Radiologists' vs. surgeons' reading performance in non traumatic acute abdomen CTs : P32

Purpose: Emergency room reading performances have been a point of interest in recent studies comparing radiologists to other physician groups. Our objective was to evaluate and compare the reading performances of radiologists and surgeons in an emergency room setting of non-traumatic abdominal CTs. Methods and materials: A total of ten readers representing four groups participated in this study: three senior radiologists and visceral surgeons, respectively, and two junior radiologists and surgeons, respectively. Each observer blindedly evaluated a total of 150 multi-slice acute abdominal CTs. CTs were chosen representing established proportions of acute abdomen pathologies in a Level I trauma centre from 2003 to 2005. Each answer was interpretated as right or wrong regarding pathology location, diagnosis and need for operation. Gold standard was the intraoperative result, and the clinical patient follow-up for non-operated patients. Significance was assumed at a p <.05 level. Results: Senior radiologists had a mean score of 2.38 ± 1.14, junior radiologists a score of 2.34 ± 1.14, whereas senior surgeons scored 2.07 ± 1.30 and junior surgeons 1.62 ± 1.42. No significant difference was found between the two radiologist groups, but results were significantly better for senior surgeons as compared to junior surgeons and better for the two radiologist groups as compared to each of the surgeon groups (all p <.05). Conclusion: Abdominal CT reading in an acute abdomen setting should continue to rely on an evaluation by a radiologist, whether senior or junior. Satisfying reading results can be achieved by senior visceral surgeons, but junior surgeons need more experience for a good reading performance.

Analysis of a finite volume element method for the Stokes problem

In this paper we propose a stabilized conforming finite volume element method for the Stokes equations. On stating the convergence of the method, optimal a priori error estimates in different norms are obtained by establishing the adequate connection between the finite volume and stabilized finite element formulations. A superconvergence result is also derived by using a postprocessing projection method. In particular, the stabilization of the continuous lowest equal order pair finite volume element discretization is achieved by enriching the velocity space with local functions that do not necessarily vanish on the element boundaries. Finally, some numerical experiments that confirm the predicted behavior of the method are provided.

Inhibition of mouse mammary tumor virus-induced T cell responses in vivo by antibodies to an open reading frame protein.

Minor lymphocyte stimulating (Mls) antigens specifically stimulate T cell responses that are restricted to particular T cell receptor (TCR) beta chain variable domains. The Mls phenotype is genetically controlled by an open reading frame (orf) located in the 3' long terminal repeat of mouse mammary tumor virus (MMTV); however, the mechanism of action of the orf gene product is unknown. Whereas predicted orf amino acid sequences show strong overall homology, the 20-30 COOH-terminal residues are strikingly polymorphic. This polymorphic region correlates with TCR V beta specificity. We have generated monoclonal antibodies to a synthetic peptide encompassing the 19 COOH-terminal amino acid residues of Mtv-7 orf, which encodes the Mls-1a determinant. We show here that these antibodies block Mls responses in vitro and can interfere specifically with thymic clonal deletion of Mls-1a reactive V beta 6+ T cells in neonatal mice. Furthermore, the antibodies can inhibit V beta 6+ T cell responses in vivo to an infectious MMTV that shares orf sequence homology and TCR specificity with Mtv-7. These results confirm the predicted extracellular localization of the orf COOH terminus and imply that the orf proteins of both endogenous and exogenous MMTV interact directly with TCR V beta.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.