dataesr/scientific-paragraphs-categorization huggingface dataset:
End points The following end points will be summarized with timeto-event and event rate per 100 patient-year analyses. Note that this SAP describes the analysis of first phase only. First phase (mortality) 1. All-cause mortality 2. CV mortality 3. Non-CV mortality Adjudication of deaths as CV, non-CV, or unknown cause will be done with 2 different methods, one using the original RECORD CEC definitions and one using contemporary definitions under development by FDA through the Standardized Data Collection for Cardiovascular Trials Initiative. Analyses of CV and non-CV mortality will be done for each method of adjudication. End point status and end point dates for analyses of these end points will come from the DCRI CEC database containing the results of readjudication of identified cases. Last contact dates for vital status will be derived from the following sources: 1.
RECORD database previously submitted by GSK to the FDA 2. Additional CRF data or documentation supplied to the DCRI by GSK on the independent third-party search, the death date will be determined as described in Section 6. 2. 1. 6. 2. 4. 3. DCRI internal review of CRFs and additional documentation. Dates of follow-up or death may be obtained by DCRI review of CRFs or additional study documentation. This source includes, but is not limited to, results of an effort by GSK to confirm survival status on 437 patients withdrawn from CV follow-up and for whom survival status at study end was not obtained from a clinic visit. This effort was conducted from November 2010 to Note: Primary analysis end of observation date is described in Boxes A, C, E, and F. For patients entering Box F, the last date alive is the latest faceto-face visit where vitals were collected.
216. e20 Lopes et al March 2011, after database finalization, and results were documented by insertion of relevant information in individual patient CRF packets. For patients shown to be alive later than the last face-to-face study visit and not reported to have died, the last observation date will be determined by the information from DCRI CRF review. The date of last follow-up will be the latest date known alive on or before December 31, 2008. For patients shown to be dead, the death date will be determined as described in Section 6. 2. 1. 6. 2. 5. Derivation of survival dates from test dates and reported patient events. The electronic database for patients with incomplete survival follow-up for the primary analysis will be screened for dates of specific patient visits or events which are later than last follow-up dates derived in Section 6.
6. 2. 6. Survival status follow-up and third-party searches within the RECORD study. For patients whose vital status after August 24, 2008, cannot be determined by the method described in Sections 6. 2. 3 to 6. 2. 5, an additional method of deriving follow-up dates for mortality analysis will use dates obtained from survival status follow-up and thirdparty searches conducted as part of the RECORD study and included in the electronic database. With this method, the date last observed alive will be defined as the later of • the date last observed alive based on telephone contact with the patient, health care worker, or friend/ relative/neighbor/other person or through third-party sources reported in the RECORD CRFs and • the date last observed alive derived using the method outlined in Section 6.
† ⁎ The DCRI readjudication of events used the event definitions from the original RECORD study CEC. However, event dates were determined by the DCRI CEC based on the adjudicators' clinical judgment, without consideration for rules that may have been used by the original RECORD study CEC. † Analyses of the original RECORD trial data will not be performed. However, a descriptive discussion of previously published results from the RECORD trial and the results from the DCRI independent readjudication will be included in the final study report. death, the date of the fatal MI will be the date of death. Classification of fatal stroke will be done similarly. The MACE composite end point (number 1 in the above list) will include any additional data on CV death obtained after the database lock for the first-phase mortality report, including identification of previously unknown deaths, new information on patient follow-up, or updated classifications of cause of death.
7. RECORD CEC clinical data assistant. Specific responsibilities include, but are not limited to, the following: • Prepare and track event packets • Data enter RECORD adjudication forms into RE-CORD CEC database (InForm) • Generate reports • Assist the CEC coordinator in the logistics and conduct of the CEC Committee adjudication meetings 3. 8. RECORD CEC clinical trial assistant. Specific responsibilities include, but are not limited to, the following:.
