M. Omizo

996 total citations
10 papers, 680 citations indexed

About

M. Omizo is a scholar working on Orthopedics and Sports Medicine, Oncology and Molecular Biology. According to data from OpenAlex, M. Omizo has authored 10 papers receiving a total of 680 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Orthopedics and Sports Medicine, 4 papers in Oncology and 3 papers in Molecular Biology. Recurrent topics in M. Omizo's work include Bone health and osteoporosis research (6 papers), Bone health and treatments (3 papers) and Estrogen and related hormone effects (2 papers). M. Omizo is often cited by papers focused on Bone health and osteoporosis research (6 papers), Bone health and treatments (3 papers) and Estrogen and related hormone effects (2 papers). M. Omizo collaborates with scholars based in United States, Canada and South Africa. M. Omizo's co-authors include Erik Fink Eriksen, G. P. Dalsky, Francisco Bandeira, Michael R. McClung, Javier San Martín, Roberto Civitelli, Paul D. Miller, David W. Donley, John H. Krege and Elliott N. Schwartz and has published in prestigious journals such as The Journal of Clinical Endocrinology & Metabolism, Journal of Bone and Mineral Research and Bone.

In The Last Decade

M. Omizo

10 papers receiving 658 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Omizo United States 7 510 432 325 68 52 10 680
Sadaoki Sakai Japan 13 207 0.4× 191 0.4× 290 0.9× 54 0.8× 6 0.1× 27 555
Fredrik Stiger Sweden 12 136 0.3× 126 0.3× 235 0.7× 17 0.3× 22 0.4× 13 436
Tsuyoshi Asano Japan 10 105 0.2× 65 0.2× 121 0.4× 108 1.6× 18 0.3× 33 388
Sandra Jastrzebski United States 10 83 0.2× 198 0.5× 270 0.8× 26 0.4× 11 0.2× 13 493
Brianne S Thicke United States 7 132 0.3× 107 0.2× 258 0.8× 39 0.6× 4 0.1× 7 482
Yasuo Kuroki Japan 13 42 0.1× 101 0.2× 149 0.5× 31 0.5× 39 0.8× 20 449
Rosie Head United Kingdom 7 53 0.1× 71 0.2× 184 0.6× 87 1.3× 18 0.3× 8 444
David Reid United Kingdom 3 345 0.7× 156 0.4× 273 0.8× 70 1.0× 8 0.2× 5 574
Alexander Gawlik Germany 7 29 0.1× 78 0.2× 250 0.8× 52 0.8× 15 0.3× 8 473
Elke Piters Belgium 11 131 0.3× 154 0.4× 418 1.3× 33 0.5× 3 0.1× 19 543

Countries citing papers authored by M. Omizo

Since Specialization
Citations

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

Fields of papers citing papers by M. Omizo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Omizo

This figure shows the co-authorship network connecting the top 25 collaborators of M. Omizo. A scholar is included among the top collaborators of M. Omizo 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 M. Omizo. M. Omizo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Yoshioka, Hiroshi, Anita Lakatos, Marjorie R. Grafe, et al.. (2018). Novel valosin-containing protein mutations associated with multisystem proteinopathy. Neuromuscular Disorders. 28(6). 491–501. 13 indexed citations
2.
Surampalli, Abhilasha, Namita Goyal, Annabel Wang, et al.. (2017). Genotype‐phenotype study in patients with valosin‐containing protein mutations associated with multisystem proteinopathy. Clinical Genetics. 93(1). 119–125. 86 indexed citations
3.
Genant, Harry K., Klaus Engelke, David A. Hanley, et al.. (2010). Denosumab improves density and strength parameters as measured by QCT of the radius in postmenopausal women with low bone mineral density. Bone. 47(1). 131–139. 58 indexed citations
4.
Utian, Wulf H., Michael A. Bolognese, Robert Feldman, et al.. (2009). ARZOXIFENE IN POSTMENOPAUSAL WOMEN WITH NORMAL OR LOW BONE MASS. Maturitas. 63. S91–S91. 2 indexed citations
5.
Genant, H K, Klaus Engelke, Jacques P. Brown, et al.. (2009). 67 Denosumab Improves Forearm Densitometric, Geometric and Strength Indices as Measured by QCT in Postmenopausal Women With Low BMD. Journal of Clinical Densitometry. 12(1). 119–120. 1 indexed citations
6.
Bolognese, Michael A., John H. Krege, Wulf H. Utian, et al.. (2009). Effects of Arzoxifene on Bone Mineral Density and Endometrium in Postmenopausal Women with Normal or Low Bone Mass. The Journal of Clinical Endocrinology & Metabolism. 94(7). 2284–2289. 25 indexed citations
8.
Wehren, Lois E., et al.. (2006). Skeletal Consequences of Hormone Therapy Discontinuance: A Systematic Review. Obstetrical & Gynecological Survey. 61(2). 115–124. 11 indexed citations
9.
McClung, Michael R., Javier San Martín, Paul D. Miller, et al.. (2005). Opposite Bone Remodeling Effects of Teriparatide and Alendronate in Increasing Bone Mass. Archives of Internal Medicine. 165(15). 1762–1762. 340 indexed citations
10.
Deal, Chad, M. Omizo, Elliott N. Schwartz, et al.. (2005). Combination Teriparatide and Raloxifene Therapy for Postmenopausal Osteoporosis: Results From a 6-Month Double-Blind Placebo-Controlled Trial. Journal of Bone and Mineral Research. 20(11). 1905–1911. 143 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.

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