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10.1371/journal.pone.0195029 http://scihub22266oqcxt.onion/10.1371/journal.pone.0195029 C5871005!5871005!29584770     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2018 ; 13 (3): ä Nephropedia Template TP {{tp|p=29584770|t=2018. Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making |pdf=|usr=}}{{29584770}} gab.com Text Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29584770 #FOAvip In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for ?1-test cases?, where one binary hypothesis (?disease? vs. ?no disease?) has to be diagnosed based on one binary test result (?positive? vs. ?negative?). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical ?2-test scenarios?. Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information (?only textual information? vs. ?only tree diagram? vs. ?text and tree diagram in combination?), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams (?complete tree? vs. ?highlighted tree? vs. ?pruned tree?). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did. Twit Text FOAVip Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29584770 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29584770 #FOAvip English Wikipedia <ref>{{Cite pmid|29584770}}</ref> Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making #MMPMID29584770 Binder K; Krauss S; Bruckmaier G; Marienhagen J PLoS One 2018[]; 13 (3): ä PMID29584770show ga In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for ?1-test cases?, where one binary hypothesis (?disease? vs. ?no disease?) has to be diagnosed based on one binary test result (?positive? vs. ?negative?). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical ?2-test scenarios?. Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information (?only textual information? vs. ?only tree diagram? vs. ?text and tree diagram in combination?), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams (?complete tree? vs. ?highlighted tree? vs. ?pruned tree?). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did. äVisualizing the Bayesian 2-test case: The effect of tree diagrams on m(epmc).pdf DeepDyve Pubget Overpricing