Neuromuscular Electrical Stimulation for Treatment of Muscle Impairment: Critical Review and Recommendations for Clinical Practice
Bibliographic record
Abstract
Purpose: In response to requests from physiotherapists for guidance on optimal stimulation of muscle using neuromuscular electrical stimulation (NMES), a review, synthesis, and extraction of key data from the literature was undertaken by six Canadian physical therapy (PT) educators, clinicians, and researchers in the field of electrophysical agents. The objective was to identify commonly treated conditions for which there was a substantial body of literature from which to draw conclusions regarding the effectiveness of NMES. Included studies had to apply NMES with visible and tetanic muscle contractions. Method: Four electronic databases (CINAHL, Embase, PUBMED, and SCOPUS) were searched for relevant literature published between database inceptions until May 2015. Additional articles were identified from bibliographies of the systematic reviews and from personal collections. Results: The extracted data were synthesized using a consensus process among the authors to provide recommendations for optimal stimulation parameters and application techniques to address muscle impairments associated with the following conditions: stroke (upper or lower extremity; both acute and chronic), anterior cruciate ligament reconstruction, patellofemoral pain syndrome, knee osteoarthritis, and total knee arthroplasty as well as critical illness and advanced disease states. Summaries of key details from each study incorporated into the review were also developed. The final sections of the article outline the recommended terminology for describing practice using electrical currents and provide tips for safe and effective clinical practice using NMES. Conclusion: This article provides physiotherapists with a resource to enable evidence-informed, effective use of NMES for PT practice.
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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 | 0.073 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".