Arm Motor Recovery Using a Virtual Reality Intervention in Chronic Stroke
Bibliographic record
Abstract
INTRODUCTION: Despite interest in virtual environments (VEs) for poststroke arm motor rehabilitation, advantages over physical environment (PE) training have not been established. OBJECTIVE: The authors compared kinematic and clinical outcomes of dose-matched upper-limb training between a 3D VE and a PE in chronic stroke. METHODS: Participants (n = 32) were randomized to a 3D VE or PE for training. They pointed to 6 workspace targets (72 trials, 12 trials/target, randomized) for 12 sessions over 4 weeks with similar feedback on precision, movement speed, and trunk displacement. Primary (kinematics, clinical arm motor impairment) and secondary (activity level, arm use) outcomes were compared by time (PRE, POST, and follow-up, RET), training environment, and impairment severity (mild, moderate-to-severe) using mixed-model analyses of variance (ANOVAs). RESULTS: Endpoint speed, overall performance on a reach-to-grasp task, and activity levels increased in both groups. Only participants in the VE group improved shoulder horizontal adduction at POST (9.5°) and flexion at both POST (6.3°) and RET (13°). Impairment level affected outcomes. After VE training, the mild group increased elbow extension (RET, 25.5°). The moderate-to-severe group in VE increased arm use at POST (0.5 points) and reaching ability at RET (2.2 points). The moderate-to-severe group training in PE increased reaching ability earlier (POST, 1.7 points) and both elbow extension (10.7°) and arm use (0.4 points) at RET, but these changes were accompanied by increased compensatory trunk displacement (RET, 30.2 mm). CONCLUSION: VE training led to more changes in the mild group and a motor recovery pattern in the moderate-to-severe group indicative of less compensation, possibly because of a better use of feedback.
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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 | 0.000 | 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) | 0.003 | 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".