Background: Around 15% of patients die or become dependent after cerebral vein and dural sinus thrombosis (CVT). Method: We used the International Study on Cerebral Vein and Dural Sinus Thrombosis (ISCVT) sample (624 patients, with a median follow-up time of 478 days) to develop a Cox proportional hazards regression model to predict outcome, dichotomised by a modified Rankin Scale score >2. From the model hazard ratios, a risk score was derived and a cut-off point selected. The model and the score were tested in 2 validation samples: (1) the prospective Cerebral Venous Thrombosis Portuguese Collaborative Study Group (VENOPORT) sample with 91 patients; (2) a sample of 169 consecutive CVT patients admitted to 5 ISCVT centres after the end of the ISCVT recruitment period. Sensitivity, specificity, c statistics and overall efficiency to predict outcome at 6 months were calculated. Results: The model (hazard ratios: malignancy 4.53; coma 4.19; thrombosis of the deep venous system 3.03; mental status disturbance 2.18; male gender 1.60; intracranial haemorrhage 1.42) had overall efficiencies of 85.1, 84.4 and 90.0%, in the derivation sample and validation samples 1 and 2, respectively. Using the risk score (range from 0 to 9) with a cut-off of ≥3 points, overall efficiency was 85.4, 84.4 and 90.1% in the derivation sample and validation samples 1 and 2, respectively. Sensitivity and specificity in the combined samples were 96.1 and 13.6%, respectively. Conclusions: The CVT risk score has a good estimated overall rate of correct classifications in both validation samples, but its specificity is low. It can be used to avoid unnecessary or dangerous interventions in low-risk patients, and may help to identify high-risk CVT patients.

In various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was developed and implemented in R. Cluster analysis is performed with the corresponding distance for the estimated simulated autoregressive moving average of order one. The primary findings indicate that processes with similar forecast functions are grouped (in the same cluster) as expected concerning the affinity coefficient. It was also possible to conclude that this affinity coefficient is very sensitive to the behavior changes of the forecast functions: processes with small different forecast functions appear to be well separated in different clusters. Moreover, if the two processes have at least an infinite number of π- weights with a symmetric signal, the affinity value is also symmetric.

From the affinity coefficient between two discrete probability distributions proposed by Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and extended it to different types of data, including for the case of complex and heterogeneous data within the scope of symbolic data analysis (SDA). In this study, we refer to the most significant partitions obtained using the hierarchical cluster analysis (h.c.a.) of two well-known datasets that were taken from the literature on complex (symbolic) data analysis. h.c.a. is based on the weighted generalized affinity coefficient for the case of interval data and on probabilistic aggregation criteria from a VL parametric family. To calculate the values of this coefficient, two alternative algorithms were used and compared. Both algorithms were able to detect clusters of macrodata (aggregated data into groups of interest) that were consistent and consonant with those reported in the literature, but one performed better than the other in some specific cases. Moreover, both approaches allow for the treatment of large microdatabases (non-aggregated data) after their transformation into macrodata from the huge microdata.

Leadership has been considerate as a competitive advantage for organizations, contributing to their success and effective and efficient performance. Motivation, on the other hand, is assumed as a basic competence of leadership. Therefore, the main purpose of this paper is to know the perceptions of bank employees on the main motivational factors in the organizational context. Data analysis was performed based on several statistical methods, among which the Categorical Principal Component Analysis (CatPCA) and some agglomerative hierarchical clustering algorithms from VL (V for Validity, L for Linkage) parametrical family, applied to the items that aim to assess the aspects most valued by bankers in the work context. The CatPCA allowed to extract four principal components which explain almost 70% of the total data variance. The dendrograms provided by the hierarchical clustering algorithms over the same data, exhibit four main branches, which are associated with different main motivational factors. Moreover, CatPCA and clustering results show an important correspondence concerning the main motivations in this sector.

Background/Objectives: Antimicrobial resistance (AMR) constitutes a major public health and economic burden in contemporary society. This work aims to identify the patterns and temporal trends of Klebsiella pneumoniae resistance to carbapenems across several European countries. Methods: Data on K. pneumoniae resistance to carbapenems in several countries from 2005 to 2021 (with data available up to 2023) were retrieved from the public ECDC website in 2023. Agglomerative Hierarchical Cluster Analysis (HCA), based on the new generalised affinity coefficient, was applied to the estimated ARIMA models. Results: The dendrogram at the cut-off presented four clusters: the first cluster with Slovenia, Luxembourg, Italy and Estonia; the second cluster with Romania, Malta, Greece, Portugal and Spain; the third cluster Denmark, France and Czechia; and finally the fourth cluster with Lithuania, Cyprus, Finland, Austria, Belgium and Slovakia. Although the resistance values showed an increasing trend over the years in clusters II and III, the increase was more pronounced in cluster II. Conclusions: The application of data analysis, for the first time, using HCA based on a new similarity coefficient, namely, the generalised affinity coefficient, enables the evaluation of time-series patterns in different regions and enables the identification of clusters of countries showing similar temporal patterns of antimicrobial resistance. Carbapenem resistance in K. pneumoniae among countries belonging to cluster II showed increasing trends throughout the study period.

The evaluation of clustering structures is a crucial step in cluster analysis. This study presents the main results of the hierarchical cluster analysis of variables concerning a real dataset in the context of Higher Education. The goal of this research is to find a typology of some relevant items taking into account both the homogeneity and the isolation of the clusters. Two similarity measures, namely the standard affinity coefficient and Spearman’s correlation coefficient, were used, and combined with three probabilistic (AVL, AVB and AV1) aggregation criteria, from a parametric family in the scope of the VL (Validity Link) methodology. The best partitions were selected based on some validation indices, namely the global STAT levels statistics and the measures P(I2, Σ) and γ, adapted to the case of similarity coefficients. In order to evaluate the clusters and identify their most representative elements, the Mann and Whitney U statistics and the silhouette plot were also used.