Effectiveness of robot-assisted virtual reality mirror therapy for upper limb motor dysfunction after stroke: study protocol for a single-center randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Upper limb motor dysfunction is a common sequela of stroke, and its clinical efficacy needs to be improved. This protocol describes a trial to verify the clinical efficacy of robot-assisted virtual reality mirror therapy (RAVRMT) in improving upper limb motor dysfunction in stroke patients, and to explore the central mechanism by using functional magnetic resonance imaging (fMRI). METHODS: This trial will be a single-center, assessor-blinded, randomized controlled clinical study. Thirty-two eligible patients will be randomly divided into 2 groups according to the ratio of 1:1, namely virtual reality mirror therapy (VRMT) group and robot-assisted virtual reality mirror therapy (RAVRMT) group. The interventions will be performed once a day for 4 weeks. Primary outcome is Fugl-Meyer motor function assessment-Upper Extremity (FMA-UE), secondary outcomes are the Montreal Cognitive Assessment (MoCA), activities of daily living (ADL), quality of life (QOL), the pain visual analogue scale (VAS-pain) and fMRI. Adverse events will be recorded, and severe adverse events will be used as criteria to discontinue the intervention. DISCUSSION: Combined application of robot-assisted therapy and virtual reality mirror therapy could theoretically activate mirror neuron system and reward circuits to a greater extent, but further high-quality research is needed. The results of this trial will determine whether RAVRMT could better improve upper limb motor dysfunction after stroke and explore its central mechanism using fMRI. TRIAL REGISTRATION: This trial was prospectively registered at ClinicalTrials.gov (ChiCTR2200061721; 01 July 2022).
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".