Tim Peoples

1.3k total citations · 1 hit paper
6 papers, 938 citations indexed

About

Tim Peoples is a scholar working on Endocrinology, Diabetes and Metabolism, Surgery and Genetics. According to data from OpenAlex, Tim Peoples has authored 6 papers receiving a total of 938 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Endocrinology, Diabetes and Metabolism, 3 papers in Surgery and 3 papers in Genetics. Recurrent topics in Tim Peoples's work include Diabetes Management and Research (5 papers), Diabetes and associated disorders (3 papers) and Pancreatic function and diabetes (3 papers). Tim Peoples is often cited by papers focused on Diabetes Management and Research (5 papers), Diabetes and associated disorders (3 papers) and Pancreatic function and diabetes (3 papers). Tim Peoples collaborates with scholars based in United States, Ireland and Canada. Tim Peoples's co-authors include George Dailey, Richard M. Bergenstal, John B. Buse, John Welsh, Michael A. Wood, Andrew Ahmann, Stephen N. Davis, Bruce A. Perkins, Steven M. Willi and William V. Tamborlane and has published in prestigious journals such as New England Journal of Medicine, The Oncologist and Diabetes Technology & Therapeutics.

In The Last Decade

Tim Peoples

6 papers receiving 895 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.

Effectiveness of Sensor-Augmented Insulin-Pump Therapy in Type 1 Diabetes

2010 651 citations Richard M. Bergenstal, William V. Tamborlane et al. New England Journal of Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tim Peoples United States	6	656	554	452	176	89	6	938
Letizia Pieruzzi Italy	11	494 0.8×	175 0.3×	85 0.2×	133 0.8×	71 0.8×	25	625
L. Busutti Italy	11	288 0.4×	218 0.4×	46 0.1×	117 0.7×	83 0.9×	28	498
Bie‐Yu Huang Taiwan	12	519 0.8×	315 0.6×	105 0.2×	99 0.6×	28 0.3×	19	640
Monica M. Whitcher United States	12	752 1.1×	532 1.0×	111 0.2×	115 0.7×	32 0.4×	12	819
Dai Takeuchi Japan	9	277 0.4×	224 0.4×	39 0.1×	65 0.4×	37 0.4×	23	432
Concetta Sciammarella Italy	15	198 0.3×	132 0.2×	26 0.1×	412 2.3×	45 0.5×	24	709
Nagayoshi Ohta Japan	15	112 0.2×	284 0.5×	44 0.1×	218 1.2×	41 0.5×	35	584
Cristina Pasquini Italy	10	335 0.5×	199 0.4×	37 0.1×	34 0.2×	22 0.2×	15	426
Hankui Lu China	12	149 0.2×	102 0.2×	25 0.1×	127 0.7×	51 0.6×	31	430
Fabiana Pani Italy	14	212 0.3×	67 0.1×	37 0.1×	161 0.9×	51 0.6×	28	446

Countries citing papers authored by Tim Peoples

Since Specialization

Citations

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

Fields of papers citing papers by Tim Peoples

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tim Peoples

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

All Works

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6 of 6 papers shown

1.

Klempner, Samuel J., David Fabrizio, Shalmali Bane, et al.. (2019). Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. The Oncologist. 25(1). e147–e159. 243 indexed citations

2.

Buse, John B., George Dailey, Richard M. Bergenstal, et al.. (2011). Baseline Predictors of A1C Reduction in Adults Using Sensor-Augmented Pump Therapy or Multiple Daily Injection Therapy: The STAR 3 Experience. Diabetes Technology & Therapeutics. 13(6). 601–606. 12 indexed citations

3.

Peoples, Tim, Timothy S. Bailey, Ronald Brazg, et al.. (2011). Accuracy Performance of the Medtronic NexSensor® for 6 Days in an Inpatient Setting Using Abdomen and Buttocks Insertion Sites. Journal of Diabetes Science and Technology. 5(2). 358–364. 5 indexed citations

4.

Lynch, Peter, et al.. (2010). Resource utilization with insulin pump therapy for type 2 diabetes mellitus.. PubMed. 16(12). 892–6. 17 indexed citations

5.

Lynch, Peter, et al.. (2010). Improved A1C by switching to continuous subcutaneous insulin infusion from injection insulin therapy in type 2 diabetes: A retrospective claims analysis. Primary care diabetes. 4(4). 209–214. 10 indexed citations

6.

Bergenstal, Richard M., William V. Tamborlane, Andrew Ahmann, et al.. (2010). Effectiveness of Sensor-Augmented Insulin-Pump Therapy in Type 1 Diabetes. New England Journal of Medicine. 363(4). 311–320. 651 indexed citations breakdown →

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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