Kevin M. Hellman

1.3k total citations
45 papers, 903 citations indexed

About

Kevin M. Hellman is a scholar working on Public Health, Environmental and Occupational Health, Reproductive Medicine and Physiology. According to data from OpenAlex, Kevin M. Hellman has authored 45 papers receiving a total of 903 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Public Health, Environmental and Occupational Health, 15 papers in Reproductive Medicine and 11 papers in Physiology. Recurrent topics in Kevin M. Hellman's work include Menstrual Health and Disorders (24 papers), Endometriosis Research and Treatment (15 papers) and Pain Mechanisms and Treatments (9 papers). Kevin M. Hellman is often cited by papers focused on Menstrual Health and Disorders (24 papers), Endometriosis Research and Treatment (15 papers) and Pain Mechanisms and Treatments (9 papers). Kevin M. Hellman collaborates with scholars based in United States, Bulgaria and Belgium. Kevin M. Hellman's co-authors include Frank F. Tu, Ted Abel, Folabomi A. Oladosu, Ming Ouyang, Steven Thomas, Peggy Mason, Sigrid C. Veasey, Allan I Pack, Julie A. Blendy and James W. Griffith and has published in prestigious journals such as Journal of Neuroscience, SHILAP Revista de lepidopterología and Journal of Neurophysiology.

In The Last Decade

Kevin M. Hellman

42 papers receiving 894 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kevin M. Hellman United States 17 341 270 184 180 156 45 903
Angie M. Cason United States 12 95 0.3× 283 1.0× 126 0.7× 123 0.7× 341 2.2× 16 727
Silvia Begliuomini Italy 15 155 0.5× 116 0.4× 260 1.4× 124 0.7× 47 0.3× 18 875
P. Clément France 20 70 0.2× 124 0.5× 105 0.6× 186 1.0× 183 1.2× 61 1.4k
J. Bennie United Kingdom 20 160 0.5× 68 0.3× 212 1.2× 156 0.9× 126 0.8× 38 1.1k
P Marrama Italy 21 119 0.3× 81 0.3× 208 1.1× 344 1.9× 221 1.4× 71 1.5k
Yan Rao United States 13 73 0.2× 330 1.2× 276 1.5× 68 0.4× 480 3.1× 18 1.2k
Susann Scherag Germany 17 110 0.3× 149 0.6× 50 0.3× 59 0.3× 209 1.3× 19 821
Gilberto Luiz Sanvitto Brazil 21 91 0.3× 76 0.3× 131 0.7× 144 0.8× 109 0.7× 39 1.1k
Kenneth G. Onishi United States 9 204 0.6× 66 0.2× 92 0.5× 28 0.2× 161 1.0× 15 758

Countries citing papers authored by Kevin M. Hellman

Since Specialization
Citations

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

Fields of papers citing papers by Kevin M. Hellman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kevin M. Hellman

