 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 |
|---|
| 1987 | 0 | 2 | 2 | | 1988 | 0 | 5 | 5 | | 1989 | 0 | 2 | 2 | | 1990 | 0 | 2 | 2 | | 1991 | 0 | 11 | 11 | | 1992 | 0 | 8 | 8 | | 1993 | 0 | 7 | 7 | | 1994 | 0 | 12 | 12 | | 1995 | 0 | 9 | 9 | | 1996 | 0 | 10 | 10 | | 1997 | 0 | 11 | 11 | | 1998 | 0 | 11 | 11 | | 1999 | 0 | 16 | 16 | | 2000 | 0 | 18 | 18 | | 2001 | 0 | 20 | 20 | | 2002 | 0 | 27 | 27 | | 2003 | 0 | 32 | 32 | | 2004 | 0 | 24 | 24 | | 2005 | 0 | 37 | 37 | | 2006 | 0 | 39 | 39 | | 2007 | 0 | 46 | 46 | | 2008 | 2 | 49 | 51 | | 2009 | 1 | 58 | 59 | | 2010 | 0 | 64 | 64 | | 2011 | 0 | 72 | 72 | | 2012 | 0 | 58 | 58 | | 2013 | 0 | 62 | 62 | | 2014 | 0 | 48 | 48 | | 2015 | 0 | 65 | 65 | | 2016 | 0 | 55 | 55 | | 2017 | 0 | 56 | 56 | | 2018 | 2 | 54 | 56 | | 2019 | 1 | 68 | 69 | | 2020 | 1 | 60 | 61 | | 2021 | 1 | 49 | 50 | | 2022 | 0 | 9 | 9 |
To return to the timeline, click here.
Below are the most recent publications written about "Predictive Value of Tests" by people in Profiles.
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Groos D, Adde L, Aubert S, Boswell L, de Regnier RA, Fjørtoft T, Gaebler-Spira D, Haukeland A, Loennecken M, Msall M, Möinichen UI, Pascal A, Peyton C, Ramampiaro H, Schreiber MD, Silberg IE, Songstad NT, Thomas N, Van den Broeck C, Øberg GK, Ihlen EAF, Støen R. 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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Ojha V, Khalique OK, Khurana R, Lorenzatti D, Leung SW, Lawton B, Slesnick TC, Cavalcante JC, Ducci CB, Patel AR, Prieto CC, Plein S, Raman SV, Salerno M, Parwani P. 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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Wang S, Chauhan D, Patel H, Amir-Khalili A, da Silva IF, Sojoudi A, Friedrich S, Singh A, Landeras L, Miller T, Ameyaw K, Narang A, Kawaji K, Tang Q, Mor-Avi V, Patel AR. 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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Chen W, Doeblin P, Al-Tabatabaee S, Klingel K, Tanacli R, Jakob Weiß K, Stehning C, Patel AR, Pieske B, Zou J, Kelle S. 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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Greenland P, Polonsky TS. 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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Raman SV, Markl M, Patel AR, Bryant J, Allen BD, Plein S, Seiberlich N. 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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Rojas JC, Fahrenbach J, Makhni S, Cook SC, Williams JS, Umscheid CA, Chin MH. Framework for Integrating Equity Into Machine Learning Models: A Case Study. Chest. 2022 06; 161(6):1621-1627.
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Walpot J, Massalha S, Jayasinghe P, Sadaf M, Clarkin O, Godkin L, Sharma A, Ratnayake I, Godkin K, Jia K, Hossain A, Crean AM, Chan M, Butler C, Tandon V, Nagele P, Woodard PK, Mrkobrada M, Szczeklik W, Aziz YFA, Biccard B, Devereaux PJ, Sheth T, Chow BJW. Normalized Subendocardial Myocardial Attenuation on Coronary Computed Tomography Angiography Predicts Postoperative Adverse Cardiovascular Events: Coronary CTA VISION Substudy. Circ Cardiovasc Imaging. 2022 01; 15(1):e012654.
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Palmer C, Truong VT, Slivnick JA, Wolking S, Coleman P, Mazur W, Zareba KM. Atrial function and geometry differences in transthyretin versus immunoglobulin light chain amyloidosis: a cardiac magnetic resonance study. Sci Rep. 2022 01 07; 12(1):140.
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Patel AR, Kelle S, Fontana M, Jacob R, Stojanovska J, Collins J, Patel HN, Francone M, Han Y, Bandettini WP, Bucciarelli-Ducci C, Raman S, Weissman G. SCMR level II/independent practitioner training guidelines for cardiovascular magnetic resonance: integration of a virtual training environment. J Cardiovasc Magn Reson. 2021 12 27; 23(1):139.
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