The education effect: higher educational qualifications are robustly associated with beneficial personal and socio-political outcomes

Level of education is a predictor of a range of important outcomes, such as political interest and cynicism, social trust, health, well-being, and intergroup attitudes. We address a gap in the literature by analyzing the strength and stability of the education effect associated with this diverse range of outcomes across three surveys covering the period 1986–2011, including novel latent growth analyses of the stability of the education effect within the same individuals over time. Our analyses of the British Social Attitudes Survey, British Household Panel Survey, and International Social Survey Programme indicated that the education effect was robust across these outcomes and relatively stable over time, with higher education levels being associated with higher trust and political interest, better health and well-being, and with less political cynicism and less negative intergroup attitudes. The education effect was strongest when associated with political outcomes and attitudes towards immigrants, whereas it was weakest when associated with health and well-being. Most of the education effect appears to be due to the beneficial consequences of having a university education. Our results demonstrate that this beneficial education effect is also manifested in within-individual changes, with the education effect tending to become stronger as individuals age

Development and external validation of the ‘Global Surgical-Site Infection’ (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery

Background Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July–November 2014) and GlobalSurg-2 (January–July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator (‘LASSO’)) and machine learning (extreme gradient boosting (‘XGBoost’)). Final model selection was based on prognostic accuracy and clinical utility. Results There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept −0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion The ‘Global Surgical-Site Infection’ score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

Prevalence of voriconazole-resistant invasive aspergillosis and its impact on mortality in haematology patients

BACKGROUND: Increasing resistance of Aspergillus fumigatus to triazoles in high-risk populations is a concern. Its impact on mortality is not well understood, but rates from 50% to 100% have been reported. OBJECTIVES: To determine the prevalence of voriconazole-resistant A. fumigatus invasive aspergillosis (IA) and its associated mortality in a large multicentre cohort of haematology patients with culture-positive IA. METHODS: We performed a multicentre retrospective study, in which outcomes of culture-positive haematology patients with proven/probable IA were analysed. Patients were stratified based on the voriconazole susceptibility of their isolates (EUCAST broth microdilution test). Mycological and clinical data were compared, along with survival at 6 and 12 weeks. RESULTS: We identified 129 A. fumigatus culture-positive proven or probable IA cases; 103 were voriconazole susceptible (79.8%) and 26 were voriconazole resistant (20.2%). All but one resistant case harboured environment-associated resistance mutations in the cyp51A gene: TR34/L98H (13 cases) and TR46/Y121F/T289A (12 cases). Triazole monotherapy was started in 75.0% (97/129) of patients. Mortality at 6 and 12 weeks was higher in voriconazole-resistant cases in all patients (42.3% versus 28.2%, P = 0.20; and 57.7% versus 36.9%, P = 0.064) and in non-ICU patients (36.4% versus 21.6%, P = 0.16; and 54.4% versus 30.7%; P = 0.035), compared with susceptible ones. ICU patient mortality at 6 and 12 weeks was very high regardless of triazole susceptibility (75.0% versus 66.7%, P = 0.99; and 75.0% versus 73.3%, P = 0.99). CONCLUSIONS: A very high prevalence of voriconazole resistance among culture-positive IA haematology patients was observed. The overall mortality at 12 weeks was significantly higher in non-ICU patients with voriconazole-resistant IA compared with voriconazole-susceptible IA.status: publishe

PCR-based detection of A. fumigatus cyp51A mutations on bronchoalveolar lavage can readily predict azole treatment failure : a multi-center validation study in 201 hematology patients with suspected invasive aspergillosis

Medical Microbiolog