Jay H. Shubrook

5.3k total citations · 2 hit papers
175 papers, 2.9k citations indexed

About

Jay H. Shubrook is a scholar working on Endocrinology, Diabetes and Metabolism, Epidemiology and Surgery. According to data from OpenAlex, Jay H. Shubrook has authored 175 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Endocrinology, Diabetes and Metabolism, 33 papers in Epidemiology and 29 papers in Surgery. Recurrent topics in Jay H. Shubrook's work include Diabetes Management and Research (59 papers), Diabetes Treatment and Management (39 papers) and Diabetes Management and Education (31 papers). Jay H. Shubrook is often cited by papers focused on Diabetes Management and Research (59 papers), Diabetes Treatment and Management (39 papers) and Diabetes Management and Education (31 papers). Jay H. Shubrook collaborates with scholars based in United States, Italy and Australia. Jay H. Shubrook's co-authors include Frank Schwartz, Kim Pfotenhauer, Carol Wysham, Cynthia R. Marling, James J. Chamberlain, NEIL SKOLNIK, Răzvan Bunescu, Cindy Marling, Kenneth Cusi and Stephen A. Harrison and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Internal Medicine and Gastroenterology.

In The Last Decade

Jay H. Shubrook

162 papers receiving 2.8k citations

Hit Papers

Clinical Care Pathway for... 2021 2026 2022 2024 2021 2025 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Clinical Care Pathway for the Risk Stratification and Management of Patients With Nonalcoholic Fatty Liver Disease
    2021 357 citations Jay H. Shubrook, Eugene E. Wright et al. Gastroenterology profile →
  2. Metabolic Dysfunction–Associated Steatotic Liver Disease (MASLD) in People With Diabetes: The Need for Screening and Early Intervention. A Consensus Report of the American Diabetes Association
    2025 25 citations Kenneth Cusi, Jay H. Shubrook et al. Diabetes Care profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jay H. Shubrook United States 29 1.6k 799 469 392 309 175 2.9k
Andrea O. Y. Luk Hong Kong 33 2.3k 1.4× 1.1k 1.4× 642 1.4× 758 1.9× 569 1.8× 188 4.2k
Paloma Almeda‐Valdés Mexico 25 848 0.5× 808 1.0× 287 0.6× 377 1.0× 329 1.1× 98 2.2k
Mojtaba Malek Iran 28 974 0.6× 690 0.9× 409 0.9× 266 0.7× 199 0.6× 141 2.4k
Banshi Saboo India 29 1.7k 1.1× 361 0.5× 610 1.3× 245 0.6× 362 1.2× 173 2.8k
Xuhong Hou China 33 856 0.5× 764 1.0× 319 0.7× 807 2.1× 344 1.1× 94 3.5k
Clara Weil Israel 13 2.0k 1.2× 771 1.0× 478 1.0× 709 1.8× 309 1.0× 39 4.4k
Anca Pantea Stoian Romania 31 986 0.6× 446 0.6× 694 1.5× 602 1.5× 313 1.0× 202 3.3k
Chin Meng Khoo Singapore 29 842 0.5× 765 1.0× 439 0.9× 811 2.1× 360 1.2× 107 2.8k
Sang Youl Rhee South Korea 35 1.0k 0.7× 553 0.7× 514 1.1× 714 1.8× 303 1.0× 210 3.9k
Franco De Michieli Italy 22 984 0.6× 1.6k 2.0× 359 0.8× 448 1.1× 369 1.2× 35 2.8k

Countries citing papers authored by Jay H. Shubrook

Since Specialization
Citations

This map shows the geographic impact of Jay H. Shubrook'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 Jay H. Shubrook with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay H. Shubrook more than expected).

Fields of papers citing papers by Jay H. Shubrook

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay H. Shubrook. 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 Jay H. Shubrook. The network helps show where Jay H. Shubrook may publish in the future.

Co-authorship network of co-authors of Jay H. Shubrook

This figure shows the co-authorship network connecting the top 25 collaborators of Jay H. Shubrook. A scholar is included among the top collaborators of Jay H. Shubrook 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 Jay H. Shubrook. Jay H. Shubrook 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
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Jing, Xia, Yuchun Zhou, James J. Cimino, et al.. (2025). Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses. BMC Medical Research Methodology. 25(1). 11–11. 1 indexed citations
3.
Shubrook, Jay H., et al.. (2025). Pioneering the future: incorporating lifestyle medicine tools in osteopathic medical education. Journal of Osteopathic Medicine. 125(7). 337–340.
4.
Shubrook, Jay H., et al.. (2025). Dermatological Manifestations of Diabetes Mellitus and Its Complications. Diabetology. 6(3). 18–18. 1 indexed citations
5.
Cusi, Kenneth, et al.. (2024). Making Sense of the Nonalcoholic Fatty Liver Disease Clinical Practice Guidelines: What Clinicians Need to Know. Diabetes Spectrum. 37(1). 29–38. 7 indexed citations
6.
Jing, Xia, James J. Cimino, Vimla L. Patel, et al.. (2024). Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?. Medical Research Archives. 12(2). 1 indexed citations
7.
Jing, Xia, James J. Cimino, Vimla L. Patel, et al.. (2024). Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools. Journal of Clinical and Translational Science. 8(1). e13–e13. 4 indexed citations
8.
Allen, Alina M., Michael Charlton, Kenneth Cusi, et al.. (2024). Guideline-based management of metabolic dysfunction-associated steatotic liver disease in the primary care setting. Postgraduate Medicine. 136(3). 229–245. 11 indexed citations
9.
Saha, Chandan, et al.. (2023). Program ACTIVE II: 6- and 12-month outcomes of a treatment approach for major depressive disorder in adults with type 2 diabetes. Journal of Diabetes and its Complications. 38(2). 108666–108666. 4 indexed citations
10.
Jing, Xia, Vimla L. Patel, James J. Cimino, et al.. (2023). A Visual Analytic Tool (VIADS) to Assist the Hypothesis Generation Process in Clinical Research: Mixed Methods Usability Study. JMIR Human Factors. 10. e44644–e44644. 6 indexed citations
11.
Jing, Xia, Vimla L. Patel, James J. Cimino, et al.. (2022). The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study. JMIR Research Protocols. 11(7). e39414–e39414. 7 indexed citations
12.
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Shubrook, Jay H., et al.. (2021). The necessary evolution of diabetes fellowships in the United States. Postgraduate Medicine. 133(4). 385–387.
14.
Brooks, Matthew, et al.. (2018). Improved Visualization of Hierarchical Datasets with VIADS.. AMIA. 2 indexed citations
15.
Shubrook, Jay H., et al.. (2018). Thrombotic Thrombocytopenic Purpura-Hemolytic Uremic Syndrome. Touro Scholar (Touro College). 10(2).
16.
Groot, Mary de, et al.. (2016). Lifetime Duration of Depressive Disorders in Patients With Type 2 Diabetes. Diabetes Care. 39(12). 2174–2181. 32 indexed citations
17.
Groot, Mary de, et al.. (2016). Lifetime Duration of Depressive Disorders in Patients With Type 2 Diabetes. PMC. 2 indexed citations
18.
Johnson, Eric L., et al.. (2016). Diabetes update: Your guide to the latest ADA standards.. PubMed. 65(5). 310–8. 2 indexed citations
19.
Bunescu, Răzvan, et al.. (2014). A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management. National Conference on Artificial Intelligence. 80 indexed citations
20.

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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