 Sensitivity and Specificity
"Sensitivity and Specificity" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
| Descriptor ID |
D012680
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| MeSH Number(s) |
E05.318.740.872 E05.318.780.800 G17.800 N05.715.360.750.725 N05.715.360.780.700 N06.850.520.445.800 N06.850.520.830.872
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| Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Sensitivity and Specificity".
Below are MeSH descriptors whose meaning is more specific than "Sensitivity and Specificity".
This graph shows the total number of publications written about "Sensitivity and Specificity" by people in this website by year, and whether "Sensitivity and Specificity" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 1987 | 0 | 1 | 1 | | 1989 | 0 | 1 | 1 | | 1991 | 0 | 2 | 2 | | 1992 | 0 | 4 | 4 | | 1993 | 0 | 3 | 3 | | 1995 | 0 | 4 | 4 | | 1996 | 0 | 7 | 7 | | 1997 | 0 | 8 | 8 | | 1998 | 0 | 6 | 6 | | 1999 | 0 | 9 | 9 | | 2000 | 0 | 11 | 11 | | 2001 | 0 | 5 | 5 | | 2002 | 0 | 21 | 21 | | 2003 | 0 | 18 | 18 | | 2004 | 0 | 17 | 17 | | 2005 | 0 | 30 | 30 | | 2006 | 0 | 30 | 30 | | 2007 | 0 | 18 | 18 | | 2008 | 0 | 30 | 30 | | 2009 | 0 | 21 | 21 | | 2010 | 0 | 18 | 18 | | 2011 | 0 | 22 | 22 | | 2012 | 0 | 24 | 24 | | 2013 | 0 | 24 | 24 | | 2014 | 0 | 16 | 16 | | 2015 | 0 | 24 | 24 | | 2016 | 0 | 21 | 21 | | 2017 | 0 | 13 | 13 | | 2018 | 0 | 21 | 21 | | 2019 | 0 | 13 | 13 | | 2020 | 0 | 7 | 7 | | 2021 | 0 | 1 | 1 |
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Below are the most recent publications written about "Sensitivity and Specificity" by people in Profiles.
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Lashmanova N, Braun A, Cheng L, Gattuso P, Yan L. Endocervical adenocarcinoma in situ-from Papanicolaou test to hysterectomy: a series of 74 cases. J Am Soc Cytopathol. 2022 Jan-Feb; 11(1):13-20.
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Vespa PM, Olson DM, John S, Hobbs KS, Gururangan K, Nie K, Desai MJ, Markert M, Parvizi J, Bleck TP, Hirsch LJ, Westover MB. Evaluating the Clinical Impact of Rapid Response Electroencephalography: The DECIDE Multicenter Prospective Observational Clinical Study. Crit Care Med. 2020 09; 48(9):1249-1257.
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Moore NM, Li H, Schejbal D, Lindsley J, Hayden MK. Comparison of Two Commercial Molecular Tests and a Laboratory-Developed Modification of the CDC 2019-nCoV Reverse Transcriptase PCR Assay for the Detection of SARS-CoV-2. J Clin Microbiol. 2020 07 23; 58(8).
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Lopes Vendrami C, McCarthy RJ, Villavicencio CP, Miller FH. Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics. Abdom Radiol (NY). 2020 09; 45(9):2797-2809.
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Hu D, Lin H, Zeng X, Wang T, Deng J, Su X. Risk Factors for and Prediction Model of Skip Metastasis to Lateral Lymph Nodes in Papillary Thyroid Carcinoma. World J Surg. 2020 05; 44(5):1498-1505.
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Li J, Zhang B, Liu WX, Lu K, Pan H, Wang T, Oh CD, Yi D, Huang J, Zhao L, Ning G, Xing C, Xiao G, Liu-Bryan R, Feng S, Chen D. Metformin limits osteoarthritis development and progression through activation of AMPK signalling. Ann Rheum Dis. 2020 05; 79(5):635-645.
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Sidhu R, Kell P, Dietzen DJ, Farhat NY, Do AND, Porter FD, Berry-Kravis E, Vite CH, Reunert J, Marquardt T, Giugliani R, Lourenço CM, Bodamer O, Wang RY, Plummer E, Schaffer JE, Ory DS, Jiang X. Application of N-palmitoyl-O-phosphocholineserine for diagnosis and assessment of response to treatment in Niemann-Pick type C disease. Mol Genet Metab. 2020 04; 129(4):292-302.
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Swanson B, Fogg L, Julion W, Arrieta MT. Electronic Nose Analysis of Exhaled Breath Volatiles to Identify Lung Cancer Cases: A Systematic Review. J Assoc Nurses AIDS Care. 2020 Jan-Feb; 31(1):71-79.
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Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, Bao LY, Deng YB, Li XR, Cui XW, Dietrich CF. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. Radiology. 2020 01; 294(1):19-28.
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Blanco-Campal A, Diaz-Orueta U, Navarro-Prados AB, Burke T, Libon DJ, Lamar M. Features and psychometric properties of the Montreal Cognitive Assessment: Review and proposal of a process-based approach version (MoCA-PA). Appl Neuropsychol Adult. 2021 Nov-Dec; 28(6):658-672.
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