David Kerr

35.7k total citations · 5 hit papers
202 papers, 10.0k citations indexed

About

David Kerr is a scholar working on Oncology, Pathology and Forensic Medicine and Cancer Research. According to data from OpenAlex, David Kerr has authored 202 papers receiving a total of 10.0k indexed citations (citations by other indexed papers that have themselves been cited), including 117 papers in Oncology, 62 papers in Pathology and Forensic Medicine and 38 papers in Cancer Research. Recurrent topics in David Kerr's work include Colorectal Cancer Treatments and Studies (65 papers), Genetic factors in colorectal cancer (56 papers) and Colorectal Cancer Surgical Treatments (25 papers). David Kerr is often cited by papers focused on Colorectal Cancer Treatments and Studies (65 papers), Genetic factors in colorectal cancer (56 papers) and Colorectal Cancer Surgical Treatments (25 papers). David Kerr collaborates with scholars based in United Kingdom, United States and Norway. David Kerr's co-authors include Nicholas B. La Thangue, John P. Neoptolemos, F Lacaine, Helmut Friess, Laureano Fernández‐Cruz, Christos Dervenis, Markus W. Büchler, Richard Gray, Janet Dunn and Hans G. Beger and has published in prestigious journals such as New England Journal of Medicine, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

David Kerr

190 papers receiving 9.7k citations

Hit Papers

A Randomized Trial of Chemoradiotherapy and Chemotherapy ... 2004 2026 2011 2018 2004 2005 2011 2021 2021 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Kerr United Kingdom 45 6.5k 2.4k 2.1k 2.1k 1.9k 202 10.0k
Brigette Ma Hong Kong 53 4.9k 0.7× 2.7k 1.1× 2.3k 1.1× 2.4k 1.2× 1.1k 0.6× 243 10.2k
Howard S. Höchster United States 56 8.1k 1.2× 2.4k 1.0× 3.3k 1.6× 1.8k 0.9× 2.0k 1.1× 342 12.0k
David Khayat France 38 5.3k 0.8× 2.4k 1.0× 2.6k 1.2× 1.4k 0.7× 1.2k 0.7× 108 8.4k
Fausto Petrelli Italy 50 5.9k 0.9× 1.3k 0.6× 3.2k 1.5× 1.7k 0.8× 1.2k 0.7× 280 9.1k
George Pentheroudakis Greece 43 4.5k 0.7× 1.6k 0.7× 1.5k 0.7× 1.3k 0.7× 1.7k 0.9× 228 7.6k
Edith P. Mitchell United States 47 8.8k 1.4× 2.4k 1.0× 3.6k 1.7× 2.7k 1.3× 1.4k 0.8× 205 12.1k
Jean‐Luc Van Laethem Belgium 44 7.9k 1.2× 1.4k 0.6× 3.2k 1.5× 2.2k 1.1× 1.2k 0.6× 275 10.4k
Kristine Broglio United States 52 7.2k 1.1× 1.9k 0.8× 2.6k 1.2× 4.3k 2.1× 2.3k 1.2× 148 12.3k
Andrea Cercek United States 36 7.0k 1.1× 2.9k 1.2× 2.6k 1.2× 2.2k 1.1× 2.1k 1.1× 212 11.2k
Rocio García‐Carbonero Spain 51 6.7k 1.0× 2.4k 1.0× 2.1k 1.0× 1.4k 0.7× 1.2k 0.6× 306 9.7k

Countries citing papers authored by David Kerr

Since Specialization
Citations

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

Fields of papers citing papers by David Kerr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Kerr

