Papers

1

Total Citations

16

H-Index

1

About

Connie To is a surgical researcher whose work focuses on the adoption and evaluation of advanced minimally invasive techniques, particularly in the field of robotic gastrectomy. Her most-cited study, "Short-Term and Textbook Surgical Outcomes During the Implementation of a Robotic Gastrectomy Program" (2023), has garnered 16 citations, reflecting its timely contribution to the evidence base for robotic surgery. In this work, To provides a critical analysis of the learning curve and safety profile associated with transitioning to robotic-assisted procedures, offering practical insights for surgical teams. By emphasizing "textbook outcomes"—a composite measure of ideal postoperative recovery—she moves beyond traditional metrics to assess the true quality of care. To’s research is instrumental for surgeons and institutions considering the adoption of robotic platforms, as it balances the promise of innovation with the realities of implementation. Her contributions highlight a commitment to rigorous, patient-centered evaluation in surgical innovation, making her a valuable voice in the ongoing dialogue about technology in the operating room.

Research Focus

Key Achievements

1
H-Index
1
Papers
16
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Short-Term and Textbook Surgical Outcomes During the Implementation of a Robotic Gastrectomy Program
16 citations · 2023
📈 Most Prolific Year: 2023 (1 Papers)
🤝 Key Collaborators: 10
🏛 Institutions: The University of Texas MD Anderson Cancer Center

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 11 days ago