Hand Rehabilitation Following Stroke: A Pilot Study of Assisted Finger Extension Training in a Virtual Environment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this pilot study was to investigate the impact of assisted motor training in a virtual environment on hand function in stroke survivors. PARTICIPANTS: Fifteen volunteer stroke survivors (32-88 years old) with chronic upper extremity hemiparesis (1-38 years post incident) took part. METHOD: Participants had 6 weeks of training in reach-to-grasp of virtual and actual objects. They were randomized to one of three groups: assistance of digit extension provided by a novel cable orthosis, assistance provided by a novel pneumatic orthosis, or no assistance provided. Hand performance was evaluated at baseline, immediately following training, and 1 month after completion of training. Clinical assessments included the Wolf Motor Function Test (WMFT), Box and Blocks Test (BB), Upper Extremity Fugl-Meyer Test (FM), and Rancho Los Amigos Functional Test of the Hemiparetic Upper Extremity (RLA). Biomechanical assessments included grip strength, extension range of motion and velocity, spasticity, and isometric strength. RESULTS: Participants demonstrated a significant decrease in time to perform functional tasks for the WMFT (p = .02), an increase in the number of blocks successfully grasped and released during the BB (p = .09), and an increase for the FM score (p = .08). There were no statistically significant changes in time to complete tasks on the RLA or any of the biomechanical measures. Assistance of extension did not have a significant effect. DISCUSSION AND CONCLUSION: After the training period, participants in all 3 groups demonstrated a decrease in time to perform some of the functional tasks. Although the overall gains were slight, the general acceptance of the novel rehabilitation tools by a population with substantial impairment suggests that a larger randomized controlled trial, potentially in a subacute population, may be warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".