David Scheinker

1.7k total citations
91 papers, 913 citations indexed

About

David Scheinker is a scholar working on Surgery, Endocrinology, Diabetes and Metabolism and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, David Scheinker has authored 91 papers receiving a total of 913 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Surgery, 21 papers in Endocrinology, Diabetes and Metabolism and 19 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in David Scheinker's work include Diabetes Management and Research (19 papers), Diabetes and associated disorders (15 papers) and Pancreatic function and diabetes (14 papers). David Scheinker is often cited by papers focused on Diabetes Management and Research (19 papers), Diabetes and associated disorders (15 papers) and Pancreatic function and diabetes (14 papers). David Scheinker collaborates with scholars based in United States, Canada and United Kingdom. David Scheinker's co-authors include Alexander Scheinker, Fátima Rodríguez, Margaret L. Brandeau, Priya Prahalad, David M. Maahs, Michael Fairley, Andrew Ward, Ananta Addala, Korey K. Hood and Manisha Desai and has published in prestigious journals such as JAMA, Circulation and Nature Medicine.

In The Last Decade

David Scheinker

81 papers receiving 896 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Scheinker United States 17 305 277 205 178 132 91 913
Rishi Sikka United States 11 118 0.4× 119 0.4× 33 0.2× 125 0.7× 47 0.4× 27 1.0k
Ya‐Wen Chuang Taiwan 21 267 0.9× 67 0.2× 85 0.4× 106 0.6× 42 0.3× 109 1.4k
Henry C. Chueh United States 20 120 0.4× 237 0.9× 75 0.4× 163 0.9× 35 0.3× 49 1.7k
Christopher J. Tignanelli United States 25 435 1.4× 86 0.3× 53 0.3× 127 0.7× 54 0.4× 128 2.1k
Tamás Ferenci Hungary 17 123 0.4× 101 0.4× 35 0.2× 145 0.8× 14 0.1× 108 832
Kayo Waki Japan 21 221 0.7× 301 1.1× 35 0.2× 101 0.6× 24 0.2× 72 1.5k
Boikanyo Makubate Botswana 12 52 0.2× 32 0.1× 67 0.3× 148 0.8× 37 0.3× 63 1.3k
Kristel J.M. Janssen Netherlands 17 298 1.0× 59 0.2× 28 0.1× 277 1.6× 30 0.2× 26 1.3k
Sebastian J. Vollmer United Kingdom 15 50 0.2× 59 0.2× 58 0.3× 41 0.2× 21 0.2× 37 1.0k
Mark Joy United Kingdom 16 99 0.3× 55 0.2× 22 0.1× 112 0.6× 45 0.3× 86 1.4k

Countries citing papers authored by David Scheinker

Since Specialization
Citations

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

Fields of papers citing papers by David Scheinker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Scheinker

This figure shows the co-authorship network connecting the top 25 collaborators of David Scheinker. A scholar is included among the top collaborators of David Scheinker 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 David Scheinker. David Scheinker 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.
Sandhu, Alexander T., et al.. (2026). Changes in Clinician Telemedicine Use and Care Patterns for Patients With Heart Failure in the United States. PubMed. 19(2). e012089–e012089.
2.
Rezaii, Paymon G., Daniel B. Herrick, Seth Tigchelaar, et al.. (2024). Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study. Neurospine. 21(2). 620–632. 1 indexed citations
3.
Miller, Daniel, Gurpreet S. Dhillon, Nicholas Bambos, Andrew Y. Shin, & David Scheinker. (2023). WAVES – The Lucile Packard Children’s Hospital Pediatric Physiological Waveforms Dataset. Scientific Data. 10(1). 124–124. 2 indexed citations
4.
Bunning, Bryan, Haley Hedlin, Jonathan H. Chen, et al.. (2023). The evolving role of data & safety monitoring boards for real-world clinical trials. Journal of Clinical and Translational Science. 7(1). e179–e179. 1 indexed citations
5.
Shi, Yuan, et al.. (2023). Surgical scheduling via optimization and machine learning with long-tailed data. Health Care Management Science. 26(4). 692–718. 6 indexed citations
6.
Scheinker, David, Andrew Ward, Korey K. Hood, et al.. (2022). Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice. JMIR Diabetes. 7(2). e27284–e27284. 14 indexed citations
7.
Safranek, Conrad & David Scheinker. (2022). A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Annals of Epidemiology. 76. 136–142. 3 indexed citations
8.
9.
Fleming, Scott L., A. Johnson, Jenna Kruger, et al.. (2021). Performance of a Commonly Used Pressure Injury Risk Model Under Changing Incidence. The Joint Commission Journal on Quality and Patient Safety. 48(3). 131–138. 4 indexed citations
10.
Parizo, Justin, et al.. (2021). County‐Level Factors Associated With Cardiovascular Mortality by Race/Ethnicity. Journal of the American Heart Association. 10(6). e018835–e018835. 12 indexed citations
11.
Scheinker, David, et al.. (2021). Individualized risk trajectories for iron‐related adverse outcomes in repeat blood donors. Transfusion. 62(1). 116–124. 6 indexed citations
12.
Ward, Andrew, et al.. (2021). Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning. Anesthesia & Analgesia. 133(2). 304–313. 19 indexed citations
13.
Bagshaw, H.P., Nastaran Heidari, David Scheinker, et al.. (2021). A personalized decision aid for prostate cancer shared decision making. BMC Medical Informatics and Decision Making. 21(1). 374–374. 8 indexed citations
14.
Ward, Andrew, et al.. (2021). Improved individual and population-level HbA1c estimation using CGM data and patient characteristics. Journal of Diabetes and its Complications. 35(8). 107950–107950. 10 indexed citations
15.
Glynn, Peter W., et al.. (2021). The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE). Health Care Management Science. 24(2). 375–401. 3 indexed citations
16.
Addala, Ananta, et al.. (2020). Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance. Pediatric Diabetes. 21(7). 1301–1309. 54 indexed citations
17.
Ward, Andrew, Ashish Sarraju, Sukyung Chung, et al.. (2020). Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. npj Digital Medicine. 3(1). 125–125. 70 indexed citations
18.
Prahalad, Priya, Dessi P. Zaharieva, Ananta Addala, et al.. (2020). Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control—The 4T Study. Frontiers in Endocrinology. 11. 360–360. 39 indexed citations
19.
Singleton, Mark, Rita Agarwal, David Scheinker, et al.. (2017). The Pediatric Anesthesiology Workforce: Projecting Supply and Trends 2015–2035. Anesthesia & Analgesia. 126(2). 568–578. 14 indexed citations
20.
Scheinker, David. (2013). Hilbert function spaces and the Nevanlinna–Pick problem on the polydisc II. Journal of Functional Analysis. 266(1). 355–367. 2 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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