AIMC Topic: Bariatric Surgery

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Does Surgeon Experience Correlate with Crowd-Sourced Skill Assessment in Robotic Bariatric Surgery?

The American surgeon
BACKGROUND: The Global Evaluative Assessment of Robotic Skills (GEARS) rubric provides a measure of skill in robotic surgery. We hypothesize surgery performed by more experienced operators will be associated with higher GEARS scores.

Adopt or Abandon? Surgeon-Specific Trends in Robotic Bariatric Surgery Utilization Between 2010 and 2019.

Journal of laparoendoscopic & advanced surgical techniques. Part A
It is unknown if surgeons are more likely to adopt or abandon robotic techniques given that bariatric procedures are already performed by surgeons with advanced laparoscopic skills. We used a statewide bariatric-specific data registry to evaluate s...

Robot-assisted versus laparoscopic approach to concurrent bariatric surgery and hiatal hernia repair: propensity score matching analysis using the 2015-2018 MBSAQIP.

Surgical endoscopy
BACKGROUND: Up to 37% of class three obesity patients have a Hiatal Hernia (HH). Most of the existent HHs get repaired at the time of bariatric surgery. Although the robotic platform might offer potential technical advantages over traditional laparos...

Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States.

Surgery
BACKGROUND: Risk prediction models that estimate patient probabilities of adverse events are commonly deployed in bariatric surgery. The objective was to validate a machine learning (Super Learner) prediction model of 30-day readmission after bariatr...

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview.

Biomolecules
Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFL...

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

Diabetes care
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and pra...

Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.

Surgical endoscopy
BACKGROUND: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learn...