Prosthetic treatment in the adult French population: Prevalence and relation with demographic,socioeconomic and medical characteristics

BackgroundThe aim of this study was to estimate the prevalence of dental prosthetic treatment and to investigate the demographic, social, economic and medical factors associated with the use of fixed and removable dentures in a representative sample of adults living in France.MethodsThe data were obtained from the 2002–2003 Decennial Health Survey, a cross-sectional study of a representative sample of the population living in France, which included 29,679 adults. Information was collected by interview. The variables collected were fixed denture, removable denture, age, gender, number of children, area of residence, nationality, educational attainment, family social status, employment status, annual household income per capita, supplementary insurance, chronic disease, eyesight problems/glasses, hearing problems/hearing aids. Multinomial logistic regression models were used to study the relationship between prosthetic treatment and demographic, socioeconomic and medical characteristics unadjusted, adjusted for age and adjusted for all the characteristics.ResultsThe prevalence of prosthetic treatment was 34.6% (95% confidence interval (CI): [34.1; 35.2]) for fixed prosthetic dentures and 13.8% (95% CI: [13.4; 14.2]) for removable prosthetic dentures. We showed a gradient between educational attainment and removable dentures; the odds ratio adjusted for all the variables (aOR) associated with no or primary education compared to post-secondary education was 2.56; 95% CI: [2.09; 3.13]. When annual household income per capita was low, subjects were less likely to report fixed dentures (aOR = 0.68; 95% CI: [0.62; 0.75]) than those with high annual household income per capita. Individuals without insurance less often reported fixed dentures than those with private insurance. Those reporting chronic disease were less likely to report fixed dentures (aOR = 0.87; 95% CI: [0.79; 0.95]) but more likely to report removable dentures (aOR = 1.29; 95% CI: [1.17; 1.43]) than those without chronic disease.ConclusionThis study reveals social, economic and medical inequalities in fixed and removable prosthetic treatment among adults in France.     

A comparison of the academic impact of plastic surgery units in the United Kingdom and Ireland using bibliometric analysis

Academic output is just one aspect of a successful career as a plastic surgeon. However, for those with a strong interest in academia, the academic output of a department will likely be a key factor when deciding how to rank jobs. The aim of this study was to quantify and rank the academic output of plastic surgery units across the UK and Ireland. The Institute for Scientific Information (ISI) Web of Science Bibliometric analysis tool was used to collate cumulative (1950–2016), 10 year (2006–2016) and 3 years (2013–2015) research output data for plastic surgery units in the UK and Ireland. Sixty-six plastic surgery units were identified. Departments were ranked for each time period according to the number of papers produced, number of citations (Nc) and h-index (a measure of the impact of scientific output). The top 3 departments for number of papers in the last 10 years were The Royal Free Hospital, London (226) Broomfield Hospital, Chelmsford (218), and Morriston Hospital and Swansea (188). The top 3 for h-number were The Royal Free Hospital (21) Wythenshawe Hospital, Manchester (18) and Morriston Hospital (17). Academic output varies across plastic surgery units in the UK and Ireland. A number of departments have consistently maintained high academic outputs across the years and will be of interest to surgeons hoping to pursue a career in academia.     

Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.