Sushil Kumar

56 papers receiving 477 citations

Peers

Sushil Kumar
Comparison fields: 5 of 118
  • Modeling and Simulation 43
  • Applied Microbiology and Biotechnology 16
  • Gastroenterology 37
  • Endocrinology 20
  • Molecular Medicine 19
Replace Wan‐Ting Hsu with:
Wan‐Ting Hsu Taiwan
Xun Huang China
Saurabh Gombar United States
Hanley J. Ho Singapore
Keith E. Willard United States
Ramin Sami Iran
Shiran Shetty India
Fatma Bozkurt Türkiye
Christopher W Farnsworth United States
Kate Honeyford United Kingdom
Sushil Kumar relative to Wan‐Ting Hsu Taiwan Wan‐Ting Hsu's profile →
Citations per field
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Wan‐Ting Hsu · 1×
Citations per year

Countries citing papers authored by Sushil Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Sushil Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020114
2 201547
3 201038
4 201536
5 200933
6 201517
7 201314
8 200813
9 200910
10 20219
11 20158
12 20058
13 20118
14 20218
15 20118
16
Seroprevalence of IgG against SARS-CoV-2 and its determinants among healthcare workers of a COVID-19 dedicated hospital of India.
20218
17 20177
18
Intrathecal fentanyl with low dose hyperbaric bupivacaine for caesarean delivery in patients with pregnancy induced Hypertension
20057
19 20116
20 20206

About Sushil Kumar

Sushil Kumar is a scholar working on Surgery, Epidemiology, Obstetrics and Gynecology, Public Health, Environmental and Occupational Health and Cardiology and Cardiovascular Medicine, having authored 67 papers that have together received 501 indexed citations. Recurring topics across this work include Cardiac, Anesthesia and Surgical Outcomes (4 papers), COVID-19 epidemiological studies (4 papers), Pregnancy and preeclampsia studies (4 papers), Maternal and fetal healthcare (4 papers), Hernia repair and management (3 papers), Anesthesia and Pain Management (3 papers), Liver Disease Diagnosis and Treatment (3 papers) and Uterine Myomas and Treatments (3 papers). The work is most often cited by research in Modeling and Simulation (43 citations), Applied Microbiology and Biotechnology (16 citations), Gastroenterology (37 citations), Endocrinology (20 citations) and Molecular Medicine (19 citations). Sushil Kumar has collaborated with scholars based in India, United States and Nepal. Frequent co-authors include Sunita Tiwari, Kalpna Guleria, Anil Kumar Verma, Anil Agarwal, Ketoki Kapila, Vineet Ahuja, Basile Njei, Eugene J Kongnyuy, Vikas Sachdev and Tsering Stobdan. Their work appears in journals such as Journal of Anaesthesiology Clinical Pharmacology, Cochrane Database of Systematic Reviews, Diagnosis, Pediatric Anesthesia and Frontiers in Microbiology.

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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