Neuromuscular electrical stimulation (NMES) on the tibialis anterior muscle and the effects on strength and gait mechanics on stroke patients: A systematic review.
Bibliographic record
Abstract
Purpose: To establish the effects of neuromuscular electrical stimulation (NMES) on the tibialis anterior muscle (TA) on chronic stroke patients in order to improve gait mechanics.\nSubjects: 6\nMaterials & Methods: A systematic search of Pubmed, PEDro, CINAHL, and Cochrane Library was conducted to identify all pertinent randomized control trials (RCTs). RCTs that met the inclusion criteria were then assessed using the 11-point PEDro scale. Studies were assessed by two raters, and articles that scored 6 or above were accepted for review.\nResults: The initial search yielded 34 articles: 21 were pulled for data extraction, six met selected inclusion and exclusion criteria. PEDro scores for selected articles range from 6 to 9, with a mean score of 6.8.\nConclusions: Studies varied widely in parameters of application and prescription of NMES and baseline characteristics of subjects. Research supports the use of NMES to treat impairments of the TA following stroke, such as ankle range of motion and TA strength, as well as functional parameters, such as Fugl-Meyer Assessment scores, obstacle avoidance, and modified Emory Functional Ambulation Profile. However, these results were equivalent to outcomes using ankle-foot orthoses (AFO) or conventional rehabilitation program (CRP). In conclusion, NMES improved gait mechanics in patients with chronic stroke, but was not superior when compared with AFO or CRP.\nClinical Relevance: This systematic review demonstrates that NMES improves gait mechanics in chronic stroke patients, however, is no more effective than CRP or AFO interventions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".