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10.3348/kjr.2016.17.3.339 http://scihub22266oqcxt.onion/10.3348/kjr.2016.17.3.339 C4842854!4842854!27134523     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Korean+J+Radiol 2016 ; 17 (3): 339-50 Nephropedia Template TP {{tp|p=27134523|t=2016. How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods |pdf=|usr=}}{{27134523}} gab.com Text How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods JO: Korean J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=27134523 #FOAvip Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers. Twit Text FOAVip How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods ????Korean J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=27134523 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27134523 #FOAvip English Wikipedia <ref>{{Cite pmid|27134523}}</ref> How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods #MMPMID27134523 Han K; Song K; Choi BW Korean J Radiol 2016[May]; 17 (3): 339-50 PMID27134523show ga Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers. äHow to Develop, Validate, and Compare Clinical Prediction Models Invol(epmc).pdf DeepDyve Pubget Overpricing