ABSTRACT
Primary gastric Burkitt's lymphoma (BL) is rare in the pediatric population. Furthermore, the association of Burkitt's lymphoma with Helicobacter pylori is not well defined. We report a case of primary gastric Burkitt's lymphoma associated with Helicobacter pylori diagnosed in a pediatric patient. This diagnosis was made with the aid of endoscopic ultrasound (EUS)-guided fine-needle biopsy (FNB). This is one of the first pediatric cases of EUS-guided FNB for the diagnosis of H. pylori-associated gastric BL.
Subject(s)
Burkitt Lymphoma , Helicobacter Infections , Helicobacter pylori , Stomach Neoplasms , Adolescent , Burkitt Lymphoma/diagnostic imaging , Burkitt Lymphoma/microbiology , Female , Helicobacter Infections/complications , Humans , Stomach Neoplasms/diagnostic imaging , Stomach Neoplasms/microbiology , Ultrasonography, InterventionalABSTRACT
Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.