Notes in 05 Information Sources and Diagnostic Statistics

To Subscribe, use this Key

Status	Last Update	Fields
Published	01/19/2024	{{c2::Bisphosphonates (first-line)::First-line}} and {{c2::calcitonin (second-line)::second-line}} are useful in the management of {{c1::Paget disease…
Published	01/19/2024	{{c1::Sensitivity}} is the probability that when the disease is present, the test is positive
Published	01/19/2024	Sensitivity values approaching 100% is desirable for ruling {{c1::OUT::in or out}} disease
Published	01/19/2024	A high {{c1::sensitivity::sensitivity or specificity}} test is useful for screening in diseases with low prevalence
Published	01/19/2024	{{c1::Specificity}} is the probability that when the disease is absent, the test is negative
Published	01/19/2024	Specificity values approaching 100% is desirable for ruling {{c1::IN::in or out}} disease
Published	01/19/2024	A high {{c1::specificity}} test is useful for confirmation after a positive screening test
Published	01/19/2024	{{c1::Positive predictive value}} is the probability that when the test is positive, the disease is present
Published	01/19/2024	{{c1::Negative predictive value}} is the probability that when the test is negative, the disease is absent
Published	01/19/2024	Sensitivity/specificity = {{c1::fixed::fixed/vary}} depending on prevalence in population testedPositive/negative predictive value = {{c1::vary::fixed…
Published	01/19/2024	If sensitivity is 100%, then the false negative is {{c1::zero::#}}
Published	01/19/2024	If specificity is 100%, then false positive is {{c1::zero::#}}
Published	01/19/2024	What equation is used to calculate positive predictive value (PPV) using the table below?{{c1::PPV = TP / (TP + FP)}}
Published	01/19/2024	What equation is used to calculate negative predictive value (NPV) using the table below?{{c1::NPV = TN / (TN + FN)}}
Published	01/19/2024	What equation is used to calculate sensitivity using the table below?{{c1::Sensitivity = TP / (TP + FN)}}
Published	01/19/2024	What equation is used to calculate specificity using the table below?{{c1::Specificity = TN / (TN + FP)}}
Published	01/19/2024	Sources of evidence on the internet can sometimes be {{c1::biased}} and not have accurate information
Published	01/19/2024	When appraising an article, see if the research question has well-defined {{c1::PICOT}} elements and if the {{c1::study design}} is appropriate
Published	01/19/2024	5d1e727af72244f49989b3d8f86d169e-oa-1
Published	01/19/2024	5d1e727af72244f49989b3d8f86d169e-oa-2
Published	01/19/2024	5d1e727af72244f49989b3d8f86d169e-oa-3
Published	01/19/2024	{{c1::Verification}} bias is when the index test influences if the clinical reference standard test is performed or not
Published	01/19/2024	When appraising an article, it is important to see if there was {{c1::blinding}} for those that interpret the results of various tests or data collect…
Published	01/19/2024	The {{c1::reference}} line in an ROC curve corresponds to the point where you have an equal chance of getting a false positive or true positive result
Published	01/19/2024	In an ROC curve, a {{c1::larger}} area under the curve (approximately > 0.8) means that the test you are evaluating is better able to differen…
Published	01/22/2024	In an ROC curve, optimal compromise point between sensitivity and specificity is at the {{c1::top left}} of the graph
Published	01/19/2024	In an ROC curve, higher AUC means that there is {{c1::better}} diagnostic accuracy
Status	Last Update	Fields