4 research outputs found

    Assessment of the requisites of microbiology based infectious disease training under the pressure of consultation needs

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    <p>Abstract</p> <p>Background</p> <p>Training of infectious disease (ID) specialists is structured on classical clinical microbiology training in Turkey and ID specialists work as clinical microbiologists at the same time. Hence, this study aimed to determine the clinical skills and knowledge required by clinical microbiologists.</p> <p>Methods</p> <p>A cross-sectional study was carried out between June 1, 2010 and September 15, 2010 in 32 ID departments in Turkey. Only patients hospitalized and followed up in the ID departments between January-June 2010 who required consultation with other disciplines were included.</p> <p>Results</p> <p>A total of 605 patients undergoing 1343 consultations were included, with pulmonology, neurology, cardiology, gastroenterology, nephrology, dermatology, haematology, and endocrinology being the most frequent consultation specialties. The consultation patterns were quite similar and were not affected by either the nature of infections or the critical clinical status of ID patients.</p> <p>Conclusions</p> <p>The results of our study show that certain internal medicine subdisciplines such as pulmonology, neurology and dermatology appear to be the principal clinical requisites in the training of ID specialists, rather than internal medicine as a whole.</p

    Lesion Features on Magnetic Resonance Imaging Discriminate Multiple Sclerosis Patients

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    Background Magnetic resonance imaging (MRI) provides insight into various pathological processes in multiple sclerosis (MS) and may provide insight into patterns of damage among patients. Objective We sought to determine if MRI features have clinical discriminative power among a cohort of MS patients. Methods Ninety-six relapsing remitting and seven progressive MS patients underwent myelin water fraction (MWF) imaging and conventional MRI for cortical thickness and thalamic volume. Patients were clustered based on lesion level MRI features using an agglomerative hierarchical clustering algorithm based on principal component analysis (PCA). Results One hundred and three patients with 1689 MS lesions were analyzed. PCA on MRI features demonstrated that lesion MWF and volume distributions (characterized by 25th, 50th, and 75th percentiles) accounted for 87% of the total variability based on four principal components. The best hierarchical cluster confirmed two distinct patient clusters. The clustering features in order of importance were lesion median MWF, MWF 25th, MWF 75th, volume 75th percentiles, median individual lesion volume, total lesion volume, cortical thickness, and thalamic volume (all p value
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