Medulloblastoma incidence has not changed over time: A CBTRUS study

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 9058-9058
Author(s):  
P. G. Fisher ◽  
E. K. Curran ◽  
K. L. Cobb ◽  
G. M. Le ◽  
J. M. Propp
Keyword(s):  
The United States ◽  
Joinpoint Regression ◽  
Statistical Research ◽  
Data Set ◽  
Tumor Registry ◽  
Incidence Trends ◽  
Data Registry ◽  
Definition Of ◽  
Acute Changes

9058 Background: Past studies of medulloblastoma (MB) present conflicting claims about declines and rises in MB incidence, possibly due to misclassification. By using a strict classification of the disease and a rigorous analysis of a data registry, we aimed to determine the incidence trends of MB over the last three decades. Methods: 441 MB patients diagnosed between 1985 and 2002 were identified from the Central Brain Tumor Registry of the United States (CBTRUS), a data set representing approximately 5% of the American population (6 registries). MB was strictly defined and non-cerebellar embryonal tumors (primitive neuro-ectodermal tumors [PNETs]) excluded, using histology and site codes. Multiplicative Poisson regression and joinpoint regression were performed (Joinpoint Regression Program, version 3.0, Statistical Research and Applications Branch, National Cancer Institute) to determine the estimated average annual percentage change (EAPC) and sharp (i.e., acute) changes in incidence, respectively. Results: A slight but nonsignificant (p=.18) increase in medulloblastoma was demonstrated (EAPC = 1.1), and no sharp changes in incidence were found (joinpoints = 0). The analysis was repeated with a less strict definition of MB (including non-cerebellar PNETs) and 559 patients were identified. Using this broader classification scheme, there was a statistically significant increase in incidence (p=.02, EAPC = 1.6), but no sharp changes in incidence (joinpoints = 0). Conclusions: MB incidence does not appear to have changed since the 1980s. “Medulloblastoma” incidence increased only when the diagnosis was not strictly defined and misclassified by including non-cerebellar PNETs in the analysis. The observed increase in the combined MB/PNET classification may relate to the PNET hypothesis–a proposal that all brain tumors of apparently undifferentiated neuroepithelial cells be considered a unique diagnostic group–popularized in the 1980s and early 1990s. No significant financial relationships to disclose.

scholarly journals The Banff Working Group Classification of Definitive Polyomavirus Nephropathy: Morphologic Definitions and Clinical Correlations

2017 ◽  
Vol 29 (2) ◽  
pp. 680-693 ◽  
Author(s):  
Volker Nickeleit ◽  
Harsharan K. Singh ◽  
Parmjeet Randhawa ◽  
Cinthia B. Drachenberg ◽  
Ramneesh Bhatnagar ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Working Group ◽  
Classification Scheme ◽  
Clinical Presentation ◽  
Interstitial Fibrosis ◽  
The United States ◽  
Systematic Analysis ◽  
Definition Of ◽  
Morphologic Classification

Polyomavirus nephropathy (PVN) is a common viral infection of renal allografts, with biopsy-proven incidence of approximately 5%. A generally accepted morphologic classification of definitive PVN that groups histologic changes, reflects clinical presentation, and facilitates comparative outcome analyses is lacking. Here, we report a morphologic classification scheme for definitive PVN from the Banff Working Group on Polyomavirus Nephropathy, comprising nine transplant centers in the United States and Europe. This study represents the largest systematic analysis of definitive PVN undertaken thus far. In a retrospective fashion, clinical data were collected from 192 patients and correlated with morphologic findings from index biopsies at the time of initial PVN diagnosis. Histologic features were centrally scored according to Banff guidelines, including additional semiquantitative histologic assessment of intrarenal polyomavirus replication/load levels. In-depth statistical analyses, including mixed effects repeated measures models and logistic regression, revealed two independent histologic variables to be most significantly associated with clinical presentation: intrarenal polyomavirus load levels and Banff interstitial fibrosis ci scores. These two statistically determined histologic variables formed the basis for the definition of three PVN classes that correlated strongest with three clinical parameters: presentation at time of index biopsy, serum creatinine levels/renal function over 24 months of follow-up, and graft failure. The PVN classes 1–3 as described here can easily be recognized in routine renal biopsy specimens. We recommend using this morphologic PVN classification scheme for diagnostic communication, especially at the time of index diagnosis, and in scientific studies to improve comparative data analysis.

scholarly journals Wetlands and Environmental Legislation Issues

1994 ◽  
Vol 26 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Roy R. Carriker
Keyword(s):  
United States ◽  
United States Army ◽  
Clean Water Act ◽  
The United States ◽  
Army Corps ◽  
Clean Water ◽  
Army Corps Of Engineers ◽  
Section 404 ◽  
Definition Of

AbstractThe federal government program for wetlands regulation is administered by the United States Army Corps of Engineers pursuant to Section 404 of the Clean Water Act. Proposals for amending and/or reforming the Section 404 program are included in Congressional deliberations regarding Clean Water Act reauthorization. Specific issues of public policy include the definition of “waters of the United States”, criteria for delineation of jurisdictional wetlands, definition of activities exempt from regulation, mitigation and classification of wetlands, and issues of property rights.

