Sheldon X. Kong

4.4k total citations · 1 hit paper
96 papers, 3.2k citations indexed

About

Sheldon X. Kong is a scholar working on Endocrinology, Diabetes and Metabolism, Pharmacology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Sheldon X. Kong has authored 96 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Endocrinology, Diabetes and Metabolism, 21 papers in Pharmacology and 14 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Sheldon X. Kong's work include Diabetes Treatment and Management (29 papers), Diabetes Management and Research (20 papers) and Inflammatory mediators and NSAID effects (16 papers). Sheldon X. Kong is often cited by papers focused on Diabetes Treatment and Management (29 papers), Diabetes Management and Research (20 papers) and Inflammatory mediators and NSAID effects (16 papers). Sheldon X. Kong collaborates with scholars based in United States, Canada and United Kingdom. Sheldon X. Kong's co-authors include Panagiotis Mavros, Bengt Jönsson, Hind T. Hatoum, William C. Gerth, Hans‐Georg Eichler, Susan Keller, Elke Hunsche, Todd Hobbs, Frederick Wolfe and Mark Kosinski and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Diabetes Care.

In The Last Decade

Sheldon X. Kong

92 papers receiving 3.1k citations

Hit Papers

align trajectories

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.

Use of Cost-Effectiveness Analysis in Health-Care Resource Allocation Decision-Making: How Are Cost-Effectiveness Thresholds Expected to Emerge?

2004 623 citations Sheldon X. Kong, Panagiotis Mavros et al. Value in Health profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sheldon X. Kong United States	29	637	588	567	480	453	96	3.2k
Patrick C. Souverein Netherlands	35	717 1.1×	290 0.5×	616 1.1×	221 0.5×	305 0.7×	201	4.4k
Francesco Lapi Italy	33	469 0.7×	305 0.5×	559 1.0×	273 0.6×	190 0.4×	163	4.2k
Susana Perez‐Gutthann United States	30	848 1.3×	1.4k 2.4×	584 1.0×	274 0.6×	1.0k 2.2×	80	5.1k
Yea‐Huei Kao Yang Taiwan	31	743 1.2×	208 0.4×	528 0.9×	494 1.0×	265 0.6×	107	4.8k
Susan E. Andrade United States	46	1.3k 2.1×	291 0.5×	579 1.0×	1.1k 2.3×	551 1.2×	103	7.6k
Raymond G. Schlienger Switzerland	34	565 0.9×	548 0.9×	332 0.6×	249 0.5×	349 0.8×	81	4.0k
Mette Madsen Denmark	31	499 0.8×	476 0.8×	435 0.8×	268 0.6×	144 0.3×	73	3.9k
Umesh Kadam United Kingdom	31	396 0.6×	594 1.0×	274 0.5×	494 1.0×	394 0.9×	81	4.1k
Corri Black United Kingdom	36	526 0.8×	237 0.4×	467 0.8×	515 1.1×	209 0.5×	94	5.0k
Linda L. Humphrey United States	24	568 0.9×	147 0.3×	1.1k 2.0×	228 0.5×	228 0.5×	44	5.4k

Countries citing papers authored by Sheldon X. Kong

Since Specialization

Citations

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

Fields of papers citing papers by Sheldon X. Kong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheldon X. Kong. 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 Sheldon X. Kong. The network helps show where Sheldon X. Kong may publish in the future.

Co-authorship network of co-authors of Sheldon X. Kong

This figure shows the co-authorship network connecting the top 25 collaborators of Sheldon X. Kong. A scholar is included among the top collaborators of Sheldon X. Kong 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 Sheldon X. Kong. Sheldon X. Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Tangri, Navdeep, Rakesh K. Singh, Yan Chen, et al.. (2025). Change in urine albumin-to-creatinine ratio and clinical outcomes in patients with chronic kidney disease and type 2 diabetes. BMJ Open Diabetes Research & Care. 13(5). e004854–e004854.

Kong, Sheldon X., et al.. (2025). Guideline-directed medical therapies (GDMTs) for chronic kidney disease with type 2 diabetes (CKD + T2D): Translation of lessons learned from the management of heart failure. Journal of Diabetes and its Complications. 39(4). 108985–108985.

