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10.1038/srep32151 http://scihub22266oqcxt.onion/10.1038/srep32151 C4997332!4997332!27557856     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27557856&cmd=llinks): Failed to open stream: HTTP request failed! 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Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability |pdf=|usr=}}{{27557856}} gab.com Text Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27557856 #FOAvip In the clinic, the pathological types of chronic kidney diseases (CKD) are considered references for choosing treatment protocols. From a statistical viewpoint, a non-invasive method to predict pathological types of CKD is a focus of our work. In the current study, following a frequency analysis of the clinical indices of 588 CKD patients in the department of nephrology, a third-grade class-A hospital, a novel theory is proposed: ?bi-directional cumulative probability dichotomy?. Further, two models for the prediction and differential diagnosis of CKD pathological type are established. The former indicates an occurrence probability of the pathological types, and the latter indicates an occurrence of CKD pathological type according to logistic binary regression. To verify the models, data were collected from 135 patients, and the results showed that the highest accuracy rate on membranous nephropathy (MN-100%), followed by IgA nephropathy (IgAN-83.33%) and mild lesion type (MLN-73.53%), whereas lower prediction accuracy was observed for mesangial proliferative glomerulonephritis (0%) and focal segmental sclerosis type (21.74%). The models of bi-directional probability prediction and differential diagnosis indicate a good prediction value in MN, IgAN and MLN and may be considered alternative methods for the pathological discrimination of CKD patients who are unable to undergo renal biopsy. Twit Text FOAVip Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27557856 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27557856 #FOAvip English Wikipedia <ref>{{Cite pmid|27557856}}</ref> Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability #MMPMID27557856 Yang Y; Luo M; Xiao L; Zhu Xj; Wang C; Fu X; Yuan Sg; Xiao F; Liu H; Dong Z; Liu Fy; Sun L Sci Rep 2016[]; 6 (ä): ä PMID27557856show ga In the clinic, the pathological types of chronic kidney diseases (CKD) are considered references for choosing treatment protocols. From a statistical viewpoint, a non-invasive method to predict pathological types of CKD is a focus of our work. In the current study, following a frequency analysis of the clinical indices of 588 CKD patients in the department of nephrology, a third-grade class-A hospital, a novel theory is proposed: ?bi-directional cumulative probability dichotomy?. Further, two models for the prediction and differential diagnosis of CKD pathological type are established. The former indicates an occurrence probability of the pathological types, and the latter indicates an occurrence of CKD pathological type according to logistic binary regression. To verify the models, data were collected from 135 patients, and the results showed that the highest accuracy rate on membranous nephropathy (MN-100%), followed by IgA nephropathy (IgAN-83.33%) and mild lesion type (MLN-73.53%), whereas lower prediction accuracy was observed for mesangial proliferative glomerulonephritis (0%) and focal segmental sclerosis type (21.74%). The models of bi-directional probability prediction and differential diagnosis indicate a good prediction value in MN, IgAN and MLN and may be considered alternative methods for the pathological discrimination of CKD patients who are unable to undergo renal biopsy. äExploration of pathological prediction of chronic kidney diseases by a(epmc).pdf DeepDyve Pubget Overpricing