The Impact of Rehabilitation-oriented Virtual Reality Device in Patients With Ischemic Stroke in the Early Subacute Recovery Phase: Study Protocol for a Phase III, Single-Blinded, Randomized, Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND AND RATIONALE: Stroke is considered the most common cause of adult disability. Intensive rehabilitation protocols outperform nonintensive counterparts. The subacute stroke phase represents a potential window to recovery. Virtual reality (VR) has been shown to provide a more stimulating environment, allowing for increased patient compliance. However, the quality of current literature comparing VR with standard therapies is limited. Our aim is to measure the impact of VR versus standard therapy on the recovery of the upper limb motor function in patients with stroke in the early subacute recovery phase. METHOD: This is a randomized, controlled trial that will assign 262 patients to tailor-made standard rehabilitation (TMSR) or TMSR plus immersive VR device. The trial will be conducted in an urban rehabilitation clinic in the United States with expertise in the management of poststroke patients. Patients will be 18 to 70 years of age and in the early subacute period (30-90 days post ischemic stroke). The primary outcome will be the change of Fugl-Meyer Assessment-Upper Extremity (FMA-UE) score, measured at baseline and 13 weeks after randomization. The secondary outcome will be the change in the UK Functional Independence Measure and Functional Assessment Measure (UK FIM-FAM) score at the same time points. DISCUSSION: If the use of VR in the rehabilitation of patients with stroke proves to have a significant impact on their motor recovery, it will constitute an extremely important step into decreasing the functional impairment associated with stroke and the related health care expense burden.
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 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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".