Single-port robotic colorectal surgery: a scoping review of outcome reporting and future directions for standardisation

单孔机器人结直肠手术:结果报告的范围界定综述及标准化的未来方向

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Abstract

BACKGROUND: Single-port (SP) robotic-assisted surgery is the latest development in minimally invasive colorectal surgery and may offer advantages such as improved cosmesis, reduced pain, and shorter hospital stay. However, evidence remains fragmented, and inconsistent outcome reporting limits comparison across studies and meaningful meta-analysis. This scoping review evaluated outcome reporting in SP robotic colorectal surgery to identify gaps and inform standardisation. METHODS: A scoping review was conducted in accordance with PRISMA-ScR guidelines. Ovid MEDLINE, Embase, and the Cochrane Library were searched from inception to January 2026. Studies reporting clinical outcomes of SP transabdominal robotic colorectal surgery or comparing SP with multi-port (MP) approaches were included. Data extraction assessed reporting completeness across patient demographics, operative details, intraoperative outcomes, postoperative recovery, and long-term follow-up. RESULTS: Nineteen studies met inclusion criteria, comprising 992 patients, of whom 610 underwent SP robotic resection. Eight studies were comparative and eleven non-comparative. Most were single-centre retrospective series from high-volume centres in the United States and South Korea. Outcome reporting was highly heterogeneous. Operating time, complications, and conversion rates were most consistently reported. In contrast, postoperative and patient-centred outcomes were inconsistently captured, including pain scores (4/19 studies), return to theatre (2/19), and follow-up interval (5/19). All studies reported complications, but only 9/19 used standardised grading systems. No study assessed health economic outcomes or cost-effectiveness. CONCLUSION: Outcome reporting in SP robotic colorectal surgery remains inconsistent and focused mainly on technical feasibility. Standardised core outcome sets are needed to support robust comparison, pooled analysis, and evidence-based adoption.

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