Osteoporosis has recently been acknowledged as a major public health issue in developed countries because of the decrease in the quality of life of the affected person and the increase in public costs due to complete or partial physical disability. The aim of this study was to use the J48 algorithm as a classification task for data from women exhibiting changes in bone densitometry. The study population included all patients treated at the diagnostic center for bone densitometry since 2010. Census sample data collection was conducted as all elements of the population were included in the sample. The service in question provides care to patients via the Brazilian Unified Health System and private plans. The results of the classification task were analyzed using the J48 algorithm, and among the dichotomized variables associated with a diagnosis of osteoporosis, the mean accuracy was 74.0 (95% confidence interval [CI], 61.0-68.0) and the mean area under the curve of the receiver operating characteristic (ROC) curve was 0.65 (95% CI, 0.64-0.66), with a mean sensitivity of 76.0 (95% CI, 76.0-76.0) and a mean specificity of 48.0 (95% CI, 46.0-49.0). The analyzed results showed higher values of sensitivity, accuracy, and curve of the ROC area in experiments conducted with individuals with osteoporosis. Most of the generated rules were consistent with the literature, and the few differences might serve as hypotheses for further studies.

Lemierre syndrome is characterized by septic thrombophlebitis of the internal jugular vein, after an oropharyngeal infection, with septic embolization to the lungs or other organs. This case report describes a 37-year-old female patient who presented with edema and pain in the right hemiface with onset 3 days previously and progressive fatigue and dyspnea since the previous day. She had had tooth 48 extracted 3 days previously. Physical examination at admission found tachypnea, with 60% saturation (in room air), edema at the angle of the right mandible, diffuse reduction of vesicular murmur, and calves free from clubbing. Angiotomography of the chest and laboratory tests were compatible with septic emboli, and cervical computed tomography confirmed a diagnosis of septic thrombophlebitis of the internal jugular vein. She was managed with antibiotics and given treatment for her symptoms. Lemierre syndrome most often occurs in young men and there is embolization to the lungs in up to 97% of cases. Rarely, the etiology of this syndrome may be tooth extraction. Computed tomography is the imaging method most often used for diagnosis and treatment is basically antibiotic. Surgery is thus rarely necessary.A síndrome de Lemierre caracteriza-se pela tromboflebite séptica da veia jugular interna, após uma orofaringite, com embolização séptica para o pulmão ou outros órgãos. Neste relato de caso, apresentamos uma paciente feminina, 37 anos de idade, com história de edema e dor em hemiface direita há três dias, associada a fadiga e dispneia progressiva há um dia. História de extração dentária do elemento 48 há três dias. No exame físico admissional, apresentava-se taquipneica, saturando 60% (em ar ambiente), com edema em ângulo da mandíbula direita, redução difusa do murmúrio vesicular e panturrilhas sem empastamento. Angiotomografia de tórax e exames laboratoriais foram compatíveis com quadro de embolia séptica, e tomografia computadorizada da cervical corroborou o diagnóstico de tromboflebite séptica da veia jugular interna. Foi tratada com antibióticos e sintomáticos. A síndrome de Lemierre afeta mais homens jovens e tem embolização para o pulmão em até 97% dos casos. Extrações dentárias raramente podem ser a etiologia dessa síndrome. A tomografia computadorizada é o método de imagem mais utilizado no diagnóstico, e o tratamento é, essencialmente, com antibióticos; portanto, a abordagem cirúrgica é raramente necessária.Palavras-chave: síndrome de Lemierre; extração dentária; tromboflebite; embolia pulmonar. Lemierre syndrome: case report 338 338/340 J Vasc Bras. 2018, Out.-Dez.; 17(4):337-340

Background: Bayesian classifiers have the advantage of determining the class to which a given record belongs compared to traditional classifiers, taking as base the probability of an element belonging to a class. Thus, the diagnosis of diseases such as osteoporosis and osteopenia can become faster and precise. This paper presents an assessment of the accuracy of the Bayesian classifiers Bayes Net, Naive Bayes and Averaged One-Dependence Estimators to support diagnoses of osteopenia and osteoporosis. Method: The methodology that guided the development of this research relied on the choice of database, the study of the Bayes Net, Naive Bayes and Averaged One-Dependence Estimators algorithms, and the description of the experiments. Results: The algorithm with the highest specificity was Bayes Net, (53.0±0.27). The highest accuracy was obtained using the AODE classifier (83.0±0.17). Our results showed higher mean instances correctly classified using the Naive Bayes algorithm (82.84±14.42), and the average of incorrectly classified instances was higher for Bayes Net (17.46±14.76). Conclusion: Based on the statistical measures analyzed in the experiments (instances classified correctly and incorrectly, the kappa coefficient, mean absolute error, sensitivity, specificity, accuracy, recall, F-measure, and Area Under Curve (AUC)), all classifiers showed good results, thus, given these data, it is possible to produce a reasonably accurate estimate of the diagnosis.