What Is Neglect? State Legal Definitions in the United States

2018 ◽  
Vol 23 (3) ◽  
pp. 303-315 ◽  
Author(s):  
Rebecca Rebbe
Keyword(s):  
United States ◽  
Cluster Analysis ◽  
Child Maltreatment ◽  
District Of Columbia ◽  
The United States ◽  
Policy Implications ◽  
Data Set ◽  
Standard Definition ◽  
Welfare Systems ◽  
Definition Of

Neglect is the most common form of reported child maltreatment in the United States with 75.3% of confirmed child maltreatment victims in 2015 neglected. Despite constituting the majority of reported child maltreatment cases and victims, neglect still lacks a standard definition. In the United States, congruent with the pervasiveness of law in child welfare systems, every state and the District of Columbia has its own statutory definition of neglect. This study used content analysis to compare state legal statutory definitions with the Fourth National Incidence Survey (NIS-4) operationalization of neglect. The resulting data set was then analyzed using cluster analysis, resulting in the identification of three distinct groups of states based on how they define neglect: minimal, cornerstones, and expanded. The states’ definitions incorporate few of the NIS-4 components. Practice and policy implications of these constructions of neglect definitions are discussed.

scholarly journals Diagnosis of Medical Images Using Cloud-Deep Learning System

2021 ◽  
Vol 10 (2) ◽  
pp. 155
Author(s):  
Michael Jacobs ◽  
Ali Arfan ◽  
Alaa Sheta
Keyword(s):  
Brain Tumors ◽  
Medical Images ◽  
The United States ◽  
Classification Performance ◽  
Input Image ◽  
Image Resolution ◽  
Learning System ◽  
Data Set ◽  
Mri Scans

Diagnosis of brain tumors is one of the most severe medical problems that affect thousands of people each year in the United States. Manual classification of cancerous tumors through examination of MRI images is a difficult task even for trained professionals. It is an error-prone procedure that is dependent on the experience of the radiologist. Brain tumors, in particular, have a high level of complexity.  Therefore, computer-aided diagnosis systems designed to assist with this task are of specific interest for physicians. Accurate detection and classification of brain tumors via magnetic resonance imaging (MRI) examination is a famous approach to analyze MRI images. This paper proposes a method to classify different brain tumors using a Convolutional Neural Network (CNN). We explore the performance of several CNN architectures and examine if decreasing the input image resolution affects the model's accuracy. The dataset used to train the model has initially been 3064 MRI scans. We augmented the data set to 8544 MRI scans to balance the available classes of images. The results show that the design of a suitable CNN architecture can significantly better diagnose medical images. The developed model classification performance was up to 97\% accuracy.

scholarly journals Big Data, Big Noise

2016 ◽  
Vol 35 (4) ◽  
pp. 427-443 ◽  
Author(s):  
Annie Waldherr ◽  
Daniel Maier ◽  
Peter Miltner ◽  
Enrico Günther
Keyword(s):  
Big Data ◽  
Learning Algorithm ◽  
The United States ◽  
Automated Classification ◽  
Core Network ◽  
Data Set ◽  
Core Networks ◽  
Test Corpus ◽  
Web Research

In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.

scholarly journals Changing Patterns of Anal Canal Carcinoma in the United States

2013 ◽  
Vol 31 (12) ◽  
pp. 1569-1575 ◽  
Author(s):  
Rebecca A. Nelson ◽  
Alexandra M. Levine ◽  
Leslie Bernstein ◽  
David D. Smith ◽  
Lily L. Lai
Keyword(s):  
Anal Canal ◽  
Sexual Behaviors ◽  
Anal Carcinoma ◽  
The United States ◽  
Incidence Rates ◽  
Annual Percent Change ◽  
Data Set ◽  
Incidence Trends ◽  
Anal Adenocarcinoma ◽  
Time Period

