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 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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".