Recurrence rates following tLMD for LDH compare favorably with those in patients who have undergone open discectomy, lending further support for its effectiveness in treating single-level LDH. Nonobese patients with a relatively lower body mass index, in particular, appear to be at greater risk for recurrence.

Background: The new emerging application of decompression combined with fusion comes with a concern of cost performance, however, it is a lack of big data support. We aimed to evaluate the necessity or not of the addition of fusion for decompression in patients with lumbar degenerative spondylolisthesis. Methods: Potential studies were selected from PubMed, Web of Science, and Cochrane Library, and gray relevant studies were manually searched. We set the searching time spanning from the creating date of electronic engines to August 2020. STATA version 11.0 was exerted to process the pooled data. Results: Six RCTs were included in this study. A total of 650 patients were divided into 275 in the decompression group and 375 in the fusion group. No statistic differences were found in the visual analog scales (VAS) score for low back pain (weighted mean difference [WMD], –0.045; 95% confidence interval [CI], –1.259–1.169; P = .942) and leg pain (WMD, 0.075; 95% CI, –1.201–1.35; P = .908), Oswestry Disability Index (ODI) score (WMD, 1.489; 95% CI, –7.232–10.211; P = .738), European Quality of Life-5 Dimensions (EQ-5D) score (WMD, 0.03; 95% CI, –0.05–0.12; P = .43), Odom classification (OR, 0.353; 95% CI 0.113–1.099; P = .072), postoperative complications (OR, 0.437; 95% CI, 0.065–2.949; P = .395), secondary operation (OR, 2.541; 95% CI 0.897–7.198; P = .079), and postoperative degenerative spondylolisthesis (OR = 8.59, P = .27). Subgroup analysis of VAS score on low back pain (OR = 0.77, 95% CI, 0.36–1.65; P = .50) was demonstrated as no significant difference as well. Conclusion: The overall efficacy of the decompression combined with fusion is not revealed to be superior to decompression alone. At the same time, more evidence-based performance is needed to supplement this opinion.

Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVMbased products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG;(2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R 2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R 2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.