Individual characteristics inherent in the expert, as well as their physical and psycho-emotional state subject to the influence of random, uncontrollable factors, contribute to subjectivity in the sensory evaluation of wines. With great variability of opinions, the final results of sensory evaluation may become doubtful. The presence of a random component in the sensory evaluation justifies the use of statistical methods for analyzing the consistency of expert evaluations. Along with Spearman's correlation coefficients and Kendall matching, Cronbach alpha criterion was used to assess the consistency of expert opinions. The advantages of positional analysis have been discussed -Cronbach's alpha criterion is calculated not by the rank of expert points, but by the initial point scale considering its variability; it allows to evaluate the contribution of each expert to the consistency of expert evaluations, as well as the reliability of the total scale of points set for each wine sample. Based on the data analysis from sensory evaluation of the quality of dry red and white wines of Russian production, the results of the consistency study of expert evaluations as well as the reliability of the total score scale have been presented. What is more, analysis of the "loyalty" of experts in evaluating the quality of wines has been performed.
The analysis of data on the sensory evaluation of the quality of wines obtained using traditional technologies in the Krasnodar Territory, Russia, was carried out using the statistical ranking criteria – the Spearman and Kendall correlation coefficients, as well as the positional analysis – Cronbach's alpha. Data on the sensory evaluation of 60 samples of natural dry red and white wines are presented, among which 20 are white wines, 40 are red wines produced in 2010–2015. Eleven specialists aged between 32 and 66 years (the average age was 50 years; 4 females and 7 males) participated in the sensory evaluation procedure. All participants are considered experts in the field of wine, work in the wine industry and have professional experience in the field of sensory analysis. The results of the consistency study of expert evaluations, the reliability of the general score scale, as well as the analysis of the loyalty of experts in the wine quality assessment are presented in the article. The reliability of the proposed loyalty scale is shown, i.e., the scale of the sum of scores given by each expert in the evaluation of the quality of wines. The database on the sensory evaluation of the quality of wines, obtained for all wine samples using positional analysis, makes it possible to assess the contribution of each of the 60 wine samples to their ranking by mean scores. The data may be of interest to scientists and oenologists for the wine quality assessment.
With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.
Background. According to the literature data, acute coronary syndrome (ACS) in 2-20 % of cases is combined with atrial fibrillation (AF). According to the current guidelines of the European Society of Cardiology (ESC), patients with coexisting AF and ACS should receive dual antiplatelet therapy for the prevention of recurrent cardiovascular events and anticoagulant therapy for the prevention of thromboembolic complications. However, this combination is fraught with the development of hemorrhagic syndrome.Aim. To develop a model and software module for predicting possible bleeding in patients with ACS combined with AF taking three-component antithrombotic therapy.Materials and Methods. To build prognostic models for the development of hemorrhagic syndrome, a statistical method was used for classification trees and the neural network procedure implemented in the STATISTICA package. To build prognostic models, a sample was used consisting of 201 patients with a combination of ACS and AF with and without fatal outcome, the state of which was described by 42 quantitative and qualitative clinical indicators. The control group included 205 patients with ACS and intact sinus rhythm.Results. To identify predictors of predictive models of the possible development of hemorrhagic syndrome in patients with triple antithrombotic therapy, the Spearman correlation coefficient was used. The study of correlations allowed to reveal clinical indicators – predictors of prognostic models. After analyzing the predictive ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment that allows determining the possibility of hemorrhagic syndrome in patients with a combination of ACS and AF using classification trees and neural networks.Сonclusion. A classification model and a software module were developed to predict possible bleeding in patients taking three-component antithrombotic therapy. Models contain both quantitative and qualitative (categorical) clinical indicators. The current level of development of data analysis technologies opens up broad horizons for medicine in solving problems on real medical data, without translating them into scoring risk scales, including prediction of the diagnosis of the disease, stage of the disease, treatment outcome, possible complications, etc. High reliability of such systems can be provided by large volumes of medical data accumulated on servers.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.