7. RECORD CEC clinical data assistant. Specific responsibilities include, but are not limited to, the following: • Prepare and track event packets • Data enter RECORD adjudication forms into RE-CORD CEC database (InForm) • Generate reports • Assist the CEC coordinator in the logistics and conduct of the CEC Committee adjudication meetings 3. 8. RECORD CEC clinical trial assistant. Specific responsibilities include, but are not limited to, the following: • Adjudicate and classify events, with accurate and consistent application of the event criteria • Participate in discussions related to event criteria and the application of the criteria • Participate in adjudication conference calls and meetings • • Develop the RECORD CEC project plan (in concert with team members) to include timelines, processes for clinical research studies, and milestones and recommend amendments to the RECORD CEC Charter as appropriate • Develop and implement training and new processes in CEC to ensure that work flow is consistent with CEC SOPs • Develops performance and process improvement strategies when appropriate 3.
6. DCRI RECORD CEC coordinators. • Work with RECORD Principal Investigator and RECORD CEC chairperson to develop electronic and manual trigger specifications to identify all potential events • Ensure that all events identified from trigger specifications have been reviewed as appropriate 216. e2Lopes et al• • Track source documents into the incoming document center • Track subject folders into the CEC Tracking Database • Assist CEC clinical data assistant to prepare and track event packets as necessary • Assist the RECORD CEC coordinator in the collection of source documents 4. 0. Operations 4. 1. Identification of suspected events.
Data source The REIN design has been described in detail  [13]. The data included in this study were released from the REIN registry data under the responsibility of the authors, and with the approval of the Regional Committee in Limousin. Data gathered for this study included patient demographics, co-morbidities, severe disability, mobility, nutritional status, renal function, anaemia status, and dialysis modality. Primary renal diseases and renal biopsy before dialysis were included. The eight co-morbidities assessed were diabetes, chronic respiratory disease, heart failure, cerebrovascular disease, peripheral vascular disease, arrhythmia, coronary vascular disease and active malignancy. The two vascular risk factors analysed were hypertension and smoker/ex-smoker. Handicaps included severe disabilities (severely impaired vision, amputation, hemiplegia and paraplegia) and severe behavioural disorders (dementia, psychosis, and severe neurosis that may affect patient dependence or compliance with treatment).
Mobility was classified into three groups according to patient dependence. Serum albumin concentrations were measured and body mass index (BMI) was calculated as weight (kg)/square of height (m). Data on mobility and BMI were missing for <5% of patients. An urgent first dialysis was defined as its immediate performance after evaluation by a nephrologist due to risk of vital status, poorly tolerated anaemia, pericarditis or uremic confusion of origin. Renal function was assessed as estimated glomerular filtration rate (eGFR), which was calculated using the modification of diet in renal disease (MDRD) formula. Pre-dialysis anaemia and first treatment modalities were also determined.
Z. Li  and  G. Pap  have been supported by the  Hungarian Chinese Intergovernmental S & T Cooperation Programme  for 2011-2013 under Grant No. 10-1-2011-0079. M. Barczy  and  G. Pap  have been partially supported by the  Hungarian Scientific Research Fund  under Grant No. OTKA T-079128. L. Döring has been supported by the  Foundation Science Matématiques de Paris. Z. Li  has been partially supported by  NSFC  under Grant No. 11131003 and 973 Program  under Grant No. 2011CB808001. and almost sure convergence will be denoted by L -→, P -→ and a. s.
While NIS and sectoral specialization arguments are common in the innovation literature, their application to a spatial interaction framework is less well explored. We provide evidence on the extent to which similar sectoral and technology structures and private (aggregate) R&D expenditure determine similar trends in public R&D spending decisions. We test the existence of spatial interactions related to public R&D expenditures for 14 European countries using panel data for the period  1994-2006. We employ a maximum likelihood technique on a balanced panel dataset and use different spatial matrix specifications, which account for the specificity of public R&D expenditure. First, we find that factors traditionally affecting public expenditure, such as scale of the national economy, trade openness, sectoral specialization and private R&D significantly affect the level of public R&D spending by European countries between 1994 and 2006.
