ObjectiveTo evaluate the efficacy of robotic intersphincteric resection (ISR) for rectal cancer.MethodsA literature search was performed using the China biomedical literature database, Chinese CNKI, Wanfang, PubMed, Embase, and the Cochrane library. The retrieval time was from the establishment of databases to April 1, 2019. Related interest indicators were brought into meta-analysis by Review Manager 5.2 software.ResultsA total of 510 patients were included in 5 studies, including 273 patients in the robot group and 237 patients in the laparoscopic group. As compared to the laparoscopic group, the robot group had significantly longer operative time [MD=43.27, 95%CI (16.48, 70.07), P=0.002], less blood loss [MD=–19.98.27, 95%CI (–33.14, –6.81), P=0.003], lower conversion rate [MD=0.20, 95%CI (0.04, –0.95), P=0.04], less lymph node harvest [MD=–1.71, 95%CI (–3.21, –0.21), P=0.03] and shorter hospital stay [MD=–1.61, 95%CI (–2.26, –0.97), P<0.000 01]. However, there were no statistically significant differences in the first flatus [MD=–0.01, 95%CI (–0.48, 0.46), P=0.96], time to diet [MD=–0.20, 95%CI (–0.67, 0.27), P=0.41], incidence of complications [OR=0.76, 95%CI (0.50, 1.14), P=0.18], distal resection margin [MD=0.00, 95%CI (–0.17, 0.17), P=0.98] and positive rate of circumferential resection margin [OR=0.61, 95%CI (0.27, 1.37), P=0.23].ConclusionsRobotic and laparoscopic ISR for rectal cancer shows comparable perioperative outcomes. Compared with laparoscopic ISR, robotic ISR has the advantages of less blood loss, lower conversion rate, and longer operation times. These findings suggest that robotic ISR is a safe and effective technique for treating low rectal cancer.
Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Evidence-based Chinese medicine is a relatively new discipline which applies the concepts and methods of evidence-based medicine (EBM) to the clinical research and practice of Chinese medicine. It is not only a branch of EBM but also a natural product of the development of Chinese medicine. This paper introduces the theoretical concepts of evidence-based Chinese medicine and reviews the process of its development. It then elucidates the main characteristics of evidence-based Chinese medicine, emphasizes its holistic approach, prescription-syndrome relationship, and its human-centered approach. Research contents and status quo are also summarized to point out the challenges of the production and application of evidence. Finally, we innovatively indicate further research directions on combining individual-based research with population-based research and developing narrative EBM.