Jennifer H. Kuo

2.1k total citations
81 papers, 1.1k citations indexed

About

Jennifer H. Kuo is a scholar working on Surgery, Endocrinology, Diabetes and Metabolism and Nephrology. According to data from OpenAlex, Jennifer H. Kuo has authored 81 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Surgery, 39 papers in Endocrinology, Diabetes and Metabolism and 18 papers in Nephrology. Recurrent topics in Jennifer H. Kuo's work include Thyroid Cancer Diagnosis and Treatment (32 papers), Thyroid and Parathyroid Surgery (31 papers) and Parathyroid Disorders and Treatments (18 papers). Jennifer H. Kuo is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (32 papers), Thyroid and Parathyroid Surgery (31 papers) and Parathyroid Disorders and Treatments (18 papers). Jennifer H. Kuo collaborates with scholars based in United States, Canada and Italy. Jennifer H. Kuo's co-authors include James A. Lee, John A. Chabot, Catherine McManus, Gabrielle K. Steinl, Parwez Hossain, David F. Anderson, Michael S. Wong, James Lim, Christoph Troppmann and Richard V. Perez and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Cell Biology and Circulation Research.

In The Last Decade

Jennifer H. Kuo

73 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jennifer H. Kuo United States 19 530 389 330 234 156 81 1.1k
Philipp Riss Austria 21 815 1.5× 554 1.4× 461 1.4× 187 0.8× 173 1.1× 84 1.3k
Fabio Medas Italy 24 1.2k 2.2× 799 2.1× 229 0.7× 148 0.6× 111 0.7× 92 1.5k
Giuseppe Pisano Italy 21 811 1.5× 411 1.1× 179 0.5× 104 0.4× 74 0.5× 81 1.1k
Marlon A. Guerrero United States 22 683 1.3× 505 1.3× 183 0.6× 323 1.4× 145 0.9× 57 1.7k
Gianluca Donatini France 19 881 1.7× 499 1.3× 137 0.4× 167 0.7× 51 0.3× 67 1.1k
Gloria Tsvetov Israel 16 270 0.5× 492 1.3× 121 0.4× 196 0.8× 78 0.5× 44 837
Kunal Jain United States 11 340 0.6× 329 0.8× 28 0.1× 124 0.5× 75 0.5× 44 714
Menno R. Vriens Netherlands 17 471 0.9× 426 1.1× 228 0.7× 164 0.7× 87 0.6× 51 979
Jennifer B. Ogilvie United States 24 1.1k 2.2× 1.4k 3.5× 426 1.3× 435 1.9× 374 2.4× 49 2.1k
N. Caeyers Netherlands 6 171 0.3× 139 0.4× 39 0.1× 71 0.3× 54 0.3× 8 1.4k

Countries citing papers authored by Jennifer H. Kuo

Since Specialization
Citations

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

Fields of papers citing papers by Jennifer H. Kuo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jennifer H. Kuo

This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer H. Kuo. A scholar is included among the top collaborators of Jennifer H. Kuo 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 Jennifer H. Kuo. Jennifer H. Kuo 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.
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Hu, Yinin, et al.. (2025). Short-term outcomes in radiofrequency ablation for the primary treatment of T1N0M0 papillary thyroid carcinomas. The American Journal of Surgery. 253. 116702–116702.
3.
Dream, Sophie, et al.. (2025). Consensus Thyroid Nodule Radiofrequency Ablation Reporting Guidelines Established Using a Delphi Approach. Endocrine Practice. 31(10). 1292–1296.
4.
5.
Huang, Yongmei, Jason D. Wright, Jennifer H. Kuo, et al.. (2024). Does the Adoption of Molecular Testing Cause Decreased Thyroidectomy Rates in a National Cohort? A Quasiexperimental Study of High- Versus Low-Adoption States. Thyroid. 34(3). 388–398. 11 indexed citations
6.
Dream, Sophie, et al.. (2024). Variable practice patterns in the surgical management of renal hyperparathyroidism. Surgery. 177. 108880–108880.
7.
Kuo, Eric J., et al.. (2024). Radiofrequency ablation of Bethesda category III thyroid nodules with benign molecular testing: Preliminary findings from a single institution. The American Journal of Surgery. 241. 115929–115929. 1 indexed citations
8.
Brown, Jessica, Justin Kim, Douglas J. Turner, et al.. (2023). Age-stratified comparison of active surveillance versus radiofrequency ablation for papillary thyroid microcarcinoma using decision analysis. Surgery. 175(1). 153–160. 3 indexed citations
9.
Sinclair, Catherine F., Jung Hwan Baek, Steven P. Hodak, et al.. (2023). General Principles for the Safe Performance, Training, and Adoption of Ablation Techniques for Benign Thyroid Nodules: An American Thyroid Association Statement. Thyroid. 33(10). 1150–1170. 35 indexed citations
10.
Kluger, Michael D., Yongmei Huang, Jennifer H. Kuo, et al.. (2021). Perioperative and persistent opioid utilization following pancreatectomy in the United States. HPB. 24(6). 912–924. 1 indexed citations
11.
Kuo, Jennifer H., Catherine F. Sinclair, Brian Hung‐Hin Lang, et al.. (2021). A comprehensive review of interventional ablation techniques for the management of thyroid nodules and metastatic lymph nodes. Surgery. 171(4). 920–931. 15 indexed citations
12.
Graves, Claire E., Richard Y. Hwang, Catherine McManus, James A. Lee, & Jennifer H. Kuo. (2021). The effect of chronic kidney disease on intraoperative parathyroid hormone: A linear mixed model analysis. Surgery. 169(5). 1152–1157. 4 indexed citations
13.
14.
Gazes, Yunglin, Jennifer H. Kuo, James A. Lee, et al.. (2020). Functional magnetic resonance imaging in primary hyperparathyroidism. European Journal of Endocrinology. 183(1). 21–30. 5 indexed citations
15.
Madani, Amin, Jennifer H. Kuo, Elliot J. Mitmaker, et al.. (2019). Defining the competencies for laparoscopic transabdominal adrenalectomy: An investigation of intraoperative behaviors and decisions of experts. Surgery. 167(1). 241–249. 7 indexed citations
16.
Williams, John M., et al.. (2019). Risk factors for vertebral fracture in primary hyperparathyroidism. Endocrine. 66(3). 682–690. 15 indexed citations
17.
Chabot, John A., et al.. (2019). A stepwise analysis of the diagnostic algorithm for the prediction of malignancy in thyroid nodules. Surgery. 167(1). 28–33. 6 indexed citations
18.
Kuo, Jennifer H., et al.. (2018). CCP1 promotes mitochondrial fusion and motility to prevent Purkinje cell neuron loss in pcd mice. The Journal of Cell Biology. 218(1). 206–219. 27 indexed citations
19.
Kuo, Jennifer H., et al.. (2013). Secreted HoxA3 Promotes Epidermal Proliferation and Angiogenesis in Genetically Modified Three-Dimensional Composite Skin Constructs. Advances in Wound Care. 3(10). 605–613. 5 indexed citations
20.
Kuo, Jennifer H., Michael S. Wong, Richard V. Perez, et al.. (2011). Renal Transplant Wound Complications in the Modern Era of Obesity. Journal of Surgical Research. 173(2). 216–223. 46 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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