Strategic interactions in public policy A number of empirical studies show the relevance of the theoretical literature on strategic interactions related to fiscal or public expenditure decisions (for an empirical survey, see, e. g. Brueckner, 2003). Generally, observed public fiscal decisions in one region positively depend on public fiscal choices in neighboring or competing regions, leading to the conclusion that public choices are strategic complements. These empirical results emerge from data for the US states and Canadian provinces (e. g. Brett and Pinske, 2000 for Canada, and Brueckner and Saavedra, 2001  for the US) and European subnational government datasets (e. g. ,  Heyndels and Vuchelen, 1998, for Belgium; Buettner, 2001, for Germany; Feld and Reulier, 2005, for Switzerland; Bordignon et al.
, 2003, for Italy; Solé-Ollé, 2003, for Spain, and Charlot and Paty, 2007, for France). A few contributions estimate reaction functions for taxes using OECD country datasets (see  Besley et al. , 2001; Devereux et al. , 2002; Altshuler and Goodspeed, 2002). A series of factors might explain interactions among governments: these include tax competition (see  Wilson, 1999  for a survey); spillover benefits (see e. g. Wilson, 1996) ; welfare competition  (Brueckner, 2000)  and yardstick competition  (Besley and Case, 1995a,b). Public choices are confined within national boundaries, though partly depend on what neighboring states decide over time. Reaction functions have been estimated mainly for taxes, although an increasing number of contributions deal with reaction functions for public expenditures, in some cases within a spatial econometric framework  (Brueckner, 2003).
Data and descriptive evidence Data on public R&D are from national R&D surveys that comply with the Frascati Manual (OECD, 2002) recommendations. R&D statistics include three main sectors of performance: Business Enterprise, Government and Higher Education. In this study, we focus on public R&D expenditure, which refers to government departments, institutes and other public bodies, as well as profit and non-profit organisations, financed by central or local government. 14 The data for the EU-15 countries are from the Eurostat database for the period 1994-2006. The unit of R&D expenditure is purchasing power standard per inhabitant, at constant 2000 prices. Due to missing values for Luxemburg over our period of study, we excluded this country from our data. 15  We use a balanced panel data for the remaining 14 EU countries for 1994-2006, which provides 168 observations.
The past, present and future scalability of the Uintah Software framework is considered with the intention of describing a successful approach to large scale parallelism and also considering how this approach may need to be extended for future architectures. Uintah allows the solution of large scale fluid-structure interaction problems through the use of fluid flow solvers coupled with particle-based solids methods. In addition Uintah uses a combustion solver to tackle a broad and challenging class of turbulent combustion problems. A unique feature of Uintah is that it uses an asynchronous task-based approach with automatic load balancing to solve complex problems using techniques such as adaptive mesh refinement. At present, Uintah is able to make full use of present-day massively parallel machines as the result of three phases of development over the past dozen years.
INTRODUCTION The present consensus is that the move to multi-petaflop and eventually exascale computing will require step changes in many different computational and computer science areas in order to perform the large-scale computational science simulations successfully on as yet unknown computer architectures with multi-million-way parallelism. Computing the solution to such problems will require novel approaches for managing, processing and visualizing data sets at scales beyond which are manageable today. Despite uncertainty, it is clear that the time delay and energy cost of data movements will constitute a major bottleneck in future systems. Datasets will not be easily transferrable from the final multi-petaflop architecture to post-processing environments, and when such transfers will be possible an even greater data management challenge will be created on any post-processing infrastructure.
The importance of this approach for exascale computing is expressed by  [10, 17]  Exascale programming will require prioritization of critical-path and non-critical path tasks, adaptive directed acyclic graph scheduling of critical-path tasks, and adaptive rebalancing of all tasks with the freedom of not putting the rebalancing of non-critical tasks on the path itself. This is the approach used in the Uintah framework,  [24]. However achieving performance with such an approach is not guaranteed. In this paper the challenges of using such an approach are explored in the context of the Uintah software framework. This software framework was initially written as part of the CSAFE ASC center at Utah and is a sophisticated fluid-structure interaction code that is capable of scaling on large core counts.
In particular we will provide a brief description of Uintah describel how its development proceeded in three distinct phases from the point of view of how scalable the code was at each stage. In the third and most recent phase the development of a new runtime system has made it possible to use both multi-core CPUs and accelerators such as GPUs. An important requirement for Uintah is to use implicit methods and so the same discussion with regard to scalability will be applied in this case too. The paper concludes with a discussion of how the software needs to develop with regard to addressing the points made by  [10]  above.
