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Background: The use of nutrition-screening tools in cirrhotic patients is not systematized. Recently, specific tools have been proposed for patients with cirrhosis, but their diagnostic capabilities have been scarcely studied. Methods: This was a prospective study that includes outpatients with liver cirrhosis undergoing follow-up in the hepatology consultations of a tertiary-care university hospital. A trained gastroenterologist applied the screening tools: Liver Disease Universal Screening Tool (LDUST), Royal Free Hospital-Nutrition Prioritizing Tool (RFH-NPT), and Mini Nutritional Assessment-Short Form (MNA-SF). Subsequently, the diagnosis of malnutrition was made according to Global Leadership Initiative for Malnutrition (GLIM) criteria by an endocrinologist, who was blind to the results of the screening tools. Results: Sixty-three patients (38.1% women, mean age 63.1 ± 9.9 years) with cirrhosis (60.3% Child-Pugh A, 34.9% Child-Pugh B, and 4.8% Child-Pugh C) were evaluated. The prevalence of malnutrition was 38.1% (15.9% moderate, 22.2% severe). Advanced stages of cirrhosis were associated with a higher prevalence of malnutrition (P = .021). MNA-SF was the most accurate screening tool, being superior to RFH-NPT and LDUST. It presented better sensitivity than RFH-NPT (88% [0.68-0.97] vs 67% [0.45-0.84], P = .031) and better specificity than both LDUST (97% [0.87-0.99] vs 62% [0.45-0.77], P < .001) and RFH-NPT (97% [0.87-0.99] vs 82% [0.67-0.93], P = .016). Conclusions: According to the GLIM criteria, malnutrition affected 38.1% of patients with cirrhosis, being severe in 22% of the patients. MNA-SF is the most accurate screening test, superior even to tools specifically designed for cirrhotic patients (LDUST).

In this paper, we propose an algorithmic approach based on resampling and bootstrap techniques to measure the importance of a variable, or a set of variables, in econometric models. This algorithmic approach allows us to check the real weight of a variable in a model, avoiding the biases of classical tests, and to select the more relevant variables, or models, in terms of predictability, by reducing dimensions. We apply this methodology to the Global Entrepreneurship Monitor data for the year 2014, to analyze the individual and national-level determinants of entrepreneurial activity, and compare results with a forward selection approach, also based on resampling predictability, and a standard forward stepwise selection process. We find that our proposed techniques offer more accurate results, which show that innovation and new technologies, peer effects, the socio-cultural environment, entrepreneurial education at University, R&D transfers, and the availability of government subsidies, are among the most important predictors of entrepreneurial behavior.

Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison.Supplementary materials accompanying this paper appear online.

The study of records in the Linear Drift Model (LDM) has attracted much attention recently due to applications in several fields. In the present paper we study δ-records in the LDM, defined as observations which are greater than all previous observations, plus a fixed real quantity δ. We give analytical properties of the probability of δ-records and study the correlation between δ-record events. We also analyse the asymptotic behaviour of the number of δ-records among the first n observations and give conditions for convergence to the Gaussian distribution. As a consequence of our results, we solve a conjecture posed in J. Stat. Mech. 2010, P10013, regarding the total number of records in a LDM with negative drift. Examples of application to particular distributions, such as Gumbel or Pareto are also provided. We illustrate our results with a real data set of summer temperatures in Spain, where the LDM is consistent with the global-warming phenomenon.