Feasibility and preliminary efficacy of a combined virtual reality, robotics and electrical stimulation intervention in upper extremity stroke rehabilitation
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of individuals with chronic stroke present with long lasting upper extremity (UE) impairments. We designed the perSonalized UPper Extremity Rehabilitation (SUPER) intervention, which combines robotics, virtual reality activities, and neuromuscular electrical stimulation (NMES). The objectives of our study were to determine the feasibility and the preliminary efficacy of the SUPER intervention in individuals with moderate/severe stroke. METHODS: Stroke participants (n = 28) received a 4-week intervention (3 × per week), tailored to their functional level. The functional integrity of the corticospinal tract was assessed using the Predict Recovery Potential algorithm, involving measurements of motor evoked potentials and manual muscle testing. Those with low potential for hand recovery (shoulder group; n = 18) received a robotic-rehabilitation intervention focusing on elbow and shoulder movements only. Those with a good potential for hand recovery (hand group; n = 10) received EMG-triggered NMES, in addition to robot therapy. The primary outcomes were the Fugl-Meyer UE assessment and the ABILHAND assessment. Secondary outcomes included the Motor Activity Log and the Stroke Impact Scale. RESULTS: Eighteen participants (64%), in either the hand or the shoulder group, showed changes in the Fugl-Meyer UE or in the ABILHAND assessment superior to the minimal clinically important difference. CONCLUSIONS: This indicates that our personalized approach is feasible and may be beneficial in improving UE function in individuals with moderate to severe impairments due to stroke. TRIAL REGISTRATION: ClinicalTrials.gov NCT03903770. Registered 4 April 2019. Registered retrospectively.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".