Robot-assisted upper extremity rehabilitation for cervical spinal cord injuries: a systematic scoping review
Bibliographic record
Abstract
Abstact Purpose: To provide an overview of the feasibility and outcomes of robotic-assisted upper extremity training for individuals with cervical spinal cord injury (SCI), and to identify gaps in current research and articulate future research directions. MATERIALS AND METHODS: A systematic search was conducted using Medline, Embase, PsycINFO, CCTR, CDSR, CINAHL and PubMed on June 7, 2017. Search terms included 3 themes: (1) robotics; (2) SCI; (3) upper extremity. Studies using robots for upper extremity rehabilitation among individuals with cervical SCI were included. Identified articles were independently reviewed by two researchers and compared to pre-specified criteria. Disagreements regarding article inclusion were resolved through discussion. The modified Downs and Black checklist was used to assess article quality. Participant characteristics, study and intervention details, training outcomes, robot features, study limitations and recommendations for future studies were abstracted from included articles. RESULTS: Twelve articles (one randomized clinical trial, six case series, five case studies) met the inclusion criteria. Five robots were exoskeletons and three were end-effectors. Sample sizes ranged from 1 to 17 subjects. Articles had variable quality, with quality scores ranging from 8 to 20. Studies had a low internal validity primarily from lack of blinding or a control group. Individuals with mild-moderate impairments showed the greatest improvements on body structure/function and performance-level measures. This review is limited by the small number of articles, low-sample sizes and the diversity of devices and their associated training protocols, and outcome measures. CONCLUSIONS: Preliminary evidence suggests robot-assisted interventions are safe, feasible and can reduce active assistance provided by therapists. Implications for rehabilitation Robot-assisted upper extremity training for individuals with cervical spinal cord injury is safe, feasible and can reduce hands-on assistance provided by therapists. Future research in robotics rehabilitation with individuals with spinal cord injury is needed to determine the optimal device and training protocol as well as effectiveness.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".