Rehabilitation of Reaching and Grasping Function in Severe Hemiplegic Patients Using Functional Electrical Stimulation Therapy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to establish the efficacy of a therapeutic intervention based on functional electrical stimulation (FES) therapy to improve reaching and grasping function after severe hemiplegia due to stroke. METHODS: A total of 21 subjects with acute stroke were randomized into 2 groups, FES plus conventional occupational and physiotherapy (FES group) or only conventional therapy (control group) 5 days a week for 12 to 16 weeks. A third group of 7 subjects with chronic hemiplegia (at least 5 months poststroke) received only FES therapy (chronic group) and pre-post training changes were compared. FES was applied to proximal and then distal muscle groups during specific motor tasks. At baseline and at the end of treatment, grasping function was assessed using the Rehabilitation Engineering Laboratory Hand Function Test, along with more standard measures of rehabilitation outcome. RESULTS: The FES group improved significantly more than the control group in terms of object manipulation, palmar grip torque, pinch grip pulling force, Barthel Index, Upper Extremity Fugl-Meyer scores, and Upper Extremity Chedoke-McMaster Stages of Motor Recovery. The chronic stroke subjects demonstrated improvements in most categories, but the changes were not statistically significant. CONCLUSIONS: FES therapy with upper extremity training may be an efficacious intervention in the rehabilitation of reaching and grasping function during acute stroke rehabilitation.
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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) | 0.001 | 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) | 0.001 | 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".