Effectiveness of Robot-Assisted Upper Extremity Function Training (Gloreha) on Upper Extremities Function After Stroke: Systematic Review
Bibliographic record
Abstract
Background: The Gloreha (Idrogenet SRL) is a robotic device that enhances conventional rehabilitation for improving upper extremity function after stroke, but comprehensive evidence on its effectiveness is still lacking. Objective: The objective of this study was to evaluate the effectiveness of the Gloreha device on upper extremity function and activities of daily living (ADLs) in patients with stroke. Methods: PubMed, Cochrane CENTRAL, CINAHL, Embase, and EBSCO Open Dissertations were searched from January 2013 to January 2024. The inclusion criteria were randomized controlled trials involving adult patients with stroke that compared rehabilitation with the Gloreha device to conventional rehabilitation and reported upper extremity function or ADLs outcomes. All included studies underwent bias risk assessment using the Revised Cochrane risk-of-bias tool for randomized trials. Results: Out of 1123 studies identified, 3 randomized controlled trials involving 83 participants were included. Of these, 2 trials combined Gloreha training with conventional rehabilitation, while in another trial, patients engaged solely in the training by the Gloreha device. The Gloreha, whether integrated with conventional rehabilitation or used independently, has the potential to enhance motor function and functional ability in survivors of stroke. Conclusions: Gloreha passive training with conventional rehabilitation improves upper extremity function post stroke, but ADL effects and long-term optimal dosing require further research.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".