Objective:To carry out the scoping review on the form, function and application effect of decision aids for breast reconstruction of breast cancer patients, so as to provide reference for domestic research on decision aids for breast reconstruction.Methods:According to the scoping review framework, literature was systematically searched on Web of Science, PubMed, Cochrane Library, Embase, Scopus, CINAHL, China National Knowledge Infrastructure, Wanfang Data, VIP, and China Biology Medicine disc. The retrieval period was from the establishment of the database to January 19, 2024.Results:A total of 14 articles were included, including 8 randomized controlled trials (RCT), four quasi-experimental studies, one qualitative study, and one prospective cohort study. The main forms of decision aids included web-based software programs, mobile applications (APP), decision support groups, or decision support manuals. The evaluation of intervention effectiveness mainly involved indicators such as patient decision regret, decision conflict, satisfaction, acceptance, and patient-reported outcomes.Conclusions:Breast reconstruction decision aids have a certain positive impact on improving patient satisfaction, decision quality, and patient-reported outcomes, but most studies have not examined their long-term effects. The research on decision aids for breast reconstruction in China is still in its infancy. It is suggested that future research should draw on relevant foreign studies to develop and advance high quality and diversified decision aids, and promote their clinical applications.

Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.
Brain Waves ; Electroencephalography ; Epilepsy, Temporal Lobe ; diagnosis ; Humans