Elsie Ross

3.4k total citations
41 papers, 657 citations indexed

About

Elsie Ross is a scholar working on Surgery, Pulmonary and Respiratory Medicine and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Elsie Ross has authored 41 papers receiving a total of 657 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Surgery, 17 papers in Pulmonary and Respiratory Medicine and 10 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Elsie Ross's work include Peripheral Artery Disease Management (23 papers), Cerebrovascular and Carotid Artery Diseases (12 papers) and Clinical practice guidelines implementation (6 papers). Elsie Ross is often cited by papers focused on Peripheral Artery Disease Management (23 papers), Cerebrovascular and Carotid Artery Diseases (12 papers) and Clinical practice guidelines implementation (6 papers). Elsie Ross collaborates with scholars based in United States, United Kingdom and Israel. Elsie Ross's co-authors include Nicholas J. Leeper, Nigam H. Shah, Alyssa M. Flores, John P. Cooke, Saeed Amal, Ronald L. Dalman, Kevin T. Nead, Jesutofunmi A. Omiye, Li Li and Kenneth Jung and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Circulation Research.

In The Last Decade

Elsie Ross

35 papers receiving 646 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Elsie Ross United States 14 227 151 148 104 92 41 657
Saskia Haitjema Netherlands 17 174 0.8× 183 1.2× 114 0.8× 57 0.5× 160 1.7× 106 1.1k
Hayan Jouni United States 16 162 0.7× 285 1.9× 123 0.8× 50 0.5× 194 2.1× 48 953
David A. Froehling United States 18 183 0.8× 236 1.6× 70 0.5× 120 1.2× 137 1.5× 41 1.1k
Rolv‐Ole Lindsetmo Norway 14 332 1.5× 129 0.9× 50 0.3× 137 1.3× 52 0.6× 36 791
Albert J. Rogers United States 18 247 1.1× 538 3.6× 170 1.1× 52 0.5× 44 0.5× 92 1.2k
Pasquale De Cata Italy 11 181 0.8× 58 0.4× 73 0.5× 165 1.6× 73 0.8× 19 869
Eilon Gabel United States 12 260 1.1× 264 1.7× 32 0.2× 65 0.6× 44 0.5× 22 512
Zahra Azizi Canada 14 122 0.5× 293 1.9× 69 0.5× 65 0.6× 114 1.2× 75 948
Robyn L. Ball United States 17 68 0.3× 109 0.7× 139 0.9× 146 1.4× 146 1.6× 41 867
Ohad Oren United States 11 78 0.3× 168 1.1× 68 0.5× 48 0.5× 103 1.1× 31 638

Countries citing papers authored by Elsie Ross

Since Specialization
Citations

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

Fields of papers citing papers by Elsie Ross

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Elsie Ross

This figure shows the co-authorship network connecting the top 25 collaborators of Elsie Ross. A scholar is included among the top collaborators of Elsie Ross 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 Elsie Ross. Elsie Ross 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.
2.
Ross, Elsie, et al.. (2024). Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease. Current Cardiovascular Risk Reports. 18(12). 187–195. 2 indexed citations
3.
Omiye, Jesutofunmi A., et al.. (2024). Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events. PLoS ONE. 19(5). e0303610–e0303610. 11 indexed citations
4.
Thomas, Dilip, Chikage Noishiki, Sadhana Gaddam, et al.. (2024). CCL2-mediated endothelial injury drives cardiac dysfunction in long COVID. Nature Cardiovascular Research. 3(10). 1249–1265. 10 indexed citations
5.
Wornow, Michael, Elsie Ross, Alison Callahan, & Nigam H. Shah. (2023). APLUS: A Python library for usefulness simulations of machine learning models in healthcare. Journal of Biomedical Informatics. 139. 104319–104319. 9 indexed citations
6.
Han, Jialin, et al.. (2023). Endovascular versus Surgical Lower Extremity Revascularization among Patients with Chronic Kidney Disease. International Journal of Nephrology. 2023. 1–8. 1 indexed citations
7.
Dossabhoy, Shernaz S., Vy T. Ho, Elsie Ross, Fátima Rodríguez, & Shipra Arya. (2023). Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Seminars in Vascular Surgery. 36(3). 401–412. 8 indexed citations
8.
9.
Dossabhoy, Shernaz S., Kenneth Tran, Elsie Ross, et al.. (2023). Validity of the Global Vascular Guidelines in Predicting Outcomes Based on First-Time Revascularization Strategy. Annals of Vascular Surgery. 95. 142–153. 2 indexed citations
10.
Amal, Saeed, et al.. (2022). Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care. Frontiers in Cardiovascular Medicine. 9. 840262–840262. 84 indexed citations
11.
Amal, Saeed, et al.. (2022). Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Scientific Reports. 12(1). 13364–13364. 18 indexed citations
12.
Flores, Alyssa M., Alejandro Schuler, Jeffrey W. Olin, et al.. (2021). Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups. Journal of the American Heart Association. 10(23). e021976–e021976. 33 indexed citations
13.
Han, Jialin, et al.. (2021). Comparison of Pre-Amputation Evaluation in Patients with and without Chronic Kidney Disease. American Journal of Nephrology. 52(5). 388–395. 1 indexed citations
14.
Dorsey, Chelsea, et al.. (2020). Update on workforce diversity in vascular surgery. Journal of Vascular Surgery. 74(1). 5–11.e1. 21 indexed citations
15.
Ho, Vy T., Nicholas J. Leeper, Nigam H. Shah, & Elsie Ross. (2020). Toward Automated Detection of Peripheral Artery Disease Using Electronic Health Records. Journal of Vascular Surgery. 72(1). e41–e41. 1 indexed citations
16.
Lee, Jason T., et al.. (2019). Diagnosis and management of external iliac endofibrosis: A case report. Journal of Vascular Nursing. 37(2). 86–90. 3 indexed citations
17.
Mell, Matthew W., Elsie Ross, & Marco Zavatta. (2016). National Comparison of Hybrid and Open Repair for Aortoiliac-Femoral Occlusive Disease. Journal of Vascular Surgery. 64(2). 551–551.
18.
Ross, Elsie, Nigam H. Shah, Ronald L. Dalman, Kevin T. Nead, & Nicholas J. Leeper. (2016). VESS18. Use of Predictive Analytics for the Identification of Latent Vascular Disease and Future Adverse Cardiac Events. Journal of Vascular Surgery. 63(6). 28S–29S. 2 indexed citations
19.
Ross, Elsie, Nigam H. Shah, & Nicholas J. Leeper. (2016). Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines. PLoS ONE. 11(5). e0154952–e0154952. 9 indexed citations
20.
Ross, Elsie, Nigam H. Shah, Ronald L. Dalman, et al.. (2016). The use of machine learning for the identification of peripheral artery disease and future mortality risk. Journal of Vascular Surgery. 64(5). 1515–1522.e3. 101 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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