24 resultados para Canada. 1992 Oct. 7
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From 1992 to 1995 we studied 232 (69% male, 87% Caucasian) anti-human immunodeficiency virus (anti-HIV) positive Brazilian patients, through a questionnaire; HIV had been acquired sexually by 50%, from blood by 32%, sexually and/or from blood by 16.4% and by an unknown route by 1.7%. Intravenous drug use was reported by 29%; it was the most important risk factor for HIV transmission. The alanine aminotransferase quotient (qALT) was >1 for 40% of the patients, 93.6% had anti-hepatitis A virus antibody, 5.3% presented hepatitis B surface antigen, 44% were anti-hepatitis B core antigen positive and 53.8% were anti-hepatitis C virus (anti-HCV) positive. The anti-HCV test showed a significant association with qALT>1. Patients for whom the probable HIV transmission route was blood had a 10.8 times greater risk of being anti-HCV positive than patients infected by other routes. Among 30 patients submitted to liver biopsy, 18 presented chronic hepatitis.
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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas. Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas. Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas . Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas . Faculdade de Educação Física