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10.1371/journal.pone.0150891 http://scihub22266oqcxt.onion/10.1371/journal.pone.0150891 C4777363!4777363 !26939066     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26939066 &cmd=llinks): Failed to open stream: HTTP request failed! 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How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes Theorem |pdf=|usr=}}{{26939066 }} gab.com Text How Much Overtesting Is Needed to Safely Exclude a Diagnosis A Different Perspective on Triage Testing Using Bayes Theorem JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26939066 #FOAvip Ruling out disease often requires expensive or potentially harmful confirmation testing. For such testing, a less invasive triage test is often used. Intuitively, few negative confirmatory tests suggest success of this approach. However, if negative confirmation tests become too rare, too many disease cases could have been missed. It is therefore important to know how many negative tests are needed to safely exclude a diagnosis. We quantified this relationship using Bayes' theorem, and applied this to the example of pulmonary embolism (PE), for which triage is done with a Clinical Decision Rule (CDR) and D-dimer testing, and CT-angiography (CTA) is the confirmation test. For a maximum proportion of missed PEs of 1% in triage-negative patients, we calculate a 67% 'mandatory minimum' proportion of negative CTA scans. To achieve this, the proportion of patients with PE undergoing triage testing should be appropriately low, in this case no higher than 24%. Pre-test probability, triage test characteristics, the proportion of negative confirmation tests, and the number of missed diagnoses are mathematically entangled. The proportion of negative confirmation tests--not too high, but definitely not too low either--could be a quality benchmark for diagnostic processes. Twit Text FOAVip How Much Overtesting Is Needed to Safely Exclude a Diagnosis A Different Perspective on Triage Testing Using Bayes Theorem ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26939066 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26939066 #FOAvip English Wikipedia <ref>{{Cite pmid|26939066 }}</ref> How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes Theorem #MMPMID26939066 Sikkens JJ ; Beekman DG ; Thijs A ; Bossuyt PM ; Smulders YM PLoS One 2016[]; 11 (3 ): e0150891 PMID26939066 show ga Ruling out disease often requires expensive or potentially harmful confirmation testing. For such testing, a less invasive triage test is often used. Intuitively, few negative confirmatory tests suggest success of this approach. However, if negative confirmation tests become too rare, too many disease cases could have been missed. It is therefore important to know how many negative tests are needed to safely exclude a diagnosis. We quantified this relationship using Bayes' theorem, and applied this to the example of pulmonary embolism (PE), for which triage is done with a Clinical Decision Rule (CDR) and D-dimer testing, and CT-angiography (CTA) is the confirmation test. For a maximum proportion of missed PEs of 1% in triage-negative patients, we calculate a 67% 'mandatory minimum' proportion of negative CTA scans. To achieve this, the proportion of patients with PE undergoing triage testing should be appropriately low, in this case no higher than 24%. Pre-test probability, triage test characteristics, the proportion of negative confirmation tests, and the number of missed diagnoses are mathematically entangled. The proportion of negative confirmation tests--not too high, but definitely not too low either--could be a quality benchmark for diagnostic processes. |*Diagnosis, Differential [MESH] |*Predictive Value of Tests [MESH] |Algorithms [MESH] |Angiography/*methods [MESH] |Bayes Theorem [MESH] |Decision Support Techniques [MESH] |Fibrin Fibrinogen Degradation Products/analysis [MESH] |Humans [MESH] |Medical Overuse [MESH] |Models, Theoretical [MESH] |Probability [MESH] |Pulmonary Embolism/*therapy [MESH] |Risk [MESH] |Sensitivity and Specificity [MESH]How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Differ(epmc).pdf DeepDyve Pubget Overpricing