Using telerehabilitation to improve cognitive function in post-stroke survivors: is this the time for the continuity of care?
Bibliographic record
Abstract
Post-stroke cognitive disorders can affect different domains, depending on typology of stroke and lesion localization, onset time, age and diagnostic tools used. In recent years, telerehabilitation using virtual reality has been used to reduce the healthcare costs encouraging continuity of care. The aim of our study is to evaluate the efficacy of a virtual reality rehabilitation system in improving cognitive function in stroke survivors. Forty patients affected by stroke were enrolled in this study and randomized into either the control or the experimental groups in order of recruitment. The study lasted 6 months, and included two phases: (1) during the first phase the experimental group underwent cognitive rehabilitation training using the Virtual Reality Rehabilitation System-Evo, whereas the control group was submitted to standard cognitive training; (2) in the second phase (after discharge), the experimental group was treated by means of virtual reality rehabilitation system Home Tablet (three sessions a week, each session lasting about 50 minutes), and the control group continued the traditional training, with the same amount of treatment. The patients underwent a neuropsychological evaluation before and at the end of the treatment. Linear mixed-effects analysis results showed that the scores of Montreal overall cognitive assessment, attentive matrices, Trail Making Test B, Phonemic Fluency, Semantic Fluency, Rey Auditory Verbal Learning Test I, Hamilton Rating Scale-Anxiety and Hamilton Rating Scale-Depression were affected by the type of the rehabilitative treatment. Our data show the effectiveness of telerehabilitation for the treatment of cognitive disorders following stroke.
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.
How this classification was reachedexpand
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".