Predictive Value of Tests
"Predictive Value of Tests" 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.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Descriptor ID |
D011237
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MeSH Number(s) |
E05.318.780.800.650 N05.715.360.780.700.640 N06.850.520.445.800.650
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Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Predictive Value of Tests".
Below are MeSH descriptors whose meaning is more specific than "Predictive Value of Tests".
This graph shows the total number of publications written about "Predictive Value of Tests" by people in this website by year, and whether "Predictive Value of Tests" 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 |
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1993 | 0 | 8 | 8 |
1994 | 0 | 12 | 12 |
1995 | 0 | 9 | 9 |
1996 | 0 | 10 | 10 |
1997 | 0 | 10 | 10 |
1998 | 0 | 11 | 11 |
1999 | 0 | 15 | 15 |
2000 | 0 | 17 | 17 |
2001 | 0 | 20 | 20 |
2002 | 0 | 24 | 24 |
2003 | 0 | 30 | 30 |
2004 | 0 | 21 | 21 |
2005 | 0 | 34 | 34 |
2006 | 0 | 38 | 38 |
2007 | 0 | 41 | 41 |
2008 | 1 | 51 | 52 |
2009 | 1 | 61 | 62 |
2010 | 0 | 62 | 62 |
2011 | 0 | 71 | 71 |
2012 | 0 | 56 | 56 |
2013 | 0 | 61 | 61 |
2014 | 0 | 49 | 49 |
2015 | 0 | 65 | 65 |
2016 | 0 | 53 | 53 |
2017 | 0 | 48 | 48 |
2018 | 2 | 54 | 56 |
2019 | 1 | 67 | 68 |
2020 | 1 | 61 | 62 |
2021 | 1 | 43 | 44 |
2022 | 0 | 7 | 7 |
2023 | 0 | 2 | 2 |
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Below are the most recent publications written about "Predictive Value of Tests" by people in Profiles.
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Impact of wideband cardiac magnetic resonance on diagnosis, decision-making and outcomes in patients with implantable cardioverter defibrillators. Eur Heart J Cardiovasc Imaging. 2023 Jan 23; 24(2):181-189.
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Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis. Echocardiography. 2023 Mar; 40(3):188-195.
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Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk. JAMA Netw Open. 2022 07 01; 5(7):e2221325.
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Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world. J Cardiovasc Magn Reson. 2022 06 20; 24(1):38.
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Characterising acute kidney injury: The complementary roles of biomarkers of renal stress and renal function. J Crit Care. 2022 10; 71:154066.
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Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence. J Cardiovasc Magn Reson. 2022 04 11; 24(1):27.
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Synthetic Extracellular Volume in Cardiac Magnetic Resonance Without Blood Sampling: a Reliable Tool to Replace Conventional Extracellular Volume. Circ Cardiovasc Imaging. 2022 04; 15(4):e013745.
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40 Years of Research on Coronary Artery Calcium and Still No Convincing Clinical Trials? JACC Cardiovasc Imaging. 2022 05; 15(5):856-858.
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30-minute CMR for common clinical indications:?a?Society for Cardiovascular Magnetic Resonance white paper. J Cardiovasc Magn Reson. 2022 03 01; 24(1):13.
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Framework for Integrating Equity Into Machine Learning Models: A Case Study. Chest. 2022 06; 161(6):1621-1627.