Idit Lavi

2.9k citations
90 papers · 2.1k · h-index 28

Impact in

Papers in

Idit Lavi

86 papers receiving 2.1k citations

Peers

Idit Lavi
Comparison fields: 5 of 125
  • Pathology and Forensic Medicine 410
  • Rheumatology 313
  • Endocrinology, Diabetes and Metabolism 251
  • Biological Psychiatry 37
  • Endocrine and Autonomic Systems 93
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Denis Baird United Kingdom
Vanessa Y. Tan United Kingdom
Barna Vásárhelyi Hungary
B. Seriolo Italy
Francesco Ursini Italy
Benjamin Woolf United Kingdom
Tivadar Tulassay Hungary
Carmen Pizzorni Italy
Veronika Skrivankova Switzerland
Yu‐Jih Su Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by Idit Lavi

Since Specialization
Citations

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

Fields of papers citing papers by Idit Lavi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Idit Lavi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Idit Lavi Line = papers co-authored together Idit Lavi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015108
2 2008106
3 201199
4 201197
5 201880
6 201278
7 201774
8 201367
9 201757
10 201355
11 200555
12 201850
13 200748
14 201744
15 202144
16 200543
17 201241
18 199241
19 200640
20 201938

About Idit Lavi

Idit Lavi is a scholar working on Rheumatology, Pathology and Forensic Medicine, Endocrinology, Diabetes and Metabolism, Cardiology and Cardiovascular Medicine and Surgery, having authored 90 papers that have together received 2.1k indexed citations. Recurring topics across this work include Spondyloarthritis Studies and Treatments (11 papers), Multiple Sclerosis Research Studies (9 papers), Thyroid Disorders and Treatments (5 papers), Orthopedic Surgery and Rehabilitation (4 papers), Systemic Lupus Erythematosus Research (3 papers), Psoriasis: Treatment and Pathogenesis (3 papers), Vitamin D Research Studies (3 papers) and Autoimmune and Inflammatory Disorders Research (3 papers). The work is most often cited by research in Pathology and Forensic Medicine (410 citations), Rheumatology (313 citations), Endocrinology, Diabetes and Metabolism (251 citations), Biological Psychiatry (37 citations) and Endocrine and Autonomic Systems (93 citations). Idit Lavi has collaborated with scholars based in Israel, United States and Canada. Frequent co-authors include Gad Rennert, Reuven Mader, Walid Saliba, Rafael Luboshitzky, Naomi Gronich, Daniel Golan, Ariel Miller, Devy Zisman, Mohammed Adawi and Hedy S. Rennert. Their work appears in journals such as The Journal of Rheumatology, Clinical Rheumatology, Arthritis Research & Therapy, Multiple Sclerosis Journal and Endocrine Practice.

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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