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.
Use of Canakinumab in the Cryopyrin-Associated Periodic Syndrome
2009627 citationsHelen J. Lachmann, Jasmin Kuemmerle‐Deschner et al.profile →
A new staging system for cardiac transthyretin amyloidosis
2017453 citationsJulian D. Gillmore, Marianna Fontana et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Helen J. Lachmann
Since
Specialization
Citations
This map shows the geographic impact of Helen J. Lachmann'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 Helen J. Lachmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Helen J. Lachmann more than expected).
Fields of papers citing papers by Helen J. Lachmann
This network shows the impact of papers produced by Helen J. Lachmann. 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 Helen J. Lachmann. The network helps show where Helen J. Lachmann may publish in the future.
Co-authorship network of co-authors of Helen J. Lachmann
This figure shows the co-authorship network connecting the top 25 collaborators of Helen J. Lachmann.
A scholar is included among the top collaborators of Helen J. Lachmann 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 Helen J. Lachmann. Helen J. Lachmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Martinez–Naharro, Ana, Amna Abdel‐Gadir, Thomas A. Treibel, et al.. (2016). Abstract 14407: Regression of Cardiac AL Amyloid by Cardiovascular Magnetic Resonance. Circulation. 134.2 indexed citations
13.
Kuemmerle‐Deschner, Jasmin, Seza Özen, Pascal N. Tyrrell, et al.. (2015). Development and Validation of Diagnostic Criteria for Cryopyrin Associated Periodic Syndromes. Arthritis & Rheumatism.1 indexed citations
Gillmore, JD, Kim Cocks, Prayman Sattianayagam, et al.. (2010). UK AL Amyloidosis Treatment Trial (UKATT) - a randomised study: lessons for future trial design. UCL Discovery (University College London).2 indexed citations
17.
Lachmann, Helen J., et al.. (2008). Treatment of cryopyrin associated periodic fever syndrome with a fully human anti-IL-1beta monoclonal antibody (ACZ885): results from a subcutaneous administration study. UCL Discovery (University College London).4 indexed citations
18.
Lachmann, Helen J., Michael McDermott, & Philip N. Hawkins. (2003). AA amyloidosis complicating the hereditary periodic fever syndromes.. UCL Discovery (University College London).8 indexed citations
19.
Lachmann, Helen J., et al.. (2002). Correlation of changes in nephelometric quantification of serum monoclonal free light chains following chemotherapy and outcome in 137 patients with systemic AL amyloidosis.. UCL Discovery (University College London).2 indexed citations
20.
Lachmann, Helen J., Laurence Lovat, Alexander Alanine, et al.. (2002). Targeted pharmacological depletion of serum amyloid P component for treatment of human amyloidosis. RePEc: Research Papers in Economics.1 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.