Evaluating the Impact of Virtual Reality on Cognitive Recovery in Stroke Patients: A Comparative Single-centered Study in the Indian Context
Bibliographic record
Abstract
Abstract Objective: Strokes frequently result in post-stroke cognitive impairment; however, this can take many different clinical forms. Deficits exist in a number of cognitive domains, including language, executive function, memory, and visuospatial abilities. This study explores the feasibility and scalability of virtual reality (VR)-based rehabilitation interventions within the Indian healthcare setting, considering factors such as accessibility, cost-effectiveness, and cultural appropriateness. Materials and Methods: This study employed a randomized controlled trial design to compare the impact of VR integration with conventional therapy on cognitive recovery in stroke patients. The study was conducted at tertiary care rehabilitation centers in the eastern region of India. Thirty patients were enrolled, with 15 patients allocated to each group. Patients were assessed at 1-month and 3-month follow-up visits following the interventions, during which their Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), memory span (MSPAN), SCWT, and FTT scores were recorded. Results: The results indicate that the VR group exhibited significantly greater improvements in MMSE and MOCA scores compared to the conventional therapy group. In addition, the VR group demonstrated a smaller decline in MSPAN scores. However, there were no significant differences between the groups in terms of changes in FTT and SCWT scores. These findings underscore the specific cognitive domains in which stroke patients undergoing VR rehabilitation may experience more significant improvements compared to those undergoing conventional therapy. Conclusion: The findings from this study contribute to the growing body of evidence supporting the integration of VR technology in stroke rehabilitation programs, offering new avenues for improving cognitive outcomes and enhancing the quality of life for individuals recovering from stroke-related cognitive impairments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".