930 resultados para classification algorithm
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These Facts sheets have been developed to provide a multitude of information about executive branch agencies/departments on a single sheet of paper. The Facts provides general information, contact information, workforce data, leave & benefits information, and affirmative action data. This is the most recent update of information for the fiscal year 2007.
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These Facts sheets have been developed to provide a multitude of information about executive branch agencies/departments on a single sheet of paper. The Facts provides general information, contact information, workforce data, leave & benefits information, and affirmative action data. This is the most recent update of information for the fiscal year 2007.
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Summary Background: We previously derived a clinical prognostic algorithm to identify patients with pulmonary embolism (PE) who are at low-risk of short-term mortality who could be safely discharged early or treated entirely in an outpatient setting. Objectives: To externally validate the clinical prognostic algorithm in an independent patient sample. Methods: We validated the algorithm in 983 consecutive patients prospectively diagnosed with PE at an emergency department of a university hospital. Patients with none of the algorithm's 10 prognostic variables (age >/= 70 years, cancer, heart failure, chronic lung disease, chronic renal disease, cerebrovascular disease, pulse >/= 110/min., systolic blood pressure < 100 mm Hg, oxygen saturation < 90%, and altered mental status) at baseline were defined as low-risk. We compared 30-day overall mortality among low-risk patients based on the algorithm between the validation and the original derivation sample. We also assessed the rate of PE-related and bleeding-related mortality among low-risk patients. Results: Overall, the algorithm classified 16.3% of patients with PE as low-risk. Mortality at 30 days was 1.9% among low-risk patients and did not differ between the validation and the original derivation sample. Among low-risk patients, only 0.6% died from definite or possible PE, and 0% died from bleeding. Conclusions: This study validates an easy-to-use, clinical prognostic algorithm for PE that accurately identifies patients with PE who are at low-risk of short-term mortality. Low-risk patients based on our algorithm are potential candidates for less costly outpatient treatment.
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Vulvar cancer is a rare disease and its screening is depending on the quality and the relevance of our clinical examination. Incidence of vulvar cancer and especially precancerous lesions, vulvar intraepithelial neoplasias (VIN), increased during these last years. The new terminology of vulvar intraepithelial neoplasia will help us to identify high risk groups which could develop a cancer: usual and differentiated VIN. An early diagnosis is essential to propose an adequate treatment. Management is a major point according to the rising incidence of these lesions in younger women. Until we can observe a benefit from the vaccination against human papillomavirus, we must increase the quality of screening by a careful examination of the vulva.
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The development and tests of an iterative reconstruction algorithm for emission tomography based on Bayesian statistical concepts are described. The algorithm uses the entropy of the generated image as a prior distribution, can be accelerated by the choice of an exponent, and converges uniformly to feasible images by the choice of one adjustable parameter. A feasible image has been defined as one that is consistent with the initial data (i.e. it is an image that, if truly a source of radiation in a patient, could have generated the initial data by the Poisson process that governs radioactive disintegration). The fundamental ideas of Bayesian reconstruction are discussed, along with the use of an entropy prior with an adjustable contrast parameter, the use of likelihood with data increment parameters as conditional probability, and the development of the new fast maximum a posteriori with entropy (FMAPE) Algorithm by the successive substitution method. It is shown that in the maximum likelihood estimator (MLE) and FMAPE algorithms, the only correct choice of initial image for the iterative procedure in the absence of a priori knowledge about the image configuration is a uniform field.
