Effectiveness of Functional Electrical Stimulation Assisted Locomotor Training on walking Outcomes Following Incomplete Spinal Cord Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction Functional electrical stimulation (FES) may enhance the impact of locomotor training on walking impairments following spinal cord injury (SCI). Objective This systematic review (PROSPERO: CRD42023435210) evaluated the therapeutic effectiveness of FES-assisted locomotor training (FALT) on improving walking speed and endurance for individuals with motor incomplete SCI (iSCI). Methods Databases (MEDLINE, EMBASE, CINAHL) were searched for interventional studies of FALT in iSCI that assessed the therapeutic effects on walking speed and/or endurance when the FES was not active. Study characteristics and findings were extracted, summarized, and narratively synthesized. Risk of bias was assessed using the Cochrane tools for interventional studies. Random effects meta-analyses were conducted to generate standardized pooled effect sizes for both outcomes. Results Thirteen studies were identified: 4 randomized controlled trials (RCTs) and 9 pre-post tests. RCTs scored low ( n = 1 study), intermediate ( n = 1), and high ( n = 2) on the RoB2, and all pre-post tests studies ( n = 9) scored high on the ROBINS-I. Meta-analyses of 3 RCTs found that treadmill-based FALT was associated with a small, non-significant effect on walking speed ( n = 76 participants; Hedge’s g : −0.01; 95% CI: −0.46, 0.43; P = .96) and a small, non-significant effect on walking endurance ( n = 71; Hedge’s g : 0.20; 95% CI: −0.25, 0.65; P = .39) when compared to control conditions. Discussion This review did not find evidence that FALT improves walking speed or endurance for people with iSCI relative to other types of locomotor training. Future trials of FALT for SCI should aim to better standardize and report training dose and stimulation parameters to improve comparability.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".