Use of client-centered virtual reality in rehabilitation after stroke: a feasibility study
Bibliographic record
Abstract
Patient-centered virtual reality (VR) programs could assist in the functional recovery of people after a stroke. OBJECTIVES To analyze the feasibility of a rehabilitation protocol using client-centered VR and to evaluate changes in occupational performance and social participation. METHODS This was a mixed methods study. Ten subacute and chronic stroke patients participated in the rehabilitation program using games in non-immersive VR for 40 minutes/day, three days/week, for 12 weeks. Sociodemographic information was collected and the outcome variables included were the Canadian Occupational Performance Measure (COPM) and the Participation Scale. A field diary was used to record the frequency of attendance and adherence of participants and an interview was conducted at the end of program. RESULTS There were significant and clinically-relevant statistical improvements in the COPM performance score (p < 0.001; CI = 1.29 - 4.858) and in the COPM satisfaction score (p < 0.001; CI = 1.37 - 5.124), with a difference greater than 4.28 points for performance and 4.58 points for satisfaction. The change in the scores for participation was statistically significant (p = 0.046), but there was no clinical improvement (dcohen = -0.596, CI = -1.862 - 0.671). The majority of participants reported more than 75% consecutive attendance of sessions and there was 100% adherence to the program. In the interviews, the participants described their post-stroke difficulties; how the video game motivated their engagement in rehabilitation; and the improvement of occupational performance and social participation after participating in the program. CONCLUSIONS VR is a viable tool for the rehabilitation of stroke patients with functional gains, mainly regarding occupational performance and performance satisfaction.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".