ArticleJournal of robotic surgery2023
The robotic learning curve for a newly appointed colorectal surgeon.
Article in Journal of robotic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 5 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Comparison of laparoscopic and robotic surgery for inflammatory bowel disease: a systematic review and meta-analysis.Surgical endoscopy · 2026Pooled it
- Comparing surgical outcomes of robotic and laparoscopic or open ileal pouch-anal anastomosis: a systematic review and meta-analysis.Journal of robotic surgery · 2025Pooled it
- Learning curve of robotic colectomy: a systematic review and meta-analysis of surgical proficiency, outcomes, and training protocols.Journal of robotic surgery · 2025Pooled it
- Minimally Invasive Surgery for Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis of Robotic Versus Laparoscopic Surgical Techniques.Journal of Crohn's & colitis · 2024Pooled it
- Learning Curve in Robotic Colorectal Surgery.JSLS : Journal of the Society of Laparoendoscopic SurgeonsPooled it
- Global research trends in robotic surgery for rectal cancer: a bibliometric and science-mapping analysis.Journal of robotic surgery · 2026Review
- Immersive robotic colorectal training in the United Kingdom is safe and efficient.Annals of the Royal College of Surgeons of England · 2025Article
- The evolution of training in robotic colorectal surgery.Journal of robotic surgery · 2025Review
- Learning Curve on Lymph Nodes Retrieval and Postoperative Length of Hospital Stay in Robotic Rectal Cancer: A Retrospective, Observational Study.Health science reports · 2025Article
- The training pathway for residents: 'Robotic Curriculum for young Surgeons' (RoCS) does not impair patient outcome during implementation into clinical routine.Journal of robotic surgery · 2024Article
- Robotic platforms in gynaecological surgery: past, present, and future.Facts, views & vision in ObGyn · 2024Article
- Feasibility of robotic platform to perform R0 resection for locally advanced multi-visceral pelvic malignancy: an institutional experience on outcomes of robotic pelvic exenteration.Langenbeck's archives of surgery · 2023Article
- The role of Da Vinci Xi robotic simulation curriculum in enhancing skills in robotic colorectal surgery.Annals of medicine and surgery (2012) · 2023Review
- Learning curve for robotic rectal cancer resection at a community-based teaching institution.Journal of robotic surgery · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5 h, while total operative time dropped from 6 h to 5.5 h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.
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