Papers

2

Total Citations

52

H-Index

2

About

Sajeda Ghassan Matar is an emerging researcher specializing in orthopedic surgery, with a particular focus on advanced surgical technologies and knee arthroplasty outcomes. Her work sits at the intersection of comparative surgical methodology and evidence-based medicine, examining how innovative robotic-assisted techniques stack up against conventional surgical approaches in knee replacement procedures. Matar's most significant contributions come through rigorous systematic reviews and meta-analyses that synthesize clinical evidence across multiple studies. Her 2023 landmark review comparing robotic-assisted versus conventional total knee arthroplasty, which has already garnered 39 citations, critically evaluates key clinical outcomes including operation time, range of motion, Oxford knee scores, and patient-reported functional measures. Complementing this work, her meta-analysis on unicompartmental knee surgery further establishes her authority in evaluating robotic surgical precision and its real-world implications, earning 13 citations since publication. What makes Matar's research particularly valuable to the orthopedic community is her commitment to providing clinicians with high-quality comparative evidence during a pivotal period when robotic-assisted surgery is rapidly transforming joint replacement practice. With over 50 combined citations within a single year, her contributions are already meaningfully shaping surgical decision-making and guiding the adoption of emerging technologies in orthopedic care.

Research Focus

Key Achievements

2
H-Index
2
Papers
52
Total Citations
26
Avg Citations/Paper
🏆 Most Cited Paper
A Systematic Review and Meta-Analysis of Conventional Versus Robotic-Assisted Total Knee Arthroplasty
39 citations · 2023
📈 Most Prolific Year: 2023 (2 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: Al-Azhar University, London North West Healthcare NHS Trust

Top Papers

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  2. 2

Key Collaborators

Contact & Links

Available for collaboration
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