Background: Erythema nodosum leprosum (ENL) has a chronic and recurrent nature which could reduce patient’s quality of life in addition to the onset of ENL that occurs before, during, or after multidrug treatment, further emphasizing the importance of regular control and education.Purpose:This study aimed to evaluate the epidemiology, onset, duration, and recurrence of ENL. Methods:Data from medical records were obtained for a 3-year retrospective study of multibacillary leprosy patients at the Leprosy Division of the Dermatology and Venereology Outpatient Department, Dr. Soetomo General Hospital Surabaya, with a minimum of 2–5 years follow-up period.Result:The prevalence of ENL continued to increase almost every year especially in 2017 (32% in 2015; 32% in 2016; and 36% in 2017). ENL most often occurs during the first year of multi-drug therapy (MDT) administration followed by after the release from treatment (RFT) with the latest onset occurring 4 years after RFT. The majority of patients experienced chronic and recurrent reactions with the longest reactions lasting up to 4.5 years (55 months). Conclusion:Knowledge about the onset, duration, and recurrences of ENL are essential, and strict supervision for routine control shall be encouraged to increase the patients’ compliance so as to increase their quality of life.
This research purpose to determine the accuracy among Altman, Zmijewski, Grover, and the Fulmer models in predicting financial distress, and to determine the most accurate prediction models to use in Trade and Service company. With the accuracy of the overall prediction model of 89.4%, this research will compare the four prediction models using real conditions of the company. The Data that used in this research are all form of annual financial reports published by companies on the Indonesia Stock Exchange website. The population used is Trade and Service’s company listed on the Indonesia Stock Exchange for the period 2013-2017. Purposive sampling used in this research to obtain 34 companies as research sample. This research compares four prediction models of financial distress using logistic regression analysis. According to the result of this research shows the accuracy between the Altman, Zmijewski, Grover, and Fulmer models to predict financial distress, which the highest level of accuracy is achieved by Zmijewski model and Fulmer model with a value of 100%, followed by Grover model with a value of 97% while Altman model with a value of 73,5%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.