The efficacy of virtual reality technology for the postoperative rehabilitation of patients with cervical spondylotic myelopathy (CSM): a study protocol for a randomized controlled trial.

BACKGROUND: Patients with cervical spondylosis myelopathy (CSM) may experience severe neurological dysfunction due to untimely spinal cord compression after surgery. These disorders may lead to sensory and motion disorders, causing considerable psychological distress. Recent studies found that virtual reality (VR) technology can be an effective tool for treating spinal cord injuries. Owing to this discovery, we developed an exploratory research project to investigate the impact of this intervention on the postoperative recovery of patients with CSM. METHODS: The purpose of this randomized controlled trial was to evaluate the efficacy of combining VR technology with conventional rehabilitation strategies for the postoperative rehabilitation of patients with CSM. A total of 78 patients will be recruited and randomized to either the conventional rehabilitation group or the group subjected to VR technology combined with conventional rehabilitation strategies. The Japanese Orthopaedic Association (JOA) scale will be the main tool used, and secondary outcomes will be measured via the visual analogue scale (VAS), neck disability index (NDI), and functional MRI (fMRI). The data analysis will identify differences between the intervention and control groups as well as any relationship between the intragroup changes in the functional area of the brain and the subjective scale scores after the intervention. DISCUSSION: The aim of this trial is to investigate the effect of VR training on the postoperative rehabilitation of patients with CSM after 12 intervention treatments. Positive and negative outcomes will help us better understand the effectiveness of the intervention and its neural impact. If effective, this study could provide new options for the postoperative rehabilitation of patients with CSM. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2300071544). Registered 17 May 2023, https://www.chictr.org.cn/ .

Learning From the Future: Light Cone Modeling for Sequential Recommendation.

Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user's present interests from his/her past-to-present behaviors, i.e., making recommendations with only the unidirectional past information. This article argues that future information is another critical factor for the sequential recommendation. However, directly learning from future-to-present behaviors inevitably causes data leakage. Here, it is pointed out that future information can be learned from users' collaborative behaviors. Toward this end, this article introduces sequential graphs to depict item transition relationships: where and how each item transits from and will transit to. This temporal evolution information is called the light cone in special and general relativity. Then, a bidirectional sequential graph convolutional network (BiSGCN) is proposed to learn item representations by encoding past and future light cones. Finally, a manifold translating embedding (MTE) method is proposed to model item transition patterns in Riemannian manifolds, which helps to better capture the geometric structures of light cones and item transition patterns. Experimental comparisons and ablation studies verify the outstanding performance of BiSGCN, the benefits of learning from the future, and the improvements of learning in Riemannian manifolds.