ReviewJournal of neuroengineering and rehabilitation2025
Closed-loop rehabilitation of upper-limb dyskinesia after stroke: from natural motion to neuronal microfluidics.
Review in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparative efficacy of brain-computer interface-coupled robotic rehabilitation, robot-assisted therapy, and motor imagery for upper-limb motor recovery after stroke: a systematic review and network meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Neuroplasticity Mechanism of Stroke Rehabilitation Training System Based on Virtual Reality: A Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Impact of Virtual Reality-Based Task-Oriented Training for Enhancing Motor Function and Activities of Daily Living in Patients With Subacute Stroke: A Randomized Controlled Trial.Medical science monitor : international medical journal of experimental and clinical research · 2026Trial
- Development and validation of a risk prediction model for consciousness disorders in stroke patients in the intensive care unit (ICU): a retrospective study.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This review proposes an innovative closed-loop rehabilitation strategy that integrates multiple subdomains of stroke science to address the global challenge of upper-limb dyskinesia post-stroke. Despite advancements in neural remodeling and rehabilitation research, the compartmentalization of subdomains has limited the effectiveness of current rehabilitation strategies. Our approach unites key areas-including the post-stroke brain, upper-limb rehabilitation robotics, motion sensing, metrics, neural microfluidics, and neuroelectronics-into a cohesive framework designed to enhance upper-limb motion rehabilitation outcomes. By leveraging cutting-edge technologies such as lightweight rehabilitation robotics, advanced motion sensing, and neural microfluidic models, this strategy enables real-time monitoring, adaptive interventions, and personalized rehabilitation plans. Furthermore, we explore the potential of closed-loop systems to drive neural plasticity and functional recovery, offering a transformative perspective on stroke rehabilitation. Finally, we discuss future directions, emphasizing the integration of emerging technologies and interdisciplinary collaboration to advance the field. This review highlights the promise of closed-loop strategies in achieving unprecedented integration of subdomains and improving post-stroke upper-limb rehabilitation outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.