ObjectivesTo assess the methodological and reporting quality of surgical meta-analyses published in English in 2014.MethodsAll meta-analyses investigating surgical procedures published in 2014 were selected from PubMed and EMbase. The characteristics of these meta-analyses were collected, and their reporting and methodological quality were assessed by the PRISMA and AMSTAR, respectively. Independent predictive factors associated with these two qualities were evaluated by univariate and multivariate analyses.ResultsA total of 197 meta-analyses covering 10 surgical subspecialties were included. The mean PRISMA and AMSTAR score (by items) were 22.2±2.4 and 7.8±1.2, respectively, and a positive linear correlation was found between them with a R2 of 0.754. Those meta-analyses conducted by the first authors who had previously published meta-analysis was significantly higher in reporting and methodological quality than those who had not (P<0.001). Meanwhile, there were also significant differences in these reporting (P<0.001) and methodological (P<0.001) quality between studies published in Q1 ranked journals and (Q2+Q3) ranked jounals. On multivariate analyses, region of origin (non-Asiavs. Asia), publishing experience of first authors (ever vs. never), rank of publishing journals (Q1 vs. Q2+Q3), and preregistration (presence vs. absence) were associated with better reporting and methodologic quality, independently.ConclusionThe reporting and methodological quality of current surgical meta-analyses remained suboptimal, and first authors' experience and ranking of publishing journals were independently associated with both qualities. Preregistration may be an effective measure to improve the quality of meta-analysis, which deserves more attention from future meta-analysis reviewers.
With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.