Effect of biofeedback electrical stimulation combined with early intensive rehabilitation training on stroke rehabilitation
Bibliographic record
Abstract
OBJECTIVES: To investigate the efficacy of biofeedback electrical stimulation combined with early intensive rehabilitation training on cerebral blood circulation, neurological function recovery, motor performance, and self-care abilities in stroke patients. METHODS: A retrospective analysis was conducted on 120 stroke patients admitted to the Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University from September 2021 to October 2023. Patients were divided into an observation group (n=60) receiving the combined treatment and a control group (n=60) receiving standard rehabilitation. Efficacy was evaluated through the analysis of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) levels, as well as assessments of several clinical variables, including Peak Systolic Velocity (PSV), Mean Velocity (Vm), Resistance Index (RI), Fugl-Meyer Assessment (FMA), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Berg Balance Scale (BBS), National Institutes of Health Stroke Scale (NIHSS), Modified Barthel Index (MBI), and Stroke-Specific Quality of Life Scale (SS-QOL). Measurements were taken before treatment and upon completion. RESULTS: The findings revealed that the combination of electrical stimulation with early rehabilitation exercises significantly improved cerebral blood flow in stroke patients. This approach accelerated the recovery of neurological functions, enhanced motor skills, and improved self-care capabilities among participants. The results demonstrated substantial treatment benefits alongside a favorable safety profile. CONCLUSIONS: The integration of biofeedback electrical stimulation with intensive rehabilitation exercises significantly enhances neurological and motor function recovery in stroke patients while promoting better self-care skills, all within a safe treatment framework. This approach warrants further clinical research and potential implementation.
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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".