Metabolic surgery can promote integrated physiologic improvement to ameliorate metabolic illness, particularly type 2 diabetes. Nevertheless, the therapeutic scope has been limited by unexpectedly inconsistent surgical outcomes. Therefore, the purpose of this study is to overcome the obstacles by determining the core mechanism of conflicting results. The surgical anatomy, clinical course, and outcomes of various metabolic surgeries, including modified types of duodenal-jejunal bypass procedures, were compared to comprehend specific surgical patterns from distinctive perspectives. Patients in exclusively nonobese groups are subjected to avoid compounding by weight fluctuations. During intestinal anastomosis, the epithelial identity of the succeeding intestine is replaced by that of the proximal epithelium through altered crosstalk between the epithelium and opposing mesenchymal cells. The rapid turnover rate and compensatory proliferation of the succeeding intestine accelerate the propagation of the replaced epithelium. Propagation of replaced epithelium could provoke inconsistent outcomes of metabolic surgery. The major determinants of the conflicting results of metabolic surgery are inadequate duodenal exclusion and inappropriate length of the biliopancreatic limbs. The replaced enteroendocrine cells during regeneration provoke inconsistent outcomes of metabolic surgery. The critical factors are the type and density of enteroendocrine cells distributed at the terminal end of the preceding proximal intestine.

Current State-of-the-Art (SotA) chatbots are able to produce high quality sentences, handling different conversation topics, and larger interaction times. Unfortunately, the generated responses highly depend on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or produce confusion in long-term interactions. In this research paper, we propose a novel methodology consisting of two main phases: a) hierarchical automatic detection of topics and subtopics in dialogue interactions using a Zero-Shot learning approach, and b) detecting inconsistent answers using K-Means and the Silhouette coefficient. To evaluate the efficacy of topic and subtopic detection, we used a subset of the DailyDialog dataset and real dialogue interactions gathered during the Alexa Socialbot Grand Challenge 5 (SGC5). The proposed approach enables detecting up to 18 different topics and 102 subtopics. For the purpose of detecting inconsistencies, we manually generate multiple paraphrased questions and employ several pre-trained SotA chatbot models to generate responses. Our experimental results demonstrate a weighted F-1 value of 0.34 for topic detection, a weighted F-1 value of 0.78 for subtopic detection in DailyDialog, then 81% and 62% accuracy for topic and subtopic classification in SGC5; finally, to predict the number of different responses, we obtained a mean squared error (MSE) of 3.4 when testing smaller generative models and 4.9 in recent large language models.

A text obtained by a Large Language Model (LLM) such as GPT4 usually has issues in terms of incorrectness and hallucinations. We build a fact-checking system 'Truth-O-Meter' which identifies wrong facts, comparing the generation results with the web and other sources of information, and suggests corrections. Text mining and web mining techniques are leveraged to identify correct corresponding sentences; also, the syntactic and semantic generalization procedure adopted to the content improvement task. To handle inconsistent sources while fact-checking, we rely on an argumentation analysis in the form of defeasible logic programming. We compare our fact checking engine with competitive approach based on reinforcement learning on top of LLM or token-based hallucination detection. It is observed that LLM content can be substantially improved for factual correctness and meaningfulness.