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.
Efficacy of apixaban when compared with warfarin in relation to renal function in patients with atrial fibrillation: insights from the ARISTOTLE trial
Countries citing papers authored by Michael G. Hanna
Since
Specialization
Citations
This map shows the geographic impact of Michael G. Hanna'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 Michael G. Hanna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael G. Hanna more than expected).
Fields of papers citing papers by Michael G. Hanna
This network shows the impact of papers produced by Michael G. Hanna. 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 Michael G. Hanna. The network helps show where Michael G. Hanna may publish in the future.
Co-authorship network of co-authors of Michael G. Hanna
This figure shows the co-authorship network connecting the top 25 collaborators of Michael G. Hanna.
A scholar is included among the top collaborators of Michael G. Hanna 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 Michael G. Hanna. Michael G. Hanna 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.
Ioannou, Adam, Sarah Cuddy, Sharmila Dorbala, et al.. (2025). The Last Decade in Cardiac Amyloidosis. JACC. Cardiovascular imaging. 18(4). 478–499.11 indexed citations
Jaffer, Fatima, et al.. (2012). Audit of unplanned admissions in neuromuscular patients: a collaborative audit. UCL Discovery (University College London).1 indexed citations
Allen, Elizabeth, Patrick Kiely, RG Cooper, et al.. (2005). Inter-rater reliability and validity of two disease activity assessment tools in patients with idiopathic inflammatory myositis (IIM). UCL Discovery (University College London).1 indexed citations
14.
Isenberg, David, Eliezer M. Van Allen, RG Cooper, et al.. (2002). Development of disease activity and damage indices for myositis: Further testing of four tools in adult onset patients.. UCL Discovery (University College London).2 indexed citations
15.
Davies, Nigel P., Clare Beesley, Perry Elliott, et al.. (2002). Intronic and missense mutations within the LAMP-2 gene in Danon disease (X linked vacuolar cardiomyopathy and myopathy). UCL Discovery (University College London).1 indexed citations
16.
Chinnaswamy, R., et al.. (2000). Physiochemical properties of ready-to-eat breakfast cereals. Cereal Foods World. 45(4). 164–168.78 indexed citations
Hanna, Michael G., et al.. (1976). Autogenous immunity to endogenous RNA tumor virus: humoral immune response to virus envelope antigens.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 36(2 pt 2). 608–14.5 indexed citations
Hanna, Michael G., A K Szakal, & R. L. Tyndall. (1970). Histoproliferative effect of Rauscher leukemia virus on lymphatic tissue: histological and ultrastructural studies of germinal centers and their relation to leukemogenesis.. PubMed. 30(6). 1748–63.29 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.