Vanessa N. Palter

2.0k citations
41 papers · 1.4k indexed · h-index 16

Vanessa N. Palter

39 papers receiving 1.4k citations

Peers

Vanessa N. Palter
Comparison fields: 5 of 97
  • Surgery 995
  • Neurology 216
  • Physiology 353
  • Biomedical Engineering 515
  • Cardiology and Cardiovascular Medicine 251
Replace Matthew A. Kirkman with:
Matthew A. Kirkman United Kingdom
Abdulrahman J. Sabbagh Saudi Arabia
Faizal Haji Canada
Alfred Marc Iloreta United States
Michael A. Bohl United States
Edward Benzel United States
Jacob K. Greenberg United States
Steven J. Kasten United States
Ibrahim Obeid France
Nishant Ganesh Kumar United States
Vanessa N. Palter relative to Matthew A. Kirkman United Kingdom Matthew A. Kirkman's profile →
Citations per field
00.5×3.6×
Matthew A. Kirkman · 1×
Citations per year

Countries citing papers authored by Vanessa N. Palter

Since Specialization
Citations

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

Fields of papers citing papers by Vanessa N. Palter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vanessa N. Palter. 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 Vanessa N. Palter. The network helps show where Vanessa N. Palter may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20242
2 20223
3 20229
4 20228
5 20215
6 20201
7 20204
8 20207
9 201810
10 20188
11 20152
12 201456
13 201220
14 2012133
15 201282
16 201151
17 201043
18 201082
19 2004224
20 20046

About Vanessa N. Palter

Vanessa N. Palter is a scholar working on Emergency Medical Services, Pharmacy and Cardiology and Cardiovascular Medicine, having authored 41 papers that have together received 1.4k indexed citations. Recurring topics across this work include Surgical Simulation and Training (21 papers), Cardiac, Anesthesia and Surgical Outcomes (14 papers), Anatomy and Medical Technology (12 papers), Simulation-Based Education in Healthcare (9 papers), Patient Safety and Medication Errors (6 papers), Colorectal Cancer Surgical Treatments (5 papers), Medical Malpractice and Liability Issues (4 papers) and Colorectal Cancer Screening and Detection (3 papers). The work is most often cited by research in Surgery (995 citations), Neurology (216 citations) and Physiology (353 citations). Vanessa N. Palter has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Teodor Grantcharov, Neil Orzech, Richard K. Reznick, Rajesh Aggarwal, Jonathan O. Dostrovsky, William D. Hutchison, Andrés M. Lozano, Mohammed Filali, Helen MacRae and Adrian Harvey. Their work appears in journals such as SHILAP Revista de lepidopterología, Annals of Surgery and Experimental Brain Research.

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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