122 research outputs found

    Understanding Productivity Changes in Public Universities: Evidence from Spain

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    This paper describes the dynamic changes in productivity in Spanish public universities (SPU) in the period 1994 to 2008. The Malmquist index is used to illustrate the contribution of efficiency and technological change to changes in the productivity of university activities. The results indicate that annual productivity growth is attributable more to efficiency improvements than technological progress. Gains in scale efficiency appear to play only a minor role in productivity gains. The fact that technical efficiency contributes more than technological progress suggests that most universities are not operating close to the best-practice frontier.Garcia Aracil, A. (2013). Understanding Productivity Changes in Public Universities: Evidence from Spain. Research Evaluation. 22(5):351-368. doi:10.1093/reseval/rvt009S351368225Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: an analysis of research-related output efficiency. Research Evaluation, 21(1), 2-14. doi:10.1093/reseval/rvr001Agasisti, T., & PĂ©rez-Esparrells, C. (2009). Comparing efficiency in a cross-country perspective: the case of Italian and Spanish state universities. Higher Education, 59(1), 85-103. doi:10.1007/s10734-009-9235-8ARCELUS‡, F. J., & Coleman‡§, D. F. (1997). An efficiency review of university departments. International Journal of Systems Science, 28(7), 721-729. doi:10.1080/00207729708929431Athanassopoulos, A. D., & Shale, E. (1997). Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis. Education Economics, 5(2), 117-134. doi:10.1080/09645299700000011Attewell, P., Heil, S., & Reisel, L. (2012). What Is Academic Momentum? And Does It Matter? Educational Evaluation and Policy Analysis, 34(1), 27-44. doi:10.3102/0162373711421958Balk, B. M. (1993). Malmquist Productivity Indexes and Fisher Ideal Indexes: Comment. The Economic Journal, 103(418), 680. doi:10.2307/2234540Beasley, J. E. (1990). Comparing university departments. Omega, 18(2), 171-183. doi:10.1016/0305-0483(90)90064-gBeasley, J. E. (1995). Determining Teaching and Research Efficiencies. Journal of the Operational Research Society, 46(4), 441-452. doi:10.1057/jors.1995.63Bessent, A. M., & Bessent, E. W. (1980). Determining the Comparative Efficiency of Schools through Data Envelopment Analysis. Educational Administration Quarterly, 16(2), 57-75. doi:10.1177/0013161x8001600207Bonaccorsi, A., Daraio, C., & Simar, L. (2006). Advanced indicators of productivity of universitiesAn application of robust nonparametric methods to Italian data. Scientometrics, 66(2), 389-410. doi:10.1007/s11192-006-0028-xBonaccorsi, A., Daraio, C., Lepori, B., & SlipersĂŠter, S. (2007). Indicators on individual higher education institutions: addressing data problems and comparability issues. Research Evaluation, 16(2), 66-78. doi:10.3152/095820207x218141Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393. doi:10.2307/1913388Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Coelli, T., & Perelman, S. (1999). A comparison of parametric and non-parametric distance functions: With application to European railways. European Journal of Operational Research, 117(2), 326-339. doi:10.1016/s0377-2217(98)00271-9Cohn, E., Rhine, S. L. W., & Santos, M. C. (1989). Institutions of Higher Education as Multi-Product Firms: Economies of Scale and Scope. The Review of Economics and Statistics, 71(2), 284. doi:10.2307/1926974COSTAS, R., & BORDONS, M. (2007). The h-index: Advantages, limitations and its relation with other bibliometric indicators at the micro level. Journal of Informetrics, 1(3), 193-203. doi:10.1016/j.joi.2007.02.001De Groot, H., McMahon, W. W., & Volkwein, J. F. (1991). The Cost Structure of American Research Universities. The Review of Economics and Statistics, 73(3), 