Justin E. Tang

545 total citations
35 papers, 341 citations indexed

About

Justin E. Tang is a scholar working on General Health Professions, Surgery and Pathology and Forensic Medicine. According to data from OpenAlex, Justin E. Tang has authored 35 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in General Health Professions, 12 papers in Surgery and 8 papers in Pathology and Forensic Medicine. Recurrent topics in Justin E. Tang's work include Patient Satisfaction in Healthcare (11 papers), Spine and Intervertebral Disc Pathology (8 papers) and Artificial Intelligence in Healthcare and Education (7 papers). Justin E. Tang is often cited by papers focused on Patient Satisfaction in Healthcare (11 papers), Spine and Intervertebral Disc Pathology (8 papers) and Artificial Intelligence in Healthcare and Education (7 papers). Justin E. Tang collaborates with scholars based in United States. Justin E. Tang's co-authors include Jun Kim, Samuel K. Cho, Bashar Zaidat, Varun Arvind, Rami Rajjoub, Akiro H. Duey, Christopher A. White, Eric Geng, Brian Cho and Jonathan Markowitz and has published in prestigious journals such as Spine, The Journal of Urology and Journal of Vascular Surgery.

In The Last Decade

Justin E. Tang

33 papers receiving 338 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Justin E. Tang United States 12 169 86 83 57 51 35 341
Rohun Gupta United States 9 236 1.4× 50 0.6× 111 1.3× 60 1.1× 98 1.9× 33 379
Isabel Herzog United States 7 232 1.4× 41 0.5× 56 0.7× 61 1.1× 101 2.0× 25 314
Bashar Zaidat United States 11 198 1.2× 36 0.4× 93 1.1× 53 0.9× 54 1.1× 29 325
Rami Rajjoub United States 9 143 0.8× 29 0.3× 81 1.0× 42 0.7× 40 0.8× 26 283
Bryan S. Crook United States 8 117 0.7× 39 0.5× 96 1.2× 18 0.3× 38 0.7× 22 233
Michael Eppler United States 8 214 1.3× 57 0.7× 34 0.4× 18 0.3× 57 1.1× 18 310
Muhammad Saqib Pakistan 9 72 0.4× 45 0.5× 39 0.5× 55 1.0× 56 1.1× 27 339
Sebastian Fritsch Germany 6 152 0.9× 34 0.4× 28 0.3× 45 0.8× 64 1.3× 27 327
Patricia García United States 8 138 0.8× 37 0.4× 80 1.0× 56 1.0× 34 0.7× 22 326

Countries citing papers authored by Justin E. Tang

Since Specialization
Citations

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

Fields of papers citing papers by Justin E. Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Justin E. Tang. 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 Justin E. Tang. The network helps show where Justin E. Tang may publish in the future.

Co-authorship network of co-authors of Justin E. Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Justin E. Tang. A scholar is included among the top collaborators of Justin E. Tang 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 Justin E. Tang. Justin E. Tang 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.
Zaidat, Bashar, et al.. (2024). An analysis of ChatGPT recommendations for the diagnosis and treatment of cervical radiculopathy. Journal of Neurosurgery Spine. 41(3). 385–395. 10 indexed citations
2.
Zaidat, Bashar, Rami Rajjoub, Akiro H. Duey, et al.. (2024). Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery. Neurospine. 21(1). 128–146. 17 indexed citations
3.
Cho, Samuel K., Rami Rajjoub, Bashar Zaidat, et al.. (2024). P234. ChatGPT versus NASS Clinical Guidelines for Degenerative Spondylolisthesis: a comparative analysis. The Spine Journal. 24(9). S179–S180. 2 indexed citations
4.
Rajjoub, Rami, Akiro H. Duey, Bashar Zaidat, et al.. (2024). ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis. European Spine Journal. 33(11). 4182–4203. 16 indexed citations
5.
Goldstein, Jonathan, Justin E. Tang, Michelle Tran, et al.. (2023). How Are We Doing? A Scoping Review of Published Patient-Centered Outcomes Research in United States Student-Run Free Clinics. Teaching and Learning in Medicine. 36(5). 624–636. 3 indexed citations
6.
Duey, Akiro H., Bashar Zaidat, Rami Rajjoub, et al.. (2023). Thromboembolic prophylaxis in spine surgery: an analysis of ChatGPT recommendations. The Spine Journal. 23(11). 1684–1691. 47 indexed citations
7.
Zaidat, Bashar, Justin E. Tang, Varun Arvind, et al.. (2023). Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?. Global Spine Journal. 14(7). 2022–2030. 18 indexed citations
8.
Isleem, Ula, Bashar Zaidat, Eric Geng, et al.. (2023). Can generative artificial intelligence pass the orthopaedic board examination?. Journal of Orthopaedics. 53. 27–33. 15 indexed citations
9.
Duey, Akiro H., Christopher A. White, Justin E. Tang, et al.. (2023). Diabetes increases risk for readmission and infection after shoulder arthroplasty: A national readmissions study of 113,713 patients. Journal of Orthopaedics. 38. 25–29. 11 indexed citations
10.
Geng, Eric, Jonathan S. Gal, Jun Kim, et al.. (2023). Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning. European Spine Journal. 32(6). 2149–2156. 6 indexed citations
11.
Tang, Justin E., et al.. (2022). How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments. Global Spine Journal. 13(8). 2107–2114. 12 indexed citations
12.
Cho, Brian, Eric Geng, Aly Valliani, et al.. (2022). Understanding Artificial Intelligence and Predictive Analytics. JBJS Reviews. 10(3). 11 indexed citations
13.
Tang, Justin E., Alexander J. Schüpper, Christopher A. White, et al.. (2022). Trends in Online Patient Perspectives of Neurosurgeons: A Sentiment Analysis. 3(4). 3 indexed citations
14.
Tang, Justin E., et al.. (2022). How are Patients Describing You Online? A Natural Language Processing Driven Sentiment Analysis of Online Reviews on CSRS Surgeons. Clinical Spine Surgery A Spine Publication. 36(2). E107–E113. 6 indexed citations
15.
Tang, Justin E., et al.. (2022). Trends in the Charges and Utilization of Computer-Assisted Navigation in Cervical and Thoracolumbar Spinal Surgery. Asian Spine Journal. 16(5). 625–633. 5 indexed citations
16.
Tang, Justin E., et al.. (2022). Sentiment Analysis of Online Patient-written Reviews of Vascular Surgeons. Journal of Vascular Surgery. 75(6). e319–e320. 2 indexed citations
17.
Tang, Justin E., et al.. (2022). Building better pediatric surgeons: A sentiment analysis of online physician review websites. Journal of Children s Orthopaedics. 16(6). 498–504. 5 indexed citations
18.
19.
Tang, Justin E., Varun Arvind, Christopher A. White, et al.. (2021). Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online. Hand. 18(5). 854–860. 16 indexed citations
20.
Arvind, Varun, et al.. (2021). Scoliosis surgery in social media: a natural language processing approach to analyzing the online patient perspective. Spine Deformity. 10(2). 239–246. 12 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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