Derivation and validation of a risk adjustment model for predicting seven day mortality in emergency medical admissions: mixed prospective and retrospective cohort study

Objectives To derive and validate a risk adjustment model for predicting seven day mortality in emergency medical admissions, to test the value of including physiology and blood parameters, and to explore the constancy of the risk associated with each model variable across a range of settings

Evaluating research efficiency within National R&D Programmes

Relying on efficiency analysis, we evaluate to what extent policy makers have been able to promote the establishment of consolidated and comprehensive research groups to contribute to the implementation of a successful innovation system for the Spanish food technology sector, oriented to the production of knowledge based on an application model. Using data envelopment analysis techniques that allow calculation of a generalized version of the traditional distance function model for productive efficiency, we find pervasive levels of inefficiency and a typology of different research strategies. Among these, in contrast to what has been assumed, established groups do not play the pre-eminent benchmarking role; rather, partially oriented, specialized and "shooting star" groups are the most common patterns. These results correspond with an infant innovation system, where the fostering of higher levels of efficiency and the promotion of the desired research patterns are ongoing. (C) 2010 Elsevier B.V. All rights reserved.Jiménez-Sáez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL.; Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy. 40(2):230-241. doi:10.1016/j.respol.2010.10.005S23024140

Associations of ATR and CHEK1 Single Nucleotide Polymorphisms with Breast Cancer

DNA damage and replication checkpoints mediated by the ATR-CHEK1 pathway are key to the maintenance of genome stability, and both ATR and CHEK1 have been proposed as potential breast cancer susceptibility genes. Many novel variants recently identified by the large resequencing projects have not yet been thoroughly tested in genome-wide association studies for breast cancer susceptibility. We therefore used a tagging SNP (tagSNP) approach based on recent SNP data available from the 1000 genomes projects, to investigate the roles of ATR and CHEK1 in breast cancer risk and survival. ATR and CHEK1 tagSNPs were genotyped in the Sheffield Breast Cancer Study (SBCS; 1011 cases and 1024 controls) using Illumina GoldenGate assays. Untyped SNPs were imputed using IMPUTE2, and associations between genotype and breast cancer risk and survival were evaluated using logistic and Cox proportional hazard regression models respectively on a per allele basis. Significant associations were further examined in a meta-analysis of published data or confirmed in the Utah Breast Cancer Study (UBCS). The most significant associations for breast cancer risk in SBCS came from rs6805118 in ATR (p=7.6x10-5) and rs2155388 in CHEK1 (p=3.1x10-6), but neither remained significant after meta-analysis with other studies. However, meta-analysis of published data revealed a weak association between the ATR SNP rs1802904 (minor allele frequency is 12%) and breast cancer risk, with a summary odds ratio (confidence interval) of 0.90 (0.83-0.98) [p=0.0185] for the minor allele. Further replication of this SNP in larger studies is warranted since it is located in the target region of 2 microRNAs. No evidence of any survival effects of ATR or CHEK1 SNPs were identified. We conclude that common alleles of ATR and CHEK1 are not implicated in breast cancer risk or survival, but we cannot exclude effects of rare alleles and of common alleles with very small effect sizes

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138

pyclarity

pyclarity is a software suite for machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise (Clarity) and speech-in-music (Cadenza). Files can be accessed via the Related Materials links below.</p

Comparison of the clinical and pathologic staging in patients undergoing radical cystectomy for bladder cancer

PURPOSE: Radical cystectomy (RCx) is perhaps the most effective therapeutic approach for patients with muscle-invasive bladder cancer. Unfortunately, clinical staging is imprecise and the degree of understaging remains high. This study retrospectively evaluated patients undergoing RCx with regard to pathologic outcomes and degree of upstaging to better identify features that may lessen clinical understaging. MATERIALS AND METHODS: 141 consecutive patients with urothelial bladder carcinoma who were candidates for RCx with curative intent were retrospectively evaluated. Preoperative clinical and pathological (i.e. TURBT) features were compared to pathological outcomes in the cystectomy specimen. Patients were also evaluated as to whether cystectomy was performed as their primary (n = 91) versus secondary (n = 50) treatment for recurrent/progressive disease. Date of cystectomy ( 5 years prior to study) was also analyzed. RESULTS: Of the 141 patients, 54% were upstaged on operative pathology. The greatest degree of upstaging occurred in those with invasive disease preoperatively (cT2-T3). Twenty-six percent of all patients had node-positive disease, and 75% of cT3 patients were node-positive. Seven of 101 (7%) patients with clinical T2 disease were unresectable at the time of surgery. In the primary (vs. secondary) RCx group, more patients were upstaged (63% vs. 40%), non-organ confined (62% vs. 38%), and LN positive (31% vs. 20%). In the more modern cohort, the degree of upstaging was not improved. CONCLUSIONS: Pathologic findings after RCx often do not correlate with preoperative staging. Over half of patients undergoing cystectomy are upstaged on their operative pathology. An improved understanding of the relative frequency of upstaging in cystectomy patients may have important implications in the decision-making and selection for neoadjuvant and adjuvant therapies for these high-risk populations