424. doi:10.2307/2109566Fïżœre, R., Grosskopf, S., & Lovell, C. A. K. (1992). Indirect productivity measurement. Journal of Productivity Analysis, 2(4), 283-298. doi:10.1007/bf00156471Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Flegg, A. T., & Allen, D. O. (2007). Does Expansion Cause Congestion? The Case of the Older British Universities, 1994–2004. Education Economics, 15(1), 75-102. doi:10.1080/09645290601133928FLEGG, A. T., ALLEN, D. O., FIELD, K., & THURLOW, T. W. (2004). Measuring the efficiency of British universities: a multi‐period data envelopment analysis. Education Economics, 12(3), 231-249. doi:10.1080/0904529042000258590GarcĂ­a-Aracil, A., & Palomares-Montero, D. (2009). Examining benchmark indicator systems for the evaluation of higher education institutions. Higher Education, 60(2), 217-234. doi:10.1007/s10734-009-9296-8GarcĂ­a-Aracil, A., & Palomares-Montero, D. (2012). Indicadores para la evaluaciĂłn de las instituciones universitarias: validaciĂłn a travĂ©s del mĂ©todo Delphi. Revista española de DocumentaciĂłn CientĂ­fica, 35(1), 119-144. doi:10.3989/redc.2012.1.863GimĂ©nez, V. M., & MartĂ­nez, J. L. (2006). Cost efficiency in the university: A departmental evaluation model. Economics of Education Review, 25(5), 543-553. doi:10.1016/j.econedurev.2005.05.006Glass, J. C., McKillop, D. G., & O’Rourke, G. (1998). Journal of Productivity Analysis, 10(2), 153-175. doi:10.1023/a:1018607223276Grifell-TatjĂ©, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81-101. doi:10.1007/bf02564713Grosskopf, S., Margaritis, D., & Valdmanis, V. (1995). Estimating output substitutability of hospital services: A distance function approach. European Journal of Operational Research, 80(3), 575-587. doi:10.1016/0377-2217(94)00138-3JimĂ©nez-Contreras, E., de Moya AnegĂłn, F., & LĂłpez-CĂłzar, E. D. (2003). The evolution of research activity in Spain. Research Policy, 32(1), 123-142. doi:10.1016/s0048-7333(02)00008-2Johnes, G. (1988). Determinants of research output in economics departments in British universities. Research Policy, 17(3), 171-178. doi:10.1016/0048-7333(88)90041-8JOHNES, J. (2008). EFFICIENCY AND PRODUCTIVITY CHANGE IN THE ENGLISH HIGHER EDUCATION SECTOR FROM 1996/97 TO 2004/5*. Manchester School, 76(6), 653-674. doi:10.1111/j.1467-9957.2008.01087.xJohnes, G., & Schwarzenberger, A. (2011). Differences in cost structure and the evaluation of efficiency: the case of German universities. Education Economics, 19(5), 487-499. doi:10.1080/09645291003726442JOHNES, J., & YU, L. (2008). Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Economic Review, 19(4), 679-696. doi:10.1016/j.chieco.2008.08.004Koshal, R. K., & Koshal, M. (1999). Economies of scale and scope in higher education: a case of comprehensive universities. Economics of Education Review, 18(2), 269-277. doi:10.1016/s0272-7757(98)00035-1Kortelainen, M. (2008). Dynamic environmental performance analysis: A Malmquist index approach. Ecological Economics, 64(4), 701-715. doi:10.1016/j.ecolecon.2007.08.001Laudel, G. (2005). Is external research funding a valid indicator for research performance? Research Evaluation, 14(1), 27-34. doi:10.3152/147154405781776300Lovell, C. A. K. (2003). Journal of Productivity Analysis, 20(3), 437-458. doi:10.1023/a:1027312102834Lucas, S. R., & Beresford, L. (2010). Naming and Classifying: Theory, Evidence, and Equity in Education. Review of Research in Education, 34(1), 25-84. doi:10.3102/0091732x09353578Madden, G., Savage, S., & Kemp, S. (1997). Measuring Public Sector Efficiency: A Study of Economics Departments at Australian Universities. Education Economics, 5(2), 153-168. doi:10.1080/09645299700000013Abbott, M., & Doucouliagos, C. (2001). Total factor productivity and efficiency in Australian colleges of advanced education. Journal of Educational Administration, 39(4), 384-393. doi:10.1108/eum0000000005497Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209-242. doi:10.1007/bf03006863Mamun, S. A. K. (2012). Stochastic