Effect of Intensive Functional Electrical Stimulation Therapy on Upper-Limb Motor Recovery after Stroke: Case Study of a Patient with Chronic Stroke
Bibliographic record
Abstract
PURPOSE: Motivated by a prior successful randomized controlled trial showing that functional electrical stimulation (FES) therapy can restore voluntary arm and hand function in people with severe stroke, this study was designed to examine neuromuscular changes in the upper limb following intensive FES therapy, consisting of task-specific upper-limb movements with a combination of preprogrammed FES and manual assisted motion. METHODS: The patient was a 22-year-old woman who had suffered a haemorrhagic stroke 2 years earlier. FES therapy was administered for 1 hour twice daily for 12 weeks, for a total of 108 treatment sessions. RESULTS: While maximal voluntary contraction level of the upper-limb muscles did not show significant improvement, the ability to initiate and stop the muscle contraction voluntarily was regained in several upper-limb muscles (approx. 5%-15% of the maximum voluntary contraction of the same muscle in the less-affected arm). A reduction in arm spasticity was also observed, as indicated by the reduction of H-reflex in the wrist flexor muscle (82.1% to 45.0% in Hmax/Mmax) and decreased Modified Ashworth Scale scores (from 3 to 2 for the hand and 4 to 3 for the arm). Coordination between shoulder and elbow joints during the circle-drawing test improved considerably over the course of FES therapy: the patient was unable to draw a circle at all at baseline but was able to do so proficiently at discharge. CONCLUSION: Improvements in upper-limb function observed in people with severe stroke following intensive FES therapy can be attributed to (a) regained ability to voluntarily contract muscles of the affected arm, (b) reduced spasticity and improved muscle tone in the same muscles, and (c) increased range of motion of all joints.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".