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Aim Robotic surgery is increasingly being adopted by gastrointestinal surgeons. The IDEAL Collaboration has recommended evaluation of the learning curve (LC) for such surgical innovations. It is not known how learning curves have been reported for robotic gastrointestinal procedures. The aim of this study was to summarise how the LCs were measured for three robotic gastrointestinal procedures: cholecystectomy, oesophagectomy and Roux-en-Y gastric bypass (RYGB). Method Three systematic reviews conducted by the trainee led RoboSurg Collaborative identified primary clinical research involving robotic cholecystectomy, oesophagectomy and RYGB. Articles were screened in duplicate by title, abstract and then full text. References to the LC were extracted and coded. The techniques used to measure the LC were summarised using descriptive statistics. Results 259 articles were identified, of which 56 (22%) actively measured the LC. The commonest surrogate marker for performance was operative time (N = 34, 63%). Several evaluated performances using the cumulative sum of the operative times (N = 8, 14%). Complications (N = 5, 8%) and time to complete a specific surgical step (N = 4, 7%) were also used. Some authors used multiple markers (N = 4, 7%). Cases were reported individually in 48 (81%) of the LCs, whereas they were grouped in 11 (19%). 19 authors (34%) provided graphical representation of the LC. Conclusions The reporting of LCs for robotic gastrointestinal surgery was heterogeneous and lacked standardisation. There was variation in choice of surrogate markers for performance, individual versus grouped case reporting, and graphical representations. To improve the utility of LC reports, the recommendations of the IDEAL Collaboration should be implemented.

1028 How Has the Learning Curve Been Measured in Robotic Gastrointestinal Surgery?