ReviewNeuroSci2026
From Striatum to Prescription: An Evidence-Based and Bayesian Framework for Neuromotor Rehabilitation in Parkinson's Disease.
Review in NeuroSci, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) involves progressive basal ganglia dysfunction, with hyperexcitability of striatal indirect-pathway D2 medium spiny neurons (D2-MSNs) linked to motor impairment. Neuromotor rehabilitation is an important therapy, but its efficacy varies across interventions. This thematic review examines eleven rehabilitative strategies for PD, spanning forced and voluntary exercise (FE and VE respectively), non-invasive brain stimulation (rTMS, tDCS), and technology-based approaches such as exoskeletons, augmented reality, and dual-task training, within a framework distinguishing striatal recalibration from compensation via alternative motor networks. To formalize this distinction, a Bayesian ranking framework combines neurobiological plausibility with clinical evidence quality, identifying three functional clusters: a high-recalibration cluster (FE,
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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.