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin M. Hellman. A scholar is included among the top collaborators of Kevin M. Hellman 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 Kevin M. Hellman. Kevin M. Hellman 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.
Kyathanahalli, Chandrashekara, Frank F. Tu, & Kevin M. Hellman. (2025). Inflammatory Mechanisms of Dysmenorrhea: Novel Insights From Menstrual Effluent in an Adolescent Cohort. BJOG An International Journal of Obstetrics & Gynaecology. 132(11). 1626–1634.
2.
Tu, Frank F., et al.. (2024). Ultrasound and magnetic resonance imaging–based investigation of the role of perfusion and oxygen availability in menstrual pain. American Journal of Obstetrics and Gynecology. 230(5). 553.e1–553.e14. 1 indexed citations
3.
Tu, Frank F., et al.. (2024). A multidimensional appraisal of early menstrual pain experience. American Journal of Obstetrics and Gynecology. 230(5). 550.e1–550.e10. 5 indexed citations
4.
5.
Ehrenpreis, Eli D., et al.. (2022). Menstrual Cycle Variation in MRI-Based Quantification of Intraluminal Gas in Women With and Without Dysmenorrhea. SHILAP Revista de lepidopterología. 3. 720141–720141. 2 indexed citations
6.
Tu, Frank F., et al.. (2021). Cortical mechanisms of visual hypersensitivity in women at risk for chronic pelvic pain. Pain. 163(6). 1035–1048. 5 indexed citations
7.
Oladosu, Folabomi A., et al.. (2020). Low Serum Oxytocin Concentrations Are Associated with Painful Menstruation. Reproductive Sciences. 27(2). 668–674. 8 indexed citations
8.
Oladosu, Folabomi A., et al.. (2019). Persistent autonomic dysfunction and bladder sensitivity in primary dysmenorrhea. Scientific Reports. 9(1). 2194–2194. 16 indexed citations
9.
Hellman, Kevin M., et al.. (2018). Identification of experimental bladder sensitivity among dysmenorrhea sufferers. American Journal of Obstetrics and Gynecology. 219(1). 84.e1–84.e8. 25 indexed citations
10.
Hellman, Kevin M., et al.. (2018). Cine MRI during spontaneous cramps in women with menstrual pain. American Journal of Obstetrics and Gynecology. 218(5). 506.e1–506.e8. 15 indexed citations
11.
Oladosu, Folabomi A., et al.. (2018). Abdominal skeletal muscle activity precedes spontaneous menstrual cramping pain in primary dysmenorrhea. American Journal of Obstetrics and Gynecology. 219(1). 91.e1–91.e7. 14 indexed citations
12.
Oladosu, Folabomi A., Frank F. Tu, & Kevin M. Hellman. (2017). Nonsteroidal antiinflammatory drug resistance in dysmenorrhea: epidemiology, causes, and treatment. American Journal of Obstetrics and Gynecology. 218(4). 390–400. 133 indexed citations
13.
Tu, Frank F., et al.. (2013). The association of dysmenorrhea with noncyclic pelvic pain accounting for psychological factors. American Journal of Obstetrics and Gynecology. 209(5). 422.e1–422.e10. 51 indexed citations
14.
Tu, Frank F., Kevin M. Hellman, & Miroslav Bačkonja. (2011). Gynecologic management of neuropathic pain. American Journal of Obstetrics and Gynecology. 205(5). 435–443. 17 indexed citations
15.
Hellman, Kevin M., Pepe J. Hernandez, Alice Park, & Ted Abel. (2010). Genetic Evidence for a Role for Protein Kinase A in the Maintenance of Sleep and Thalamocortical Oscillations. SLEEP. 33(1). 19–28. 27 indexed citations
16.
Hellman, Kevin M. & Ted Abel. (2007). Fear conditioning increases NREM sleep.. Behavioral Neuroscience. 121(2). 310–323. 46 indexed citations
17.
Hellman, Kevin M., Thaddeus S. Brink, & Peggy Mason. (2007). Activity of Murine Raphe Magnus Cells Predicts Tachypnea and On-Going Nociceptive Responsiveness. Journal of Neurophysiology. 98(6). 3121–3133. 12 indexed citations
18.
Brink, Thaddeus S., et al.. (2006). Raphe Magnus Neurons Help Protect Reactions to Visceral Pain From Interruption by Cutaneous Pain. Journal of Neurophysiology. 96(6). 3423–3432. 23 indexed citations
19.
Wood, Marcelo A., Carolina Isiegas, Joel M. Stein, et al.. (2006). Differential transcriptional response to nonassociative and associative components of classical fear conditioning in the amygdala and hippocampus. Learning & Memory. 13(2). 135–142. 44 indexed citations
20.
Lytton, William W., Kevin M. Hellman, & Thomas P. Sutula. (1998). Computer models of hippocampal circuit changes of the kindling model of epilepsy. Artificial Intelligence in Medicine. 13(1-2). 81–97. 28 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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