Upper extremity intervention for stroke combining virtual reality, robotics and electrical stimulation
Bibliographic record
Abstract
Approximately 80% of individuals with chronic stroke present with long lasting upper extremity (UE) impairments. We propose 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 of the SUPER intervention in individuals with moderate/severe stroke. Stroke participants received a 4-week intervention (3x per week), based on their functional level. Their level of corticospinal tract recovery 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) received an intervention focusing on elbow and shoulder movements. Those with a good potential for hand recovery (hand group) also received EMG-triggered NMES. Outcomes included the Fugl-Meyer UE assessment, the Motor Activity Log and the Stroke Impact Scale. Approximately 40% of participants in either the hand or shoulder group showed changes in the Fugl-Meyer UE assessment superior to its minimum clinically important difference. This indicates that our personalized approach may be effective in improving UE function in specific individuals with moderate and severe impairments due to stroke.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".