Briefing 29: NCMP data 2008/9, East of England: ONS SOA classification

One in a series of six data briefings based on regional-level analysis of data from the National Child Measurement Programme (NCMP) undertaken by the National Obesity Observatory (NOO). The briefings are intended to complement the headline results for the region published in January 2010, at Quick Link 20510.

Documentation of fracture severity with the AO classification of pediatric long-bone fractures.

BACKGROUND: The AO comprehensive pediatric longbone fracture classification system describes the localization and morphology of fractures, and considers severity in 3 categories: (1) simple, (2) wedge, and (3) complex. We evaluated the reliability and accuracy of surgeons in using this rating system. MATERIAL AND METHODS: In a first validation phase, 5 experienced pediatric (orthopedic) surgeons reviewed radiographs of 267 prospectively collected pediatric fractures (agreement study A). In a second study (B), 70 surgeons of various levels of experience in 15 clinics classified 275 fractures via internet. Simple fractures comprised about 90%, 99% and 100% of diaphyseal (D), metaphyseal (M), and epiphyseal (E) fractures, respectively. RESULTS: Kappa coefficients for severity coding in D fractures were 0.82 and 0.51 in studies A and B, respectively. The median accuracy of surgeons in classifying simple fractures was above 97% in both studies but was lower, 85% (46-100), for wedge or complex D fractures. INTERPRETATION: While reliability and accuracy estimates were satisfactory as a whole, the ratings of some individual surgeons were inadequate. Our findings suggest that the classification of fracture severity in children should be done in only two categories that distinguish between simple and wedge/complex fractures.

Classification and management of seeds in retinoblastoma. Ellsworth Lecture Ghent August 24th 2013.

Retinoblastoma has the unique capacity to accelerate its own intra-ocular propagation by adopting semi-solid or even liquid growth properties through seeding. Until recently, the presence of any degree of seeding was mostly incompatible with successful conservative management, due to the multiresistant nature of the seeds. Surprisingly, this well-recognized retinoblastoma behavior has not undergone any detailed description of seeding patterns and anatomic sites. In this paper, we describe the phenotypic variability of seeds across the four possible intraocular seeding compartments and classify them into three fundamental types: namely dust, spheres, and clouds. We also provide an overview of the different therapeutic strategies developed for seeding, with special attention to intravitreal chemotherapy as the treatment of choice for vitreous and retro-hyaloid seeding. Finally, we propose criteria to enable assessment of the response to treatment by reporting seed regression patterns, as well as a clinical grading system for the retinal toxicity observed following intravitreal melphalan.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Health problems among detainees in Switzerland: a study using the ICPC-2 classification.

BACKGROUND: Little is known about the health status of prisoners in Switzerland. The aim of this study was to provide a detailed description of the health problems presented by detainees in Switzerland's largest remand prison. METHODS: In this retrospective cross-sectional study we reviewed the health records of all detainees leaving Switzerland's largest remand prison in 2007. The health problems were coded using the International Classification for Primary Care (ICPC-2). Analyses were descriptive, stratified by gender. RESULTS: A total of 2195 health records were reviewed. Mean age was 29.5 years (SD 9.5); 95% were male; 87.8% were migrants. Mean length of stay was 80 days (SD 160). Illicit drug use (40.2%) and mental health problems (32.6%) were frequent, but most of these detainees (57.6%) had more generic primary care problems, such as skin (27.0%), infectious diseases (23.5%), musculoskeletal (19.2%), injury related (18.3%), digestive (15.0%) or respiratory problems (14.0%). Furthermore, 7.9% reported exposure to violence during arrest by the police. CONCLUSION: Morbidity is high in this young, predominantly male population of detainees, in particular in relation to substance abuse. Other health problems more commonly seen in general practice are also frequent. These findings support the further development of coordinated primary care and mental health services within detention centers.

Pathophysiology and classification of the vibration white finger.

Skin, arteries and nerves of the upper extremities can be affected by vibration exposure. Recent advances in skin and vascular biology as well as new investigative methods, have shown that neurovascular symptoms may be due to different vascular and neurological disorders which should be differentiated if proper management is to be evaluated. Three types of vascular disorder can be observed in the vibration white finger: digital organic microangiopathy, a digital vasospastic phenomenon and arterial thrombosis in the upper extremities. An imbalance between endothelin-1 and calcitonin-gene-related peptide is probably responsible for the vasospastic phenomenon. Moreover, paresthesiae can be due to either a diffuse vibration neuropathy or a carpal tunnel syndrome. A precise diagnosis is then necessary to adapt the treatment to individual cases. A classification describing the type and severity of the vascular lesions is presented. Asymptomatic lesions are included for adequate epidemiological studies and risk assessment of vibrating tools. Monitoring of vibration exposed workers should include not only a questionnaire about symptoms, but also a clinical evaluation including diagnostic tests for the screening of early asymptomatic neurovascular injuries.

An iterative Multiscale Finite-Volume algorithm converging to the exact solution

The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Speculative parallelization of multipath radiosity algorithm

In computer graphics, global illumination algorithms take into account not only the light that comes directly from the sources, but also the light interreflections. This kind of algorithms produce very realistic images, but at a high computational cost, especially when dealing with complex environments. Parallel computation has been successfully applied to such algorithms in order to make it possible to compute highly-realistic images in a reasonable time. We introduce here a speculation-based parallel solution for a global illumination algorithm in the context of radiosity, in which we have taken advantage of the hierarchical nature of such an algorithm