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Diagnosis and management of caustic ingestion: an interdisciplinary nationwide cross-sectional survey from the Italian society of endoscopic surgery and new technologies (SICE), the Italian society of digestive endoscopy (SIED), the world society of emergency surgery-Italy chapter (WSESit), and the Italian society of surgical endoscopy and digestive diseases (ISSE)

Caustic ingestion (CI) in adults represents a potentially life-threatening condition. Diagnosis and management of CI in real life remain challenging. The aim of the survey is to evaluate on a national scale the multidisciplinary management of these patients. 24-item online Survey was sent to the mailing lists and social media of Italian Society of Endoscopic Surgery and New Technologies, Italian Society of Digestive Endoscopy, World Society of Emergency Surgery-Italy Chapter, and Italian Soci...

Protective ileostomy creation after anterior resection of the rectum (PICARR): a decision-making exploring international survey

In our previous survey of experts, surgeon’s decision-making process (DMP) about protective ileostomy (PI) creation after anterior resection was investigated. Based on our previous data, a multiple choice questionnaire has been developed. The aim is to perform a quantitative analysis of the results obtained from an international survey and to describe the clinical practice worldwide. Ten questions were related to participants’ demographics and, 20 questions (of which 17 Likert scale questions...

Efficient multi-task learning with instance selection for biomedical NLP

Background: Biomedical natural language processing (NLP) increasingly relies on large language models and extensive datasets, presenting significant computational challenges. Methods: We propose Blue5, a multi-task model based on SciFive that incorporates instance selection (IS) to enable efficient, multi-task learning (MTL) on biomedical data. We adapt the E2SC-IS framework for the biomedical domain, integrating a calibrated SVM classifier to reduce computational costs. Results: Our approach...

Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management

Background: Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-based controllers offer a promising approach for improving automated insulin administration. Methods: We propose a Dual Proximal Policy Optimization (Dual PPO) controller for personalized insulin delivery in a hybrid closed-loop system. The control...

Understanding Oxygen Concentrator Failures in Low Resource Settings: The Role of Dust and Humidity

Oxygen concentrators (OCs) are essential medical devices providing oxygen in various settings, especially low-resource settings (LRSs). Despite their adaptability and cost-effectiveness, challenges arise in such environments due to factors like dust, temperature, and humidity, leading to premature OC failure. While efforts have been made to address these issues, understanding the primary contributing factor remains unclear. This study aims to shed light on this matter through the analysis of ...

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