Background: Laparoscopic surgery is increasingly used in the treatment of colorectal cancer and more recently robotic assistance has been advocated. However, the learning curve to achieve surgical proficiency in laparoscopic surgery is ill-defined and subject to many influences. The aim of this review was to comprehensively appraise the literature on the learning curve for laparoscopic and robotic colorectal cancer surgery, and to quantify attainment of surgical proficiency and its implications in surgical clinical trial design.
Methods: A systematic review using a defined search strategy was performed. Included studies had to state an explicit numerical value of the learning curve evaluated by a single parameter or multiple parameters.
Results: Thirty-four studies were included, 28 laparoscopic and 6 robot assisted. Of the laparoscopic studies, nine defined the learning curve on the basis of a single parameter. Nine studies used more than one parameter to define learning, and 11 used a cumulative sum (CUSUM) analysis. One study used both a multiparameter and CUSUM analysis. The definition of proficiency was subjective, and the number of operations to achieve it ranged from 5 to 310 cases for laparoscopic and 15-30 cases for robotic surgery.
Conclusions: The learning curve in laparoscopic colorectal surgery is multifaceted and often ill-defined, with poor descriptions of mentorship/supervision. Further, the quantification to attain proficiency is variable. The use of a single parameter to quantify this is simplistic. Multidimensional assessment is recommended; as part of this, the CUSUM model, which assesses trends in multiple surgical outcomes, is useful and appropriate when assessing the learning curve in a clinical setting.
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http://dx.doi.org/10.1245/s10434-013-3348-0 | DOI Listing |
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Rheumatology and Immunology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China.
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, which is significant for improving patient prognosis. Based on the NHANES for the periods of 2011-2012, 2013-2014, and 2015-2016, the study involved 11,366 participants, of whom 1,434 reported a diagnosis of OA.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
World Neurosurg
January 2025
Department of Neurology, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, China. Electronic address:
Objective: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning algorithms.
Methods: Employing a retrospective cohort study design, this study collected patients with ruptured IA who received endovascular treatment at Jingzhou First People's Hospital during the inclusion period from September 2022 to December 2023. The entire dataset was randomly split into training and testing dataset with a 7:3 ratio.
J Clin Neurosci
January 2025
Division of Neurosurgery, Department of Surgery, National University Hospital, National University Health System, Singapore.
Ventriculoperitoneal shunt (VPS) insertion is a neurosurgical procedure done routinely for managing hydrocephalus. However, the technique of shunt insertion remains controversial. In this study, we retrospectively compared the accuracy of shunt placement using ultrasound (US) guidance to freehand insertion.
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