Sarah Sampson

1.7k total citations
10 papers, 472 citations indexed

About

Sarah Sampson is a scholar working on Oncology, Genetics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sarah Sampson has authored 10 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 7 papers in Genetics and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sarah Sampson's work include Global Cancer Incidence and Screening (7 papers), BRCA gene mutations in cancer (7 papers) and Cancer Risks and Factors (5 papers). Sarah Sampson is often cited by papers focused on Global Cancer Incidence and Screening (7 papers), BRCA gene mutations in cancer (7 papers) and Cancer Risks and Factors (5 papers). Sarah Sampson collaborates with scholars based in United Kingdom, United States and India. Sarah Sampson's co-authors include Anthony Howell, D. Gareth Evans, Jack Cuzick, Elaine F. Harkness, Susan Astley, Adam R. Brentnall, William G. Newman, Helen Byers, Elke M. van Veen and Paula Stavrinos and has published in prestigious journals such as British Journal of Cancer, International Journal of Cancer and BMC Public Health.

In The Last Decade

Sarah Sampson

10 papers receiving 461 citations

Peers

Sarah Sampson

ONCOL

GENET

AI

PRM

CR

Daniëlle van der Waal Netherlands

Vicky Majpruz Canada

Shivaani Mariapun Malaysia

Amber N. Hurson United Kingdom

Ursula Beetles United Kingdom

Robert A Smith United States

D Morrone Italy

Debra A. Mangino United States

Elke M. van Veen United Kingdom

Kimberley Hill Canada

Daniëlle van der Waal Netherlands

Sarah Sampson

357 ×1.2

ONCOL

135 ×0.6

GENET

106 ×1.0

AI

128 ×1.4

PRM

63 ×0.9

CR

Citations per year, relative to Sarah Sampson Sarah Sampson (= 1×) peers Daniëlle van der Waal

Countries citing papers authored by Sarah Sampson

Since Specialization

Citations

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

Fields of papers citing papers by Sarah Sampson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Sampson

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Evans, D. Gareth, Elke M. van Veen, Elaine F. Harkness, et al.. (2022). Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel. Genetics in Medicine. 24(7). 1485–1494. 30 indexed citations

2.

Evans, D. Gareth, Elaine F. Harkness, Adam R. Brentnall, et al.. (2019). Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants. Breast Cancer Research and Treatment. 176(1). 141–148. 49 indexed citations

3.

Brentnall, Adam R., Elke M. van Veen, Elaine F. Harkness, et al.. (2019). A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. International Journal of Cancer. 146(8). 2122–2129. 39 indexed citations

4.

French, David, Anthony Howell, Michelle Harvie, et al.. (2018). Psychological impact of providing women with personalised 10-year breast cancer risk estimates. British Journal of Cancer. 118(12). 1648–1657. 40 indexed citations

5.

Evans, D. Gareth, Adam R. Brentnall, Michelle Harvie, et al.. (2018). Breast cancer risk in a screening cohort of Asian and white British/Irish women from Manchester UK. BMC Public Health. 18(1). 178–178. 17 indexed citations

6.

Veen, Elke M. van, Adam R. Brentnall, Helen Byers, et al.. (2018). Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction. JAMA Oncology. 4(4). 476–476. 97 indexed citations

7.

Brentnall, Adam R., Elaine F. Harkness, Susan Astley, et al.. (2015). Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Research. 17(1). 147–147. 175 indexed citations

8.

Møller, Pål, Kukatharmini Tharmaratnam, Anthony Howell, et al.. (2015). Tumour characteristics and survival in familial breast cancer prospectively diagnosed by annual mammography. Breast Cancer Research and Treatment. 152(1). 87–94. 2 indexed citations

9.

Schneider, Rebekka K., et al.. (2005). Magnetic resonance imaging evaluation of horses with lameness problems.. 21–36. 15 indexed citations

10.

Thomas, R., et al.. (1996). An analysis of surveillance for stage I combined teratoma - seminoma of the testis. British Journal of Cancer. 74(1). 59–62. 8 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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