Use of a Portable Assistive Glove to Facilitate Rehabilitation in Stroke Survivors With Severe Hand Impairment
Bibliographic record
Abstract
Treatment options for stroke survivors with severe hand impairment are limited. Active task practice can be restricted by difficulty in voluntarily activating finger muscles and interference from involuntary muscle excitation. We developed a portable, actuated glove-orthosis, which could be employed to address both issues. We hypothesized that combining passive cyclical stretching (reducing motoneuronal hyperexcitability) imposed by the device with active-assisted, task-oriented training (rehabilitating muscle activation) would improve upper extremity motor control and task performance post-stroke. Thirteen participants who experienced a stroke 2-6 months prior to enrollment completed 15 treatment sessions over five weeks. Each session involved cyclically stretching the long finger flexors (30 min) followed by active-assisted task-oriented movement practice (60 min). Outcome measures were completed at six intervals: three before and three after treatment initiation. Overall improvement in post-training scores was observed across all outcome measures, including the Graded Wolf Motor Function Test, Action Research Arm Test, and grip and pinch strength (p ≤ 0.02), except finger extension force. No significant change in spasticity was observed. Improvement in upper extremity capabilities is achievable for stroke survivors even with severe hand impairment through a novel intervention combining passive cyclical stretching and active-assisted task practice, a paradigm which could be readily incorporated into the clinic.
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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 | 0.001 |
| 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) | 0.002 | 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".