Functional electrical stimulation as a tool to restore motor control after spinal cord injury: translation to clinical practice
Bibliographic record
Abstract
INTRODUCTION: Functional electrical stimulation (FES) is a therapeutic tool that may augment motor recovery after spinal cord injury/disease (SCI). It involves applying an electrical current to muscles and/or peripheral nerves to facilitate functional movements, such as walking and reaching. Despite the potential therapeutic benefits of FES, and the considerable investment into its research and development, FES is not widely used in clinical practice. AREAS COVERED: In this narrative review, we examine this research-to-practice gap. PubMed and Google Scholar were searched using keywords related to the population, constructs and context of interest. We provide an orientation to SCI and summarize how FES may facilitate motor recovery. Using the Knowledge-to-Action Framework as a guide, we demonstrate how co-design and implementation strategies can be incorporated into FES device development and research to aid clinical translation in SCI rehabilitation. Based on prior literature, we provide recommendations for researchers and technology developers: 1) collaborate with implementation scientists, 2) adopt participatory methods, 3) use a knowledge translation framework as a guide, 4) thoroughly understand implementation barriers and facilitators, and 5) budget time for implementation. EXPERT OPINION: Greater focus on clinical implementation is needed in the FES research field to address the current research-to-practice gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".