SELF-LEARNING CONCEPTS OF DIAGNOSTIC TESTS BY GRAPHICAL APPROACH: SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE AND NEGATIVE PREDICTIVE VALUE

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YASERI M.; YEKANINEJAD M.S.; PAKPOUR A.; RAHMANI S.; RANGIN H.; AKABERI A.

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Journal: Journal of North Khorasan University of Medical Sciences Year:2012 | Volume:4 | Issue:2 (12) Page(s): 275-283

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Title

SELF-LEARNING CONCEPTS OF DIAGNOSTIC TESTS BY GRAPHICAL APPROACH: SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE AND NEGATIVE PREDICTIVE VALUE

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Keywords

DIAGNOSTIC TESTSQ2

SENSITIVITYQ2

SPECIFICITYQ3

POSITIVE PREDICTIVE VALUEQ3

NEGATIVE PREDICTIVE VALUEQ3

Abstract

Background & Objectives: Considering the high application of DIAGNOSTIC TESTS in medical practice, understanding the concept of diagnostic test evaluation indices is highly important. Ability of test in distinguishing healthy subjects from patients is evaluated by SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE (PPV), NEGATIVE PREDICTIVE VALUE (NPV). Although their understanding is relatively straightforward, they are often used mistakenly or interchangeably. We have tried to present these concepts utilizing the diagrams to make it easier to understand and remember.Materials & Methods: we used diagrams to illustrate the concept of diagnostic indexes. Then we showed the effect of change of prevalence of disease in these indexes.Results: SENSITIVITY and SPECIFICITY are not sensitive to the prevalence of disease in the population. On the other hand positive predicted value increase when the prevalence of disease increase and NEGATIVE PREDICTIVE VALUE increase when the prevalence of disease decreases.Conclusion: Although it has been shown that SENSITIVITY and SPECIFICITY are essential in evaluation of accuracy in DIAGNOSTIC TESTS, their ability to discover the degree of confidence is limited, so the predictive values is used to indicate the posterior probability of disease after observing the test results. It should be considered that predictive values are dependent upon the prevalence of disease in population and should not be generalized for different populations with different prevalence of diseases.

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APA: Copy

YASERI, M., YEKANINEJAD, M.S., PAKPOUR, A., RAHMANI, S., RANGIN, H., & AKABERI, A.. (2012). SELF-LEARNING CONCEPTS OF DIAGNOSTIC TESTS BY GRAPHICAL APPROACH: SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE AND NEGATIVE PREDICTIVE VALUE. JOURNAL OF NORTH KHORASAN UNIVERSITY OF MEDICAL SCIENCES, 4(2 (12)), 275-283. SID. https://sid.ir/paper/186914/en

Vancouver: Copy

YASERI M., YEKANINEJAD M.S., PAKPOUR A., RAHMANI S., RANGIN H., AKABERI A.. SELF-LEARNING CONCEPTS OF DIAGNOSTIC TESTS BY GRAPHICAL APPROACH: SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE AND NEGATIVE PREDICTIVE VALUE. JOURNAL OF NORTH KHORASAN UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2012;4(2 (12)):275-283. Available from: https://sid.ir/paper/186914/en

IEEE: Copy

M. YASERI, M.S. YEKANINEJAD, A. PAKPOUR, S. RAHMANI, H. RANGIN, and A. AKABERI, “SELF-LEARNING CONCEPTS OF DIAGNOSTIC TESTS BY GRAPHICAL APPROACH: SENSITIVITY, SPECIFICITY, POSITIVE PREDICTIVE VALUE AND NEGATIVE PREDICTIVE VALUE,” JOURNAL OF NORTH KHORASAN UNIVERSITY OF MEDICAL SCIENCES, vol. 4, no. 2 (12), pp. 275–283, 2012, [Online]. Available: https://sid.ir/paper/186914/en

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