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The absolute K magnitudes and kinematic parameters of about 350 oxygen-rich Long-Period Variable stars are calibrated, by means of an up-to-date maximum-likelihood method, using HIPPARCOS parallaxes and proper motions together with radial velocities and, as additional data, periods and V-K colour indices. Four groups, differing by their kinematics and mean magnitudes, are found. For each of them, we also obtain the distributions of magnitude, period and de-reddened colour of the base population, as well as de-biased period-luminosity-colour relations and their two-dimensional projections. The SRa semiregulars do not seem to constitute a separate class of LPVs. The SRb appear to belong to two populations of different ages. In a PL diagram, they constitute two evolutionary sequences towards the Mira stage. The Miras of the disk appear to pulsate on a lower-order mode. The slopes of their de-biased PL and PC relations are found to be very different from the ones of the Oxygen Miras of the LMC. This suggests that a significant number of so-called Miras of the LMC are misclassified. This also suggests that the Miras of the LMC do not constitute a homogeneous group, but include a significant proportion of metal-deficient stars, suggesting a relatively smooth star formation history. As a consequence, one may not trivially transpose the LMC period-luminosity relation from one galaxy to the other.
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Objectives: Recent population genetic studies suggest that the Staphylococcal Chromosome Cassettes mec (SCCmec) was acquired at a global scale much more frequently than previously thought. We hypothesized that such acquisitions can also be observed at a local level. In the present study, we aimed at investigating the diversity of SCCmec in a local MRSA population, where the dissemination of four MRSA clones has been observed (JCM 2007, 45: 3729). Methods: All the MRSA isolates (one per patient) recovered in the Vaud canton of Switzerland from January 2005 to December 2008 were analyzed in this study. We used the Double Locus Sequence Typing (DLST) method, based on clfB and spa loci, and the e-BURST algorithm to group the types with one allele in common (i.e. clone). To increase the discriminatory power of the DLST method, a third polymorphic marker (clfA) was further analyzed on a sub-sample of isolates. The SCCmec type of each isolate was determined with the first two PCRs of the Kondo scheme. Results: DLST analysis indicated that 1884/2036 isolates (92.5%) belong to the four predominant clones. A majority of isolates in each clone harboured an identical SCCmec type: 61/64 (95%) isolates to DLST clone 1−1 SCCmec IV, 1282/1323 (97%) to clone 2−2 SCCmec II, 237/288 (82%) to clone 3−3 SCCmec IV, and 192/209 (92%) to clone 4−4 SCCmec I. Unexpectedly, different SCCmec types were present in a single predominant DLST clone: SCCmec V plus one unusual type in 3 isolates of clone 1−1; SCCmec I, IV, V, VI plus two unusual types in 41 isolates of clone 2−2; SCCmec I, II, VI plus three unusual types in 51 isolates of clone 3−3; and SCCmec II, IV, V plus one unusual type in 17 isolates of clone 4−4. Interestingly, adding a third locus generally did not change the classification of incongruent SCCmec types, suggesting that these SCCmec elements have been acquired locally during the dissemination of the clones. Conclusion: Although the SCCmec diversity within clones was relatively low at a local level, a significant proportion of isolates with different SCCmec have been identified in the four major clones. This suggests that the local acquisition of SCCmec elements is not a rare event and illustrates the great capacity of S. aureus to quickly adapt to its environment by acquiring new genetic elements.
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INTRODUCTION: The 2004 version of the World Health Organization classification subdivides thymic epithelial tumors into A, AB, B1, B2, and B3 (and rare other) thymomas and thymic carcinomas (TC). Due to a morphological continuum between some thymoma subtypes and some morphological overlap between thymomas and TC, a variable proportion of cases may pose problems in classification, contributing to the poor interobserver reproducibility in some studies. METHODS: To overcome this problem, hematoxylin-eosin-stained and immunohistochemically processed sections of prototypic, "borderland," and "combined" thymomas and TC (n = 72) were studied by 18 pathologists at an international consensus slide workshop supported by the International Thymic Malignancy Interest Group. RESULTS: Consensus was achieved on refined criteria for decision making at the A/AB borderland, the distinction between B1, B2, and B3 thymomas and the separation of B3 thymomas from TCs. "Atypical type A thymoma" is tentatively proposed as a new type A thymoma variant. New reporting strategies for tumors with more than one histological pattern are proposed. CONCLUSION: These guidelines can set the stage for reproducibility studies and the design of a clinically meaningful grading system for thymic epithelial tumors.
