Electromyogram-Related Neuromuscular Electrical Stimulation for Restoring Wrist and Hand Movement in Poststroke Hemiplegia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Clinical trials have demonstrated some benefits of electromyogram-triggered/controlled neuromuscular electrical stimulation (EMG-NMES) on motor recovery of upper limb (UL) function in patients with stroke. However, EMG-NMES use in clinical practice is limited due to a lack of evidence supporting its effectiveness. OBJECTIVE: To perform a systematic review and meta-analysis to determine the effects of EMG-NMES on stroke UL recovery based on each of the International Classification of Functioning, Disability, and Health (ICF) domains. METHODS: Database searches identified clinical trials comparing the effect of EMG-NMES versus no treatment or another treatment on stroke upper extremity motor recovery. A meta-analysis was done for outcomes at each ICF domain (Body Structure and Function, Activity and Participation) at posttest (short-term) and follow-up periods. Subgroup analyses were conducted based on stroke chronicity (acute/subacute, chronic phases). Sensitivity analysis was done by removing studies rated as poor or fair quality (PEDro score <6). RESULTS: Twenty-six studies (782 patients) met the inclusion criteria. Fifty percent of them were considered to be of high quality. The meta-analysis showed that EMG-NMES has a robust short-term effect on improving UL motor impairment in the Body Structure and Function domain. No evidence was found in favor of EMG-NMES for the Activity and Participation domain. EMG-NMES had a stronger effect for each ICF domain in chronic (≥3 months) compared to acute/subacute phases. CONCLUSION: EMG-NMES is effective in the short term in improving UL impairment in individuals with chronic stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".