The weekend effect on the provision of Emergency Surgery before and during the COVID-19 pandemic: case–control analysis of a retrospective multicentre database

Introduction: The concept of “weekend effect”, that is, substandard healthcare during weekends, has never been fully demonstrated, and the different outcomes of emergency surgical patients admitted during weekends may be due to different conditions at admission and/or different therapeutic approaches. Aim of this international audit was to identify any change of pattern of emergency surgical admissions and treatments during weekends. Furthermore, we aimed at investigating the impact of the COVID-19 pandemic on the alleged “weekend effect”. Methods: The database of the CovidICE-International Study was interrogated, and 6263 patients were selected for analysis. Non-trauma, 18+ yo patients admitted to 45 emergency surgery units in Europe in the months of March–April 2019 and March–April 2020 were included. Demographic and clinical data were anonymised by the referring centre and centrally collected and analysed with a statistical package. This study was endorsed by the Association of Italian Hospital Surgeons (ACOI) and the World Society of Emergency Surgery (WSES). Results: Three-quarters of patients have been admitted during workdays and only 25.7% during weekends. There was no difference in the distribution of gender, age, ASA class and diagnosis during weekends with respect to workdays. The first wave of the COVID pandemic caused a one-third reduction of emergency surgical admission both during workdays and weekends but did not change the relation between workdays and weekends. The treatment was more often surgical for patients admitted during weekends, with no difference between 2019 and 2020, and procedures were more often performed by open surgery. However, patients admitted during weekends had a threefold increased risk of laparoscopy-to-laparotomy conversion (1% vs. 3.4%). Hospital stay was longer in patients admitted during weekends, but those patients had a lower risk of readmission. There was no difference of the rate of rescue surgery between weekends and workdays. Subgroup analysis revealed that interventional procedures for hot gallbladder were less frequently performed on patients admitted during weekends. Conclusions: Our analysis revealed that demographic and clinical profiles of patients admitted during weekends do not differ significantly from workdays, but the therapeutic strategy may be different probably due to lack of availability of services and skillsets during weekends. The first wave of the COVID-19 pandemic did not impact on this difference

STUDENT ASSESSMENT VIA GRADED RESPONSE MODEL

Recently, the Faculty of Political Science at the University of Bologna has started a program of didactics reorganization for several courses, introducing more than one evaluation test during the learning process. Student assessment before the final examination has the double aim of measuring both the level of student\u2019s ability and the effectiveness of the teaching process, in order to correct it real-time. In such an evaluation system, common to the Anglo-Saxon countries, Item Response Theory (IRT) expresses its effectiveness fully. In this paper, an IRT model for ordered polytomous variables is considered in order to investigate the item properties and to evaluate the student achievement. Particularly, the Graded Response Model (GRM) is taken into account in the analysis of three different written tests of a basic Statistics course. The results highlight the different composition of the items and provide a simple description of the student ability distribution

ATTRACTION POLES IN THE UNIVERSITY OF BOLOGNA: A SOCIAL NETWORK ANALYSIS

The movements of students between the faculties of the University of Bologna represent a complex evolving network. By mapping the electronic database containing all relevant data about the students, named \u201cdatawarehouse\u201d for three academic years (2004-2007), we aim to infer on the dynamics and the structural mechanisms that lead the evolution and topology of this complex system. Empirical measurements allow us to discover the topological characteristics of the network at a given moment, as well as their evolution in time. The results highlight stable \u2018attraction\u2019 and \u2018escape\u2019 nodes within the University network, and provide a simple description of the student movements and preferences

Una nuova visione del diritto allo studio. L'esperienza dell'Universit\ue0 di Bologna

Il lavoro riprende le fasi che hanno caratterizzato il rinnovamento dei servizi agli studenti dell'Universit\ue0 di Bologna in una visione moderna del diritto allo Studio. Il progetto, avviato nel 2001, si \ue8 articolato in quattro fasi: divergenza, convergenza, priorit\ue0 e complessit\ue0, pianificazione operativa

Statistica e laboratorio

Il volume presenta un\u2019analisi dedicata alla statistica descrittiva e quindi all\u2019interpretazione dei dati relativi a fenomeni reali e lo studio inferenziale dei risultati campionari, con alcuni cenni di elementi probabilistici. L\u2019ultimo capitolo \ue8 dedicato al modello di regressione lineare semplice con un breve cenno alla regressione multipla. Il testo, indirizzato agli studenti di Scienze Sociali o Politiche, \ue8 un eserciziario con una parte teorica esplicativa di base per ogni argomento, per facilitarne la comprensione propone schede teoriche riassuntive e successive applicazioni su dataset con il foglio elettronico Excel

Learning Bayesian Networks from Classification trees and Expert knowledge: a preliminary study

To address the classification problem when the number of cases is too small to effectively use just a single technique, this paper suggests to use a \u201chybrid\u201d approach, that combines tree classifiers, expert knowledge and Bayesian networks. The output of the \u201chybrid\u201d strategy takes the form of a Bayesian network, where the serious drawback of requiring huge amounts of data was overcome by coupling the network with another classifier and using expert knowledge. The technique was applied to two clinical case-studies and its predictive performance was compared with the performances of the single approaches. The results show that the proposed technique benefits from several advantages of the three single approaches and outperforms all of them: it shows a better sensitivity, that plays a crucial role in classifying new patients into the high-risk category, and is competitive with regards to specificity. Therefore, even though additional studies are needed to validate the \u201chybrid\u201d approach, it seems a promising technique to develop reliable classification systems for small datasets

A FORECAST MODEL TO ASSESS THE CRITICAL POINTS IN UNIVERSITY SYSTEMS

The network of student transfers within the system of the University of Bologna adapts a constraint scale-free topology. Despite the presence of \u201chubs,\u201d their role is strongly influenced by different institutional decisions and choices applied to courses. Therefore, the macro model of this network is not useful for previewing its evolution over time, particularly in the creation of critical points, which are courses with high out transfer rates. The idea is to introduce a probability of transfer function for each course, in order to preview the creation of critical points. The proposed model is fundamentally a binary regression logistic model. This rough model allows us to identify the possible creation of critical points in our complex system, these being \u201cescape\u201d courses, and to preview the impact of institutional decisions. At the same time, it may suggest how to remove the existing critical points in order to optimize the academic courses on offer