April Barnado

983 total citations
36 papers, 595 citations indexed

About

April Barnado is a scholar working on Rheumatology, Immunology and Pathology and Forensic Medicine. According to data from OpenAlex, April Barnado has authored 36 papers receiving a total of 595 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Rheumatology, 17 papers in Immunology and 10 papers in Pathology and Forensic Medicine. Recurrent topics in April Barnado's work include Systemic Lupus Erythematosus Research (27 papers), Atherosclerosis and Cardiovascular Diseases (9 papers) and Systemic Sclerosis and Related Diseases (9 papers). April Barnado is often cited by papers focused on Systemic Lupus Erythematosus Research (27 papers), Atherosclerosis and Cardiovascular Diseases (9 papers) and Systemic Sclerosis and Related Diseases (9 papers). April Barnado collaborates with scholars based in United States, Taiwan and France. April Barnado's co-authors include Leslie J. Crofford, Jim C. Oates, Lee Wheless, Robert J. Carroll, Joshua C. Denny, Carolyn Casey, Melissa Cunningham, Diane L. Kamen, Gary S. Gilkeson and Liang-Miin Tsai and has published in prestigious journals such as PLoS ONE, Frontiers in Immunology and Annals of the Rheumatic Diseases.

In The Last Decade

April Barnado

34 papers receiving 580 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
April Barnado United States 14 306 277 92 89 78 36 595
S. Marzouk Tunisia 14 327 1.1× 255 0.9× 129 1.4× 53 0.6× 76 1.0× 94 652
Isabelle Ayoub United States 15 282 0.9× 164 0.6× 78 0.8× 51 0.6× 60 0.8× 49 647
DJ Wallace United States 13 555 1.8× 348 1.3× 61 0.7× 59 0.7× 102 1.3× 24 732
Maria J. H. de Hair Netherlands 18 727 2.4× 296 1.1× 109 1.2× 80 0.9× 110 1.4× 34 1.1k
Allen R. Hendricks United States 8 600 2.0× 446 1.6× 129 1.4× 81 0.9× 102 1.3× 16 913
Laura Massaro Italy 19 768 2.5× 355 1.3× 80 0.9× 47 0.5× 150 1.9× 46 990
Henrique Ataíde Mariz Brazil 11 365 1.2× 247 0.9× 56 0.6× 54 0.6× 147 1.9× 17 695
Francisco Javier Ballina‐García Spain 16 299 1.0× 260 0.9× 141 1.5× 64 0.7× 72 0.9× 35 633
Androniki Bili United States 12 410 1.3× 99 0.4× 82 0.9× 70 0.8× 35 0.4× 17 619
Philip Ruiz United States 17 151 0.5× 260 0.9× 103 1.1× 62 0.7× 51 0.7× 30 795

Countries citing papers authored by April Barnado

Since Specialization
Citations

This map shows the geographic impact of April Barnado's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by April Barnado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites April Barnado more than expected).

Fields of papers citing papers by April Barnado

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by April Barnado. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by April Barnado. The network helps show where April Barnado may publish in the future.

Co-authorship network of co-authors of April Barnado

This figure shows the co-authorship network connecting the top 25 collaborators of April Barnado. A scholar is included among the top collaborators of April Barnado based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with April Barnado. April Barnado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Barnado, April, Ryan Moore, Henry J. Domenico, et al.. (2024). Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model. Frontiers in Immunology. 15. 1384229–1384229. 4 indexed citations
2.
3.
Walker, Katherine, et al.. (2024). Peripartum maternal outcomes in individuals with systemic lupus erythematosus in a real-world electronic health record cohort. Seminars in Arthritis and Rheumatism. 70. 152603–152603.
4.
Pacheco, Jennifer A., Chengsheng Mao, Joshua Smith, et al.. (2024). Natural language processing to identify lupus nephritis phenotype in electronic health records. BMC Medical Informatics and Decision Making. 22(S2). 348–348. 4 indexed citations
5.
Barnado, April, et al.. (2023). Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis. Seminars in Arthritis and Rheumatism. 59. 152167–152167. 4 indexed citations
6.
Clowse, Megan E. B., Jim C. Oates, April Barnado, et al.. (2023). Implications of the accuracy of diagnostic algorithms for systemic lupus on our understanding of racial disparities in pregnancy outcomes. Lara D. Veeken. 63(1). 119–126. 2 indexed citations
7.
Barnado, April, et al.. (2023). Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record. Arthritis & Rheumatology. 75(9). 1532–1541. 2 indexed citations
8.
9.
Johnson, Deborah, et al.. (2021). Contribution of viral infection to risk for cancer in systemic lupus erythematosus and multiple sclerosis. PLoS ONE. 16(1). e0243150–e0243150. 13 indexed citations
10.
Wilfong, Erin M., Jennifer J. Young, Narender Annapureddy, et al.. (2021). Anti-tRNA synthetase syndrome interstitial lung disease: A single center experience. Respiratory Medicine. 191. 106432–106432. 4 indexed citations
11.
Challa, Anup P., Rebecca N Jerome, Robert R. Lavieri, et al.. (2021). Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases. Frontiers in Genetics. 12. 707836–707836. 13 indexed citations
12.
13.
Reese, Tyler, Alyson L. Dickson, Megan M. Shuey, et al.. (2019). Increased blood pressure visit-to-visit variability in patients with systemic lupus erythematosus: association with inflammation and comorbidity burden. Lupus. 28(8). 954–960. 6 indexed citations
14.
Wheless, Lee, et al.. (2019). Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record. Arthritis Research & Therapy. 21(1). 305–305. 32 indexed citations
15.
Boone, James B., et al.. (2019). Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis. Lupus. 28(8). 977–985. 8 indexed citations
16.
Barnado, April, Robert J. Carroll, Carolyn Casey, et al.. (2018). Phenome-wide association study identifies marked increased in burden of comorbidities in African Americans with systemic lupus erythematosus. Arthritis Research & Therapy. 20(1). 69–69. 24 indexed citations
17.
18.
Barnado, April, Robert J. Carroll, Carolyn Casey, et al.. (2018). Phenome‐Wide Association Studies Uncover a Novel Association of Increased Atrial Fibrillation in Male Patients With Systemic Lupus Erythematosus. Arthritis Care & Research. 70(11). 1630–1636. 23 indexed citations
19.
Shuey, Megan M., Omar Khan, Jeremy L. Neal, et al.. (2018). SAT0420 Increased resistant hypertension in patients with systemic lupus erythematosus: a retrospective cohort study. Annals of the Rheumatic Diseases. 77. 1070–1070.
20.
Casey, Carolyn, Cecilia P. Chung, Leslie J. Crofford, & April Barnado. (2016). Rheumatologists’ perception of systemic lupus erythematosus quality indicators: significant interest and perceived barriers. Clinical Rheumatology. 36(1). 97–102. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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