Yao, Jianying, et al.. (2024). Adverse events and costs among non-metastatic castration-resistant prostate cancer patients. Journal of Medical Economics. 27(1). 145–152. 1 indexed citations

Stewart, Fiona, et al.. (2024). Exploring kidney dialysis costs in the United States: a scoping review. Journal of Medical Economics. 27(1). 618–625. 1 indexed citations

Schabath, Matthew B., Jonathan P. Thompson, Jyoti Malhotra, et al.. (2024). Clinical characteristics and treatment patterns of patients with NTRK fusion–positive solid tumors: A multisite cohort study at US academic cancer centers. Journal of Managed Care & Specialty Pharmacy. 30(7). 672–683. 1 indexed citations

Thompson, J. L., Jyoti Malhotra, Gilbert Ko, et al.. (2022). CO118 TRK Inhibitor Treatment Patterns in Patients with NTRK Fusion-Positive Solid Tumors: A Multi-Site Cohort Study at U.S. Academic Cancer Centers. Value in Health. 25(12). S41–S41. 1 indexed citations

Wanner, Christoph, Johannes Schuchhardt, Chris Bauer, et al.. (2022). Clinical characteristics and disease outcomes in non-diabetic chronic kidney disease: retrospective analysis of a US healthcare claims database. Journal of Nephrology. 36(1). 45–54. 7 indexed citations

Pantalone, Kevin M., Anita D. Misra‐Hebert, Todd Hobbs, et al.. (2020). The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care. 43(8). 1910–1919. 34 indexed citations

Weng, Wayne, Sheldon X. Kong, Rahul Ganguly, et al.. (2020). The prevalence of cardiovascular disease by vascular bed and impact on healthcare costs in a large, real‐world population with type 2 diabetes. Endocrinology Diabetes & Metabolism. 3(2). e00106–e00106. 8 indexed citations

10.

Misra‐Hebert, Anita D., Alex Milinovich, Xinge Ji, et al.. (2020). Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System. Diabetes Care. 43(8). 1937–1940. 10 indexed citations

11.

Weng, Wayne, Ye Tian, Sheldon X. Kong, et al.. (2019). The prevalence of cardiovascular disease and antidiabetes treatment characteristics among a large type 2 diabetes population in the United States. Endocrinology Diabetes & Metabolism. 2(3). e00076–e00076. 43 indexed citations

12.

Weng, Wayne, Edward S. Kimball, Todd Hobbs, et al.. (2016). Decreasing incidence of type 2 diabetes mellitus in the United States, 2007–2012: Epidemiologic findings from a large US claims database. Diabetes Research and Clinical Practice. 117. 111–118. 27 indexed citations

13.

Langman, M. J. S., et al.. (2004). Initiation of antihypertensive therapy among new users of cyclooxygenase-2-selective and nonselective NSAIDs. International Journal of Clinical Pharmacology and Therapeutics. 42(5). 260–266. 1 indexed citations

14.

Schnitzer, Thomas J., et al.. (2004). A comprehensive review of clinical trials on the efficacy and safety of drugs for the treatment of low back pain. Journal of Pain and Symptom Management. 28(1). 72–95. 78 indexed citations

15.

Ethgen, Olivier, Kristijan H. Kahler, Sheldon X. Kong, Jean‐Yves Reginster, & Frederick Wolfe. (2002). The effect of health related quality of life on reported use of health care resources in patients with osteoarthritis and rheumatoid arthritis: a longitudinal analysis.. PubMed. 29(6). 1147–55. 69 indexed citations

16.

Marshall, John K., James M. Pellissier, Cheryl Attard, Sheldon X. Kong, & Michael Marentette. (2001). Incremental Cost-Effectiveness Analysis Comparing Rofecoxib with Nonselective NSAIDs in Osteoarthritis. PharmacoEconomics. 19(10). 1039–1049. 39 indexed citations

17.

Kosinski, Mark, Susan Keller, Hind T. Hatoum, Sheldon X. Kong, & John E. Ware. (1999). The SF-36 Health Survey as a Generic Outcome Measure in Clinical Trials of Patients With Osteoarthritis and Rheumatoid Arthritis. Medical Care. 37(SUPPLEMENT). MS10–MS22. 160 indexed citations

18.

Keller, Susan, John E. Ware, Hind T. Hatoum, & Sheldon X. Kong. (1999). The SF-36 Arthritis-Specific Health Index (ASHI). Medical Care. 37(SUPPLEMENT). MS51–MS60. 37 indexed citations

19.

Ware, John E., Susan Keller, Hind T. Hatoum, & Sheldon X. Kong. (1999). The SF-36 Arthritis-Specific Health Index (ASHI). Medical Care. 37(SUPPLEMENT). MS40–MS50. 54 indexed citations

20.

Hatoum, Hind T., et al.. (1998). Prevalence of insomnia: a survey of the enrollees at five managed care organizations.. PubMed. 4(1). 79–86. 52 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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