Search

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The aim of this study was to examine the processes involved in a guided self‐help (GSH) pre‐treatment intervention (RecoveryMANTRA) for patients with anorexia nervosa (AN), by measuring the levels of patient/mentor Language Style Matching (LSM). RecoveryMANTRA was supported by student mentors or peer mentors (recovered individuals) over six weekly chat‐based sessions. We examined whether LSM during RecoveryMANTRA predicted patients'working alliance with the clinic therapist, motivation, eating disorder (ED) and general psychopathology. A further aim was to examine differences in LSM between student mentors and peer mentors. Method: 87 AN adults received RecoveryMANTRA plus treatment as usual. The LSM algorithm was used to calculate verbal attunement between patient and mentor. Participants were assessed at baseline and at the end of the intervention. Results: Both early (1st session) and late (6th session) LSM predicted higher working alliance with the clinic therapist. Moreover, late LSM predicted lower EDs symptoms at the end of the RecoveryMANTRA intervention. Patient/peer mentor dyads showed higher late verbal attunement than patient/student mentor dyads. Conclusions: These findings suggests that in the early phase of treatment relational aspects can impact on engagement with treatment. Verbal attunement in a GSH for AN is associated with working alliance and better clinical outcome. Highlights: Language Style Matching (LSM) between patients and mentors in both the early and late phase of the RecoveryMANTRA intervention predicted higher working alliance with the clinic therapist.LSM in the last phase (6th session) was associated with lower eating disorders (EDs) symptoms at the end of the RecoveryMANTRA intervention.Patients/peer mentors dyads showed higher late LSM than patients/student mentors dyads. [ABSTRACT FROM AUTHOR]

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

The HLA‐DPB1 locus has been demonstrated to have a significant role on patients' outcome after allogeneic HSCT, and the so‐called T‐cell epitope (TCE) algorithm has been incorporated in international guidelines for the selection of unrelated donors. The purpose of the present study is to measure, through a national survey conducted on behalf of the Associazione Italiana di Immunogenetica e Biologia dei Trapianti (AIBT), the extent of awareness and use of HLA‐DPB1 TCE‐based algorithms during the donor search. 89% of the HLA laboratories answered to a short questionnaire and the results showed a progressive increase of the laboratories typing DPB1 in patients and their potential donors during the search (from 44% to 79% during the 2010–2019 period) as well as the application of a TCE‐based algorithm for the donor choice whenever possible (from 24% to 65% during the same period). The DP‐permissiveness status is detailed in the official HLA typing report by 12%, 32% and 50% of laboratories in 2010, 2015 and 2019, respectively. The present data indicate an encouraging raise in the awareness of the HLA‐DPB1 role in unrelated donor selection; noteworthy, mentioning the TCE‐based permissiveness status in the HLA typing report of each potential unrelated donor represents a notable mean to raise awareness among transplant physicians and to support them in their task of choosing the best donor. Nonetheless, despite the compelling evidence of the predictive ability of TCE‐based algorithms, further efforts are still needed to extend its application to all transplant centers in Italy. [ABSTRACT FROM AUTHOR]

Objective: To validate the performance of a first-trimester simple risk score based on the ASPRE trial algorithm for pre-eclampsia.Design: Multicentre retrospective cohort analysis.Setting: Four Italian hospitals.Population: Unselected nulliparous women at 11-13 weeks of gestation from January 2014 through to January 2018.Methods: Model performance was evaluated based on discrimination and calibration.Main Outcome Measures: Delivery before 37 weeks of gestation with a diagnosis of pre-eclampsia.Results: Based on 73 preterm pre-eclampsia cases and 7546 controls (including 101 cases of late pre-eclampsia), the area under the receiver operating characteristics curve was 0.659 (95% CI 0.579-0.726). The sensitivity was 32.9% (95% CI 22.1-43.7) at a false-positive rate of 8.8%. The positive likelihood ratio was 3.74 (95% CI 2.67-5.23), the positive predictive value was 3.49% (95% CI 2.12-4.86%) and the negative predictive value was 99.3% (95% CI 99.1-99.5%). The sensitivity and positive likelihood ratio were approximately 40% lower than in the original study. The calibration analysis showed a good agreement between observed and expected risks (P = 0.037). Comparison with the Fetal Medicine Foundation (FMF) algorithm yielded a difference in the area under the curve of 0.084 (P = 0.007).Conclusions: In our Italian population, the simple risk score had a lower performance than expected for the prediction of preterm pre-eclampsia in nulliparous women. The FMF algorithm applied to the same data set resulted in a better prediction.Tweetable Abstract: Simple risk score predicts preterm pre-eclampsia in Italy. [ABSTRACT FROM AUTHOR]

We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000--out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions. [ABSTRACT FROM AUTHOR]

National exercises for the evaluation of research activity by universities are becoming regular practice in ever more countries. These exercises have mainly been conducted through the application of peer-review methods. Bibliometrics has not been able to offer a valid large-scale alternative because of almost overwhelming difficulties in identifying the true author of each publication. We will address this problem by presenting a heuristic approach to author name disambiguation in bibliometric datasets for large-scale research assessments. The application proposed concerns the Italian university system, comprising 80 universities and a research staff of over 60,000 scientists. The key advantage of the proposed approach is the ease of implementation. The algorithms are of practical application and have considerably better scalability and expandability properties than state-of-the-art unsupervised approaches. Moreover, the performance in terms of precision and recall, which can be further improved, seems thoroughly adequate for the typical needs of large-scale bibliometric research assessments. [ABSTRACT FROM AUTHOR]

The article presents a study which analyzed the air quality monitoring network in Piemonte, Northern Italy, to classify sites in homogeneous clusters and identify the representative ones. A description of the monitoring network and of the motivating dataset is presented. There are some important features in the monitoring network that can be useful in the interpretation of clustering results. The study proposed to synthesize time series, preserving their temporal patterns by converting discrete data into functional ones, and to partition their corresponding estimated curves by Partitioning Around Medoids (PAM) algorithm, which through silhouettes provides an instrument of interpretation as well as validation of results.