Eshita Sharma
Papers
3
Total Citations
17
H-Index
2
About
Eshita Sharma is an emerging surgical researcher whose work sits at the intersection of robotic-assisted surgery, minimally invasive techniques, and evidence-based medicine. Her research focuses on two primary domains: hepatopancreatic surgery and spinal surgery, with a particular emphasis on evaluating the comparative effectiveness of robotic and conventional surgical approaches through rigorous meta-analyses and systematic reviews. Sharma's most notable contributions include a 2025 meta-analysis and trial sequential analysis comparing robotic versus laparoscopic pancreatoduodenectomy — one of the most technically demanding abdominal procedures — alongside a comprehensive Bayesian network meta-analysis examining pedicle screw placement accuracy across freehand, fluoroscopic, navigation-guided, and robotic-assisted techniques in adolescent idiopathic scoliosis. Both papers have rapidly accumulated eight citations each, reflecting strong early academic uptake. Her third major work further extends this comparative lens to thoracolumbar fracture surgery, assessing the real-world utility of robotic-assisted minimally invasive instrumentation. What distinguishes Sharma's scholarship is her methodological sophistication — employing advanced statistical frameworks like Bayesian network meta-analysis and trial sequential analysis to synthesize complex surgical evidence. For students and clinicians navigating the evolving landscape of robotic surgery, her work offers timely, rigorous guidance on where innovation genuinely outperforms tradition.
Research Focus
Key Achievements
Top Papers
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