This figure shows the co-authorship network connecting the top 25 collaborators of David Kerr. A scholar is included among the top collaborators of David Kerr 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 Kerr. David Kerr 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
2.
Kleppe, Andreas, Ole-Johan Skrede, Knut Liestøl, David Kerr, & Håvard E. Danielsen. (2024). Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond. Nature Machine Intelligence. 6(1). 2–3. 1 indexed citations
3.
Wang, Han, Dan Jiang, Haixian Zhang, et al.. (2023). An Explainable Coarse-to-Fine Survival Analysis Method on Multi-Center Whole Slide Images. IEEE Transactions on Artificial Intelligence. 5(3). 1316–1327. 3 indexed citations
5.
Marshall, John L., Beth N. Peshkin, Takayuki Yoshino, et al.. (2022). The Essentials of Multiomics. The Oncologist. 27(4). 272–284. 12 indexed citations
6.
Kleppe, Andreas, Ole-Johan Skrede, Sepp de Raedt, et al.. (2021). Designing deep learning studies in cancer diagnostics. Nature reviews. Cancer. 21(3). 199–211. 210 indexed citations breakdown →
7.
Lee, Lennard Y. W., Thomas Starkey, Shivan Sivakumar, et al.. (2019). ToxNav germline genetic testing and PROMinet digital mobile application toxicity monitoring: Results of a prospective single‐center clinical utility study—PRECISE study. Cancer Medicine. 8(14). 6305–6314. 6 indexed citations
8.
Huijbers, A., Gabi W. van Pelt, Rachel Kerr, et al.. (2018). The value of additional bevacizumab in patients with high‐risk stroma‐high colon cancer. A study within the QUASAR2 trial, an open‐label randomized phase 3 trial. Journal of Surgical Oncology. 117(5). 1043–1048. 13 indexed citations
9.
Kerr, David. (2016). Oxford textbook of oncology. Oxford University Press eBooks. 1 indexed citations
10.
Fotheringham, Susan, Håvard E. Danielsen, Tarjei S. Hveem, et al.. (2016). O-016 A prognostic marker for colorectal cancer: combining analyses of ploidy and stroma. Annals of Oncology. 27. ii124–ii124. 1 indexed citations
11.
Smith, Christopher G., David J. Fisher, Rebecca Harris, et al.. (2015). Analyses of 7,635 Patients with Colorectal Cancer Using Independent Training and Validation Cohorts Show That rs9929218 in CDH1 Is a Prognostic Marker of Survival. Clinical Cancer Research. 21(15). 3453–3461. 16 indexed citations
12.
Elzawawy, Ahmed & David Kerr. (2013). Variation in the availability of cancer drug generics in the United States of America. Annals of Oncology. 24. v17–v22. 6 indexed citations
13.
Hutchins, Gordon, Katie Southward, Kelly Handley, et al.. (2011). Value of Mismatch Repair, KRAS , and BRAF Mutations in Predicting Recurrence and Benefits From Chemotherapy in Colorectal Cancer. Journal of Clinical Oncology. 29(10). 1261–1270. 505 indexed citations breakdown →
14.
Kerr, David, Annie Young, & Richard Hobbs. (2011). ABC of colorectal cancer. Bulletin of Miscellaneous Information (Royal Gardens Kew). 31 indexed citations
15.
Knaul, Felícia Marie, Benjamin O. Anderson, Colin Bradley, & David Kerr. (2010). Access to Cancer Treatment in Low- and Middle-Income Countries - An Essential Part of Global Cancer Control. SSRN Electronic Journal. 6 indexed citations
16.
Kerr, David. (2008). Tougher at the top.. PubMed. Suppl. 22–3. 1 indexed citations
17.
James, R.D., David Kerr, JA Ledermann, et al.. (2000). Excess treatment related deaths and impaired quality of life show raltitrexed is inferior to infusional 5FU regimens in the palliative chemotherapy of advanced colorectal cancer (CRC): Final results of MRC CR06. UCL Discovery (University College London). 17 indexed citations
18.
Kerr, David & C S McArdle. (2000). Regional chemotherapy : theory and practice. 2 indexed citations
19.
Kerr, David, et al.. (1985). Phase I trials of poly(I,C) complexes in advanced cancer.. PubMed. 4(6). 640–9. 38 indexed citations
20.
Kerr, David, et al.. (1985). Pseudohyperkalaemia.. BMJ. 291(6499). 890–891. 13 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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