Efficacy of Virtual Reality Induced Environmental and Habitual Navigation on Psychological, Cognitive Function that Impacts on Physical Recovery in Patients with Stroke
Bibliographic record
Abstract
[Background] Cognitive dysfunction after a stroke is normal, but it is underdiagnosed and has a badprognosis. In 40-70 percent of stroke survivors, there is a degree of cognitive dysfunction. Similarly,psychometric issues such as anxiety and depression are normal following stroke but are mostly untreated,resulting in a patient’s poor quality of life. Whereas it also has an effect on a person’s rehabilitation. Theuse of traditional methodology has certain beneficial effects, but it is not necessarily handled with theindividual’s own interests in mind. Virtual reality, on the other hand, seems to play a role in dealing withsuch issues, especially where they are linked to neurological disorders. Virtual reality navigation has thepotential to enhance basic cognitive functions such as visuo-spatial perception, executive performance, andattention, both of which can affect one’s psychological state and aid in functional rehabilitation. Cognitivedeficits and social issues must be addressed because they have a detrimental impact on functional abilitiesand quality of life.[Methodology] Twenty-three participants between the ages of 40 and 60 with a stroke diagnosis were chosen.Participants were split into two groups: Group A, which received Virtual Reality induced environmental andhabitual navigation as well as Conventional Physiotherapy, and Group B, which received ConventionalPhysiotherapy as well as cognitive training and relaxation for 4 weeks of duration. The Montreal CognitiveAssessment (MoCA), Hamilton Anxiety Rating Scale (HARS), Hamilton Depression Rating Scale (HDRS),and Functional Independence Measure (FIM) were used to conduct pre and post intervention evaluations.[Conclusion] The study found that combining virtual reality-induced environmental and habitual navigationwith conventional physiotherapy improves cognitive control, psychological function, and functional recoveryin stroke patients more effectively than treating them with conventional physiotherapy alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".