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Background: Preoperative hospitalization with the purpose to obtain more effective weight loss provides intensive care for patients who have a higher body mass index (BMI) and associated diseases that involve a greater risk of peri- and postoperative complications. It is a therapeutic strategy that can make it possible to overcome obstacles related to the difficulty of adhering to obesity treatment.

Aims: To analyze the implementation of a preoperative hospitalization strategy for weight loss in patients eligible for bariatric surgery.

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Background: Gastric outlet obstruction due to unresectable tumours is usually managed with a gastrojejunostomy. Unfortunately, the unsatisfactory outcomes of this procedure have led to the search for alternatives, including gastric partitioning.

Methods: Monocentric, randomized, parallel, open-label trial that included patients with obstructive, unresectable distal gastric tumours.

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Introduction Bariatric surgery is increasingly employed to address the global burden of morbid obesity, with Roux-en-Y gastric bypass (RYGB) representing the predominant procedure. However, some patients, particularly those with extreme obesity (BMI >50 kg/m²), may experience unsatisfactory weight-related outcomes following RYGB. While biliopancreatic diversion with duodenal switch (BPD-DS) offers superior weight reduction for this population, its complexity and associated risks limit its widespread use.

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Background: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term.

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