VR) offer promising potential for cognitive recovery.This study aimed to investigate the potential benefits of virtual reality-based therapy for improving cognitive function in individuals with traumatic brain injury.A single-case study was conducted involving a 23-year-old male with TBI, recruited from the neurosurgery rehabilitation ward of Saveetha Medical College and Hospitals.Pre-and post-test measurements were taken using the Digit Span Test and the Montreal Cognitive Assessment (MoCA).The patient underwent a 10-week rehabilitation protocol, including VR-based therapy, delivered six times a week for one hour per session.The MoCA score improved from 18 (pre-test) to 27 (post-test).The Digit Span forward test score increased from 4 to 7, and the Digit Span backward test score improved from 2 to 4. These findings indicate significant improvements in cognitive function following the VR-based intervention.This study highlights the potential of virtual reality as a valuable tool for enhancing cognitive recovery in individuals with traumatic brain injury.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".