 Databases, Factual
"Databases, Factual" 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.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
| Descriptor ID |
D016208
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| MeSH Number(s) |
L01.470.750.750 L01.700.508.300.188.400
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| Concept/Terms |
Databases, Factual- Databases, Factual
- Database, Factual
- Factual Database
- Factual Databases
- Data Bases, Factual
- Data Base, Factual
- Factual Data Base
- Factual Data Bases
- Data Banks, Factual
- Data Bank, Factual
- Factual Data Bank
- Factual Data Banks
- Databanks, Factual
- Databank, Factual
- Factual Databank
- Factual Databanks
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Below are MeSH descriptors whose meaning is more general than "Databases, Factual".
Below are MeSH descriptors whose meaning is more specific than "Databases, Factual".
This graph shows the total number of publications written about "Databases, Factual" by people in this website by year, and whether "Databases, Factual" 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 |
|---|
| 1992 | 1 | 0 | 1 | | 1993 | 1 | 2 | 3 | | 1994 | 0 | 4 | 4 | | 1995 | 0 | 2 | 2 | | 1996 | 0 | 4 | 4 | | 1997 | 2 | 2 | 4 | | 1998 | 2 | 5 | 7 | | 1999 | 1 | 7 | 8 | | 2000 | 3 | 4 | 7 | | 2001 | 1 | 9 | 10 | | 2002 | 1 | 9 | 10 | | 2003 | 2 | 7 | 9 | | 2004 | 4 | 7 | 11 | | 2005 | 1 | 4 | 5 | | 2006 | 0 | 7 | 7 | | 2007 | 3 | 11 | 14 | | 2008 | 10 | 22 | 32 | | 2009 | 2 | 12 | 14 | | 2010 | 2 | 23 | 25 | | 2011 | 3 | 18 | 21 | | 2012 | 1 | 16 | 17 | | 2013 | 4 | 20 | 24 | | 2014 | 3 | 26 | 29 | | 2015 | 4 | 39 | 43 | | 2016 | 6 | 59 | 65 | | 2017 | 2 | 47 | 49 | | 2018 | 2 | 44 | 46 | | 2019 | 8 | 37 | 45 | | 2020 | 1 | 10 | 11 |
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Below are the most recent publications written about "Databases, Factual" by people in Profiles.
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Shashikumar SA, Huang K, Konetzka RT, Joynt Maddox KE. Claims-based Frailty Indices: A Systematic Review. Med Care. 2020 09; 58(9):815-825.
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Paner GP. Editorial Comment. J Urol. 2020 08; 204(2):265-266.
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Reynolds MA, Henriksen KJ, Chang A. End-Stage Kidney Disease Is Overlooked as a Proximate Cause of Death at Autopsy. Am J Clin Pathol. 2020 05 05; 153(6):772-775.
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Brown S, Savva Y, Barth R, LaMattina J, Thuluvath PJ. Multiple Regional Listing Increases Liver Transplant Rates for Those With Model for End-stage Liver Disease Score <15. Transplantation. 2020 04; 104(4):762-769.
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Rubin DS, Apfelbaum JL, Tung A. Trends in Central Venous Catheter Insertions by Anesthesia Providers: An Analysis of the Medicare Physician Supplier Procedure Summary From 2007 to 2016. Anesth Analg. 2020 04; 130(4):1026-1034.
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Shelby RD, Suarez-Kelly LP, Yu PY, Hughes TM, Ethun CG, Tran TB, Poultsides G, King DM, Bedi M, Mogal H, Clarke C, Tseng J, Roggin KK, Chouliaras K, Votanopoulos K, Krasnick BA, Fields RC, Pollock RE, Howard JH, Cardona K, Grignol V. Neoadjuvant radiation improves margin-negative resection rates in extremity sarcoma but not survival. J Surg Oncol. 2020 Jun; 121(8):1249-1258.
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Thalji SZ, Tsai S, Gamblin TC, Clarke C, Christians K, Charlson J, Ethun CG, Poultsides G, Grignol VP, Roggin KK, Votanopoulos K, Fields RC, Abbott DE, Cardona K, Mogal H. Outcomes of palliative-intent surgery in retroperitoneal sarcoma-Results from the US Sarcoma Collaborative. J Surg Oncol. 2020 Jun; 121(7):1140-1147.
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Kayle M, Valle J, Paulukonis S, Holl JL, Tanabe P, French DD, Garg R, Liem RI, Badawy SM, Treadwell MJ. Impact of Medicaid expansion on access and healthcare among individuals with sickle cell disease. Pediatr Blood Cancer. 2020 05; 67(5):e28152.
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Major A, Cox SM, Volchenboum SL. Using big data in pediatric oncology: Current applications and future directions. Semin Oncol. 2020 02; 47(1):56-64.
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Agius R, Brieghel C, Andersen MA, Pearson AT, Ledergerber B, Cozzi-Lepri A, Louzoun Y, Andersen CL, Bergstedt J, von Stemann JH, Jørgensen M, Tang ME, Fontes M, Bahlo J, Herling CD, Hallek M, Lundgren J, MacPherson CR, Larsen J, Niemann CU. Machine learning can identify newly diagnosed patients with CLL at high risk of infection. Nat Commun. 2020 01 17; 11(1):363.
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