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111 patients with acute leukemia, including 29 children, were classified according to the surface markers and cytochemistry of their blasts. The acute leukemias were separated into two majors groups (lymphoid and non-lymphoid) depending on the presence or absence of specific lymphoid markers. On the basis of these criteria a correlation of 94% with the hematological diagnosis was obtained. Acute lymphoblastic leukemia (ALL) was divisible into three sub-groups: 11 cases expressing T-cell specific markers were classified as T-ALL and 33 cases expressing the common ALL antigen (CALLA) as c-ALL. 18 of the latter expressed an additional marker, DSA (Daudi surface antigen), splitting c-ALL cases in two subgroups. Cytochemistry of the cases lacking specific surface markers (n = 67) served to diagnose 41 acute myeloid leukemia (AML) cases and 8 monoblastic leukemias. The remaining 18 cases could not be classified. The presence of absence of HLD-DR (Ia) antigens served to subdivide AML into two major subgroups. The prognostic significance of these new diagnostic splits is under active study.
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BACKGROUND: Extensive research exists estimating the effect hazardous alcohol¦use on morbidity and mortality, but little research quantifies the association between¦alcohol consumption and utility scores in patients with alcohol dependence.¦In the context of comparative research, the World Health Organisation (WHO)¦proposed to categorise the risk for alcohol-related acute and chronic harm according¦to patients' average daily alcohol consumption. OBJECTIVES: To estimate utility¦scores associated with each category of the WHO drinking risk-level classification¦in patients with alcohol dependence (AD). METHODS: We used data from¦CONTROL, an observational cohort study including 143 AD patients from the Alcohol¦Treatment Center at Lausanne University Hospital, followed for 12 months.¦Average daily alcohol consumption was assessed monthly using the Timeline Follow-¦back method and patients were categorised according to the WHO drinking¦risk-level classification: abstinent, low, medium, high and very high. Other measures¦as sociodemographic characteristics and utility scores derived from the EuroQoL¦5-Dimensions questionnaire (EQ-5D) were collected every three months.¦Mixed models for repeated measures were used to estimate mean utility scores¦associated with WHO drinking risk-level categories. RESULTS: A total of 143 patients¦were included and the 12-month follow-up permitting the assessment of¦1318 person-months. At baseline the mean age of the patients was 44.6 (SD 11.8)¦and the majority of patients was male (63.6%). Using repeated measures analysis,¦utility scores decreased with increasing drinking levels, ranging from 0.80 in abstinent¦patients to 0.62 in patients with very high risk drinking level (p_0.0001).¦CONCLUSIONS: In this sample of patients with alcohol dependence undergoing¦specialized care, utility scores estimated from the EQ-5D appeared to substantially¦and consistently vary according to patients' WHO drinking level.
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CD34/QBEND10 immunostaining has been assessed in 150 bone marrow biopsies (BMB) including 91 myelodysplastic syndromes (MDS), 16 MDS-related AML, 25 reactive BMB, and 18 cases where RA could neither be established nor ruled out. All cases were reviewed and classified according to the clinical and morphological FAB criteria. The percentage of CD34-positive (CD34 +) hematopoietic cells and the number of clusters of CD34+ cells in 10 HPF were determined. In most cases the CD34+ cell count was similar to the blast percentage determined morphologically. In RA, however, not only typical blasts but also less immature hemopoietic cells lying morphologically between blasts and promyelocytes were stained with CD34. The CD34+ cell count and cluster values were significantly higher in RA than in BMB with reactive changes (p<0.0001 for both), in RAEB than in RA (p=0.0006 and p=0.0189, respectively), in RAEBt than in RAEB (p=0.0001 and p=0.0038), and in MDS-AML than in RAEBt (p<0.0001 and p=0.0007). Presence of CD34+ cell clusters in RA correlated with increased risk of progression of the disease. We conclude that CD34 immunostaining in BMB is a useful tool for distinguishing RA from other anemias, assessing blast percentage in MDS cases, classifying them according to FAB, and following their evolution.
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BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.
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A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.