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.
Consort 2010 statement: extension to cluster randomised trials
20122.0k citationsMarion Campbell, Gilda Piaggio et al.BMJprofile →
CONSORT statement: extension to cluster randomised trials
20041.9k citationsMarion Campbell, Diana R Elbourne et al.BMJprofile →
Countries citing papers authored by Diana R Elbourne
Since
Specialization
Citations
This map shows the geographic impact of Diana R Elbourne'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 Diana R Elbourne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diana R Elbourne more than expected).
Fields of papers citing papers by Diana R Elbourne
This network shows the impact of papers produced by Diana R Elbourne. 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 Diana R Elbourne. The network helps show where Diana R Elbourne may publish in the future.
Co-authorship network of co-authors of Diana R Elbourne
This figure shows the co-authorship network connecting the top 25 collaborators of Diana R Elbourne.
A scholar is included among the top collaborators of Diana R Elbourne 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 Diana R Elbourne. Diana R Elbourne is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
McCambridge, Jim, John Witton, & Diana R Elbourne. (2013). Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects. Journal of Clinical Epidemiology. 67(3). 267–277.1659 indexed citations breakdown →
McDonald, Alison, Rochelle Knight, Marion Campbell, et al.. (2006). What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials. 7(1). 9–9.667 indexed citations breakdown →
Elbourne, Diana R, Douglas G. Altman, Julian P. T. Higgins, et al.. (2002). Meta-analyses involving cross-over trials: methodological issues. International Journal of Epidemiology. 31(1). 140–149.1683 indexed citations breakdown →
Campbell, Michael J., Allan Donner, & Diana R Elbourne. (2001). Preface. Statistics in Medicine. 20(3). 329–330.10 indexed citations
18.
Mitchell, Grant & Diana R Elbourne. (1993). The Salford Third Stage Trial. Oxytocin plus ergometrine versus oxytocin alone in the active management of the third stage of labor.. PubMed. Doc No 83. [2305 words; 32 paragraphs]–[2305 words; 32 paragraphs].23 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.