Ted Wu

1.1k citations
31 papers · 814 indexed · h-index 12

Impact in

Papers in

Ted Wu

28 papers receiving 795 citations

Peers

Ted Wu
Comparison fields: 5 of 87
  • Endocrinology, Diabetes and Metabolism 533
  • Family Practice 18
  • Genetics 176
  • Drug Discovery 1
  • Nephrology 33
Replace Connie Luo with:
Connie Luo Australia
Abdulghani Alsaeed Saudi Arabia
K.M. PRASANNA KUMAR India
James J. Chamberlain United States
Rimei Nishimura Japan
Wayne Weng United States
A. D. Morris United Kingdom
Nadia Lascar United Kingdom
Hugh D. Tildesley Canada
Khalid S Aljabri Saudi Arabia
Ted Wu relative to Connie Luo Australia Connie Luo's profile →
Citations per field
00.5×1.5×
Connie Luo · 1×
Citations per year

Countries citing papers authored by Ted Wu

Since Specialization
Citations

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

Fields of papers citing papers by Ted Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ted Wu. 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 Ted Wu. The network helps show where Ted Wu may publish in the future.

Co-authors

The 25 scholars most cited alongside Ted Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ted Wu Line = papers co-authored together Ted Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20230
4 202214
5 20227
6 202110
7 20217
8 20216
9 202118
10 201932
11
High rate of asymptomatic hypoglycemia in insulin-treated diabetes with severe chronic kidney disease: Utility of flash interstitial glucose monitoring
20183
12 20173
13 20173
14 201712
15 201534
16 201523
17 20149
18 201410
19 200920
20 200518

About Ted Wu

Ted Wu is a scholar working on Endocrinology, Diabetes and Metabolism, Obstetrics and Gynecology, Family Practice, Nephrology and Endocrine and Autonomic Systems, having authored 31 papers that have together received 814 indexed citations. Recurring topics across this work include Diabetes Management and Research (11 papers), Diabetes Treatment and Management (11 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (5 papers), Pancreatic function and diabetes (5 papers), Cardiovascular Function and Risk Factors (4 papers), Gestational Diabetes Research and Management (4 papers), Birth, Development, and Health (3 papers) and Chronic Kidney Disease and Diabetes (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (533 citations), Family Practice (18 citations), Genetics (176 citations), Drug Discovery (1 citation) and Nephrology (33 citations). Ted Wu has collaborated with scholars based in Australia, Saudi Arabia and New Zealand. Frequent co-authors include Jencia Wong, Stephen M. Twigg, Maria Constantino, Lynda Molyneaux, Dennis K. Yue, Abdulghani Alsaeed, Connie Luo, Mario D’Souza, Timothy Middleton and Margaret McGill. Their work appears in journals such as Journal of Diabetes and its Complications, Diabetes Therapy, Diabetes Care, Diabetes Research and Clinical Practice and Diabetic Medicine.

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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