estimation of cost frontier: evidence from Bangladesh. Education Economics, 20(2), 211-227. doi:10.1080/09645292.2010.494836Maniadakis, N., & Thanassoulis, E. (2004). A cost Malmquist productivity index. European Journal of Operational Research, 154(2), 396-409. doi:10.1016/s0377-2217(03)00177-2Molinero, C. M. (1996). On the Joint Determination of Efficiencies in a Data Envelopment Analysis Context. Journal of the Operational Research Society, 47(10), 1273-1279. doi:10.1057/jors.1996.154Molinero, C. M., & Tsai, P. F. (1997). Some mathematical properties of a DEA model for the joint determination of efficiencies. Journal of the Operational Research Society, 48(1), 51-56. doi:10.1057/palgrave.jors.2600327McLendon, M. K., Hearn, J. C., & Deaton, R. (2006). Called to Account: Analyzing the Origins and Spread of State Performance-Accountability Policies for Higher Education. Educational Evaluation and Policy Analysis, 28(1), 1-24. doi:10.3102/01623737028001001Monk, D. H. (1992). Education Productivity Research: An Update and Assessment of Its Role in Education Finance Reform. Educational Evaluation and Policy Analysis, 14(4), 307-332. doi:10.3102/01623737014004307Nishimizu, M., & Page, J. M. (1982). Total Factor Productivity Growth, Technological Progress and Technical Efficiency Change: Dimensions of Productivity Change in Yugoslavia, 1965-78. The Economic Journal, 92(368), 920. doi:10.2307/2232675Rodrı́guez-Álvarez, A., FernĂĄndez-Blanco, V., & Lovell, C. A. K. (2004). Allocative inefficiency and its cost: International Journal of Production Economics, 92(2), 99-111. doi:10.1016/j.ijpe.2003.08.012Salerno, C. (2006). Using Data Envelopment Analysis to Improve Estimates of Higher Education Institution’s Per‐student Education Costs1. Education Economics, 14(3), 281-295. doi:10.1080/09645290600777485Sarafoglou, N., & Haynes, K. E. (1996). University productivity in Sweden: a demonstration and explanatory analysis for economics and business programs. The Annals of Regional Science, 30(3), 285-304. doi:10.1007/bf01580523Schmoch, U., Schubert, T., Jansen, D., Heidler, R., & von Görtz, R. (2010). How to use indicators to measure scientific performance: a balanced approach. Research Evaluation, 19(1), 2-18. doi:10.3152/095820210x492477New, B. (1997). The rationing debate: Defining a package of healthcare services the NHS is responsible for The case for. BMJ, 314(7079), 498-498. doi:10.1136/bmj.314.7079.498Sinuany-Stern, Z., Mehrez, A., & Barboy, A. (1994). Academic departments efficiency via DEA. Computers & Operations Research, 21(5), 543-556. doi:10.1016/0305-0548(94)90103-1Tomkins, C., & Green, R. (1988). AN EXPERIMENT IN THE USE OF DATA ENVELOPMENT ANALYSIS FOR EVALUATING THE EFFICIENCY OF UK UNIVERSITY DEPARTMENTS OF ACCOUNTING. Financial Accountability and Management, 4(2), 147-164. doi:10.1111/j.1468-0408.1988.tb00066.xUri, N. D. (2003). Technical efficiency in telecommunications in the United States and the impact of incentive regulation. Applied Mathematical Modelling, 27(1), 53-67. doi:10.1016/s0307-904x(02)00098-7Uri, N. D. (2003). The adoption of incentive regulation and its effect on technical efficiency in telecommunications in the United States. International Journal of Production Economics, 86(1), 21-34. doi:10.1016/s0925-5273(03)00002-1Vidal, J. (2003). Quality Assurance, Legal Reforms and the European Higher Education Area in Spain. European Journal of Education, 38(3), 301-313. doi:10.1111/1467-3435.00149Williams, J. D., & Kerckhoff, A. C. (1995). The Challenge of Developing New Educational Indicators. Educational Evaluation and Policy Analysis, 17(1), 113-131. doi:10.3102/01623737017001113Worthington, A. C., & Lee, B. L. (2008). Efficiency, technology and productivity change in Australian universities, 1998–2003. Economics of Education Review, 27(3), 285-298. doi:10.1016/j.econedurev.2006.09.01

    Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

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    This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory) and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise

    Dimensional synthesis of a spherical parallel manipulator based on the evaluation of global performance indexes

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    In this work, the dimensional synthesis of a spherical Parallel Manipulator (PM) with a -1S kinematic chain is presented. The goal of the synthesis is to find a set of parameters that defines the PM with the best performance in terms of workspace capabilities, dexterity and isotropy. The PM is parametrized in terms of a reference element, and a non-directed search of these parameters is carried out. First, the inverse kinematics and instantaneous kinematics of the mechanism are presented. The latter is found using the screw theory formulation. An algorithm that explores a bounded set of parameters and determines the corresponding value of global indexes is presented. The concepts of a novel global performance index and a compound index are introduced. Simulation results are shown and discussed. The best PMs found in terms of each performance index evaluated are locally analyzed in terms of its workspace and local dexterity. The relationship between the performance of the PM and its parameters is discussed, and a prototype with the best performance in terms of the compound index is presented and analyzed

    Widespread Detection of Yersiniabactin Gene Cluster and Its Encoding Integrative Conjugative Elements (ICEKp) among Nonoutbreak OXA-48-Producing Klebsiella pneumoniae Clinical Isolates from Spain and the Netherlands

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    In this study, we determined the presence of virulence factors in nonoutbreak, high-risk clones and other isolates belonging to less common sequence types associated with the spread of OXA-48-producing Klebsiella pneumoniae clinical isolates from The Netherlands (n = 61) and Spain (n = 53). Most isolates shared a chromosomally encoded core of virulence factors, including the enterobactin gene cluster, fimbrial fim and mrk gene clusters, and urea metabolism genes (ureAD). We observed a high diversity of K-Locus and K/O loci combinations, KL17 and KL24 (both 16%), and the O1/O2v1 locus (51%) being the most prevalent in our study. The most prevalent accessory virulence factor was the yersiniabactin gene cluster (66.7%). We found seven yersiniabactin lineages-ybt 9, ybt 10, ybt 13, ybt 14, ybt 16, ybt 17, and ybt 27-which were chromosomally embedded in seven integrative conjugative elements (ICEKp): ICEKp3, ICEKp4, ICEKp2, ICEKp5, ICEKp12, ICEKp10, and ICEKp22, respectively. Multidrug-resistant lineages-ST11, ST101, and ST405-were associated with ybt 10/ICEKp4, ybt 9/ICEKp3, and ybt 27/ICEKp22, respectively. The fimbrial adhesin kpi operon (kpiABCDEFG) was predominant among ST14, ST15, and ST405 isolates, as well as the ferric uptake system kfuABC, which was also predominant among ST101 isolates. No convergence of hypervirulence and resistance was observed in this collection of OXA-48-producing K. pneumoniae clinical isolates. Nevertheless, two isolates, ST133 and ST792, were positive for the genotoxin colibactin gene cluster (ICEKp10). In this study, the integrative conjugative element, ICEKp, was the major vehicle for yersiniabactin and colibactin gene clusters spreading. IMPORTANCE; Convergence of multidrug resistance and hypervirulence in Klebsiella pneumoniae isolates has been reported mostly related to sporadic cases or small outbreaks. Nevertheless, little is known about the real prevalence of carbapenem-resistant hypervirulent K. pneumoniae since these two phenomena are often separately studied. In this study, we gathered information on the virulent content of nonoutbreak, high-risk clones (i.e., ST11, ST15, and ST405) and other less common STs associated with the spread of OXA-48-producing K. pneumoniae clinical isolates. The study of virulence content in nonoutbreak isolates can help us to expand information on the genomic landscape of virulence factors in K. pneumoniae population by identifying virulence markers and their mechanisms of spread. Surveillance should focus not only on antimicrobial resistance but also on virulence characteristics to avoid the spread of multidrug and (hyper)virulent K. pneumoniae that may cause untreatable and more severe infections.This study was supported by Plan Nacional de I+D+i 2013-2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía, Industria y Competitividad, Spanish Network for Research in Infectious Diseases (REIPI RD16CIII/0004/0002), cofinanced by European Development Regional Fund ERDF “A Way To Achieve Europe,” operative program Intelligent Growth 2014-2020. This study was also supported by a grant from the Instituto de Salud Carlos III (grant MPY 1135/16) and by the Antibiotic Resistance Surveillance Program of the Centro Nacional de Microbiología (Instituto de Salud Carlos III, Ministerio de Economía y Competitividad) of Spain. The Dutch CPE surveillance was funded by Dutch Ministry of Health, Welfare, and Sports.S

    Rate and duration of hospitalisation for acute pulmonary embolism in the real-world clinical practice of different countries : Analysis from the RIETE registry

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    Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

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    Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≄1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≀6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

    The management of acute venous thromboembolism in clinical practice. Results from the European PREFER in VTE Registry

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    Venous thromboembolism (VTE) is a significant cause of morbidity and mortality in Europe. Data from real-world registries are necessary, as clinical trials do not represent the full spectrum of VTE patients seen in clinical practice. We aimed to document the epidemiology, management and outcomes of VTE using data from a large, observational database. PREFER in VTE was an international, non-interventional disease registry conducted between January 2013 and July 2015 in primary and secondary care across seven European countries. Consecutive patients with acute VTE were documented and followed up over 12 months. PREFER in VTE included 3,455 patients with a mean age of 60.8 ± 17.0 years. Overall, 53.0 % were male. The majority of patients were assessed in the hospital setting as inpatients or outpatients (78.5 %). The diagnosis was deep-vein thrombosis (DVT) in 59.5 % and pulmonary embolism (PE) in 40.5 %. The most common comorbidities were the various types of cardiovascular disease (excluding hypertension; 45.5 %), hypertension (42.3 %) and dyslipidaemia (21.1 %). Following the index VTE, a large proportion of patients received initial therapy with heparin (73.2 %), almost half received a vitamin K antagonist (48.7 %) and nearly a quarter received a DOAC (24.5 %). Almost a quarter of all presentations were for recurrent VTE, with >80 % of previous episodes having occurred more than 12 months prior to baseline. In conclusion, PREFER in VTE has provided contemporary insights into VTE patients and their real-world management, including their baseline characteristics, risk factors, disease history, symptoms and signs, initial therapy and outcomes

    Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

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    BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362
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