Purpose Persistent human papillomavirus infection is associated with squamous cell carcinoma of the anal canal (SCCA). With changing sexual behaviors, SCCA incidence and patient demographics may also have changed in recent years. Methods The Surveillance, Epidemiology, and End Results public-use data set from 1973 to 2009 was analyzed to determine incidence trends for and demographic factors characterizing SCCA. Joinpoint analyses identified time points when incidence rates changed. For comparison, similar analyses were conducted for anal adenocarcinoma. Results Joinpoint analyses identified 1997 as the single inflection point among 11,231 patients with SCCA, at which the slope of incidence rates statistically increased (1997 to 2009 v 1973 to 1996: risk ratio [RR], 2.2; 95% CI, 2.1 to 2.3). Annual percent change (APC) increased for all SCCA stages and was the greatest for anal carcinoma in situ (CIS; APC, 14.2; 95% CI, 10.2 to 18.4). Demographic changes characterizing later versus earlier time period included younger age at diagnosis and rising incidence rates in all stage, sex, and racial groups. During 1997 to 2009, women were less likely to present with CIS (RR, 0.3; 95% CI, 0.3 to 0.3) but more likely to present with localized (RR, 1.2; 95% CI, 1.1 to 1.3) and regional SCCA (RR, 1.5; 95% CI, 1.4 to 1.7). In contrast, adenocarcinoma APCs among 1,791 patients remained stable during this time period. Conclusion CIS and SCCA incidence increased dramatically after 1997 for men and women, although men were more likely to be diagnosed with CIS. These changes likely resulted from available screening in men and argue for efforts to identify high-risk individuals who may benefit from screening.

scholarly journals Definition of Digital Economy for the Purposes of Statistical Research

2020 ◽  
Vol 27 (4) ◽  
pp. 66-79
Author(s):  
E. N. Klochkova ◽  
P. E. Prokhorov
Keyword(s):  
International Organizations ◽  
English Language ◽  
Digital Economy ◽  
Russian Language ◽  
Statistical Methodology ◽  
Statistical Research ◽  
Wide Range ◽  
Definition Of ◽  
Key Terms

The international agenda for the development of monitoring of digital transformation stimulates the development of a statistical methodology for measuring phenomena and processes in the digital economy. The barrier to the formation of a set of interrelated and comparable indicators of the development of the digital economy is the lack of a definition of this concept that would satisfy all interested parties.To date, experts from various fields of science have proposed a wide range of definitions of the digital economy. Based on the various properties and effects of digitalization, experts characterize the digital economy not only as a phenomenon in economic activity but also as a sociocultural phenomenon. Many of the proposed definitions are theoretical in nature and do not fully satisfy the needs of empirical research.The objective of this study is to formulate a definition of the digital economy for statistical research.Therefore, the authors reviewed the works analyzing various definitions of the digital economy, and, accordingly, the sources that proposed definitions of the digital economy.Due to plethora of digital economy definitions, to reveal the essence of this concept the authors used statistical text analysis (Text Mining) tools, which made it possible to identify a set of key terms defining digital economy. For analysis, was built a text corpus, consisting of 105 English-language and Russian-language definitions taken from various sources, including documents of state strategic planning, publications of international organizations, analytical materials of consulting companies and articles of individual authors.Thus, the study analyzed the concept of the digital economy for the period and nature of origin, and also proposed an approach based on which an operational definition of the digital economy was developed that meets the relevant principles. However, to identify the digital economy as an object of statistical research, along with the proposed definition, it is necessary to develop a classification of elements of the infrastructure of the digital economy, all of which will allow the formation of an agreed system of tatistical indicators.

Development and Evaluation of Fuzzy Criteria for the Diagnosis of Rheumatoid Arthritis

1996 ◽  
Vol 35 (04/05) ◽  
pp. 334-342 ◽  
Author(s):  
K.-P. Adlassnig ◽  
G. Kolarz ◽  
H. Leitich
Keyword(s):  
Rheumatoid Arthritis ◽  
Reference Model ◽  
American Rheumatism Association ◽  
Uniform Definition ◽  
Sensitivity Rate ◽  
Fuzzy Techniques ◽  
Definition Of ◽  
Different Levels ◽  
Specificity Rate

Abstract:In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.

ABOUT THE PROBLEM OF CLASSIFICATION OF THE CONCEPTS OF INFORMATICS STUDIED AT SECONDARY SCHOOL

2018 ◽  
pp. 4-7
Author(s):  
S. I. Zenko
Keyword(s):  
Secondary School ◽  
Foreign Language ◽  
Computer Science ◽  
Parts Of Speech ◽  
The Third ◽  
The Cross ◽  
Definition Of

The article raises the problem of classification of the concepts of computer science and informatics studied at secondary school. The efficiency of creation of techniques of training of pupils in these concepts depends on its solution. The author proposes to consider classifications of the concepts of school informatics from four positions: on the cross-subject basis, the content lines of the educational subject "Informatics", the logical and structural interrelations and interactions of the studied concepts, the etymology of foreign-language and translated words in the definition of the concepts of informatics. As a result of the first classification general and special concepts are allocated; the second classification — inter-content and intra-content concepts; the third classification — stable (steady), expanding, key and auxiliary concepts; the fourth classification — concepts-nouns, conceptsverbs, concepts-adjectives and concepts — combinations of parts of speech.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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