Disseminated tuberculosis presenting as chronic pancreatitis, a rare case report

Disseminated tuberculosis (TB) refers to concurrent involvement of at least two non-contiguous organ sites of the body, or involvement of the blood or bone marrow by tuberculosis process. Pancreatic TB is a rare manifestation of such a common disease possibly due to protective pancreatic enzymes. We described a case report of a patient who presented with chronic pancreatitis with pancreatic pseudocyst with empyema of left lung which intraoperatively was a psoas abscess which was managed by drainage of the abscess and Intercostal tube placement and thoracoscopic drainage of empyema and its adhesiolysis. Histopathology revealed tuberculous granulation tissue of psoas muscle biopsy and in thoracoscopic scrapings. Patient became symptomless since the surgery and initiation of anti-tubercular therapy

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct and a definitive answer. However, in real-world applications, users often ask questions that don't have a definitive answer. Incorrectly answering such questions certainly hampers a system's reliability and trustworthiness. Can SOTA models accurately identify such questions and provide a reasonable response? To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don't have definitive answers. Furthermore, for each QnotA instance, we also provide a corresponding QA instance i.e. an alternate question that ''can be'' answered. With this data, we formulate three evaluation tasks that test a system's ability to 'identify', 'distinguish', and 'justify' QnotA questions. Through comprehensive experiments, we show that even SOTA models including GPT-3 and Flan T5 do not fare well on these tasks and lack considerably behind the human performance baseline. We conduct a thorough analysis which further leads to several interesting findings. Overall, we believe our work and findings will encourage and facilitate further research in this important area and help develop more robust models.Comment: TrustNLP Workshop at ACL 202