Javier Muñoz

19.6k citations
133 papers · 1.8k · 1 hit paper · h-index 23

Impact in

Papers in

Javier Muñoz

124 papers receiving 1.8k citations

Javier Muñoz's Hit Papers

Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial 2022 · 186 citations
1860+1+2Years since publication50100150

Peers

Javier Muñoz
Comparison fields: 5 of 68
  • Genetics 545
  • Pathology and Forensic Medicine 886
  • Oncology 1.2k
  • Immunology 333
  • Hematology 154
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Citations per field
00.5×4.8×
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Citations per year

Countries citing papers authored by Javier Muñoz

Since Specialization
Citations

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

Fields of papers citing papers by Javier Muñoz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Javier Muñoz, 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 Javier Muñoz Line = papers co-authored together Javier Muñoz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 133 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial
Hit paper breakdown →
2022186
2 201993
3 201988
4 202073
5 202163
6 201462
7 201353
8 201451
9 202347
10 202045
11 202041
12 202038
13 202037
14 201236
15 202335
16 202135
17 201431
18 202429
19 202129
20 201328

About Javier Muñoz

Javier Muñoz is a scholar working on Oncology, Pathology and Forensic Medicine, Genetics, Immunology and Molecular Biology, having authored 133 papers that have together received 1.8k indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (74 papers), CAR-T cell therapy research (65 papers), Chronic Lymphocytic Leukemia Research (47 papers), Biosimilars and Bioanalytical Methods (12 papers), Chronic Myeloid Leukemia Treatments (12 papers), Cutaneous lymphoproliferative disorders research (11 papers), Integrated Circuits and Semiconductor Failure Analysis (10 papers) and Viral-associated cancers and disorders (8 papers). The work is most often cited by research in Genetics (545 citations), Pathology and Forensic Medicine (886 citations), Oncology (1.2k citations), Immunology (333 citations) and Hematology (154 citations). Javier Muñoz has collaborated with scholars based in United States, France and Australia. Frequent co-authors include Razelle Kurzrock, Filip Jankú, Sattva S. Neelapu, Stephen Opat, Constantine S. Tam, Olalekan O. Oluwole, Julio C. Chávez, Ellen Schlette, Sven de Vos and Philip R. Cohen. Their work appears in journals such as Blood, Journal of Clinical Oncology, Hematological Oncology, Transplantation and Cellular Therapy and Blood Advances.

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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