Eugene Kalinsky

Sechenov University

Papers

3

Total Citations

12

H-Index

2

About

Eugene Kalinsky is an emerging orthopedic surgery researcher whose work focuses on the rapidly evolving field of total knee arthroplasty (TKA), with particular emphasis on surgical precision techniques and alignment philosophies. His research sits at the intersection of conventional, computer-assisted, and robotic surgical approaches, positioning him as a contributor to one of the most transformative areas in modern joint replacement surgery. Kalinsky's most-cited work, a 2024 pilot clinical trial comparing conventional, computer-assisted, and robotic TKA techniques (9 citations), provides early comparative evidence that is increasingly valuable as surgical teams navigate technology adoption decisions. His subsequent investigations into kinematic alignment within robotic TKA frameworks demonstrate a focused effort to understand how patient-specific anatomy can be better preserved during surgery, moving beyond traditional mechanical alignment paradigms. His 2025 randomized controlled trial comparing mechanical versus restrictive kinematic alignment in robotic-assisted TKA represents a methodologically rigorous contribution, addressing a genuinely contested clinical question. Though early in his publication career, Kalinsky's consistent focus on alignment strategy and emerging surgical technologies signals a researcher dedicated to improving functional outcomes for patients suffering from debilitating knee osteoarthritis. His work will likely grow in relevance as robotic TKA adoption accelerates globally.

Research Focus

Key Achievements

2
H-Index
3
Papers
12
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Short-Term Outcomes of Total Knee Arthroplasty Using a Conventional, Computer-Assisted, and Robotic Technique: A Pilot Clinical Trial
9 citations · 2024
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: Sechenov University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 3 days ago