OVERVIEW OF UINTAH SOFTWARE The Uintah Software framework originated from the University of Utah Center for the Simulation of Accidental Fires and Explosions (C-SAFE)  [8, 27, 28] , a Department of Energy ASC center. Uintah was designed for the simulation of multi-scale multi-physics problems, such as those arising from combustion and fluid-structure interaction applications. Uintah is open source software 1 released under the MIT open sourse model. An important feature of Uintah is the use of a component-based design that strictly enforces separation between those components and allows them to be interchanged and independently developed and tested. This inter-operability has led to the development of components that have been used to solve a wide variety of problems, see  [5]. This component design has also enabled a strict and critical separation between the applications developers and the developers of the runtime system.
The fourth component is the ARCHES Large Eddy Simulation (LES) code developed by Prof. P. J. Smith and his group in Utah. This component is often used for many industrial and industrial-strength research simulations,  [5]  and uses a low-Mach number (Ma< 0. 3), variable density formulation to simulate heat, mass, and momentum transport in reacting flows. The Large Eddy Simulation algorithm used in ARCHES solves the filtered, density-weighted, time-dependent coupled conservation equations for mass, momentum, energy, and particle moment equations in a Cartesian coordinate system  [18]. ARCHES is a stencil-based p. d. e. code and so achieves scalability  [29]  through its use of the Uintah infrastructure. The low-Mach, pressure approach of ARCHES requires a solution of a pressure projection set of equations at every time step.
UINTAH DEVELOPMENT PHASES As mentioned above, an important feature of Uintah, is its use of a task-based paradigm, with complete isolation of the user from parallelism. The individual tasks that make up a component are viewed as part of a directed acyclic graph (DAG). While this approach provides flexibility there are also considerable challenges in ensuring that this approach works well. This challenge is reflected by the fact that the Uintah software has used three distinct and very different runtime systems to implement execution of the task graph.
Phase 3 (2010-2013) In attempting to make AMR in Uintah scale above 100K cores it was clear that there was a need to address global memory usage. Instead of each core on a node being an MPI process, one MPI process per node was used  [24]  with a multi-threaded scheduler being used to send tasks to cores. The underlying difference is shown in Figure  1  in which all mesh patches internal to a node no longer communicate using MPI but access data directly from the single copy of the data warehouse that Uintah uses to store variables on a node. This approach was very successful on Kraken 2 and the Jaguar XT5. 3 In moving to the new form of Jaguar, the XK6, 4 the greater core count per node and the faster communications due to the new Gemini interconnect gave rise to reduced scalability. Figure  2  shows the strong scalability of a number of Uintah components, such as load balancing, scheduling, regridding, data migration etc for the AMR code with the ICE fluid-flow solver and the 2 Kraken is an NSF supercomputer located at the University of Tennessee/ Oak Ridge National Laboratory with 112,896 cores.
D. E's, along with two point-wise solves, and one iterative solve. Figure  3  shows the strong and weak scaling of the AMR code with the ICE fluid-flow solver on the same standard test problem. While these results and those in  [23]  are very promising, it is also clear that it is important to be able to run codes such as Uintah on the accelerators of emerging machines such as Titan and Blue Waters. In Uintah, accelerator task execution  [16]  on a node with both CPUs and GPUs is implemented through an extension of the runtime system that enables tasks to be executed efficiently (through pre-loading of data) on one or more accelerators per core, as has been demonstrated on the DOE TitanDev development system at ORNL and on the NSF Keeneland GPU system at NICS ORNL. In the case of the implementation of a radiation ray-tracing algorithm the GPUs on Keeneland are about 35x faster (Keeneland) or 70x faster (TitanDev) than a CPU core.
SCALABILITY WITH THE HYPRE LIN-EAR SOLVER IN UINTAH As it is far from clear that the linear solvers available today will perform efficiently on such large core counts, as those on the Titan and Blue Waters machines, in this section the scalability of the Uintah software applied to incompressible flow problems when using the hypre software is addressed  [3, 12]. This issue is described in detail in the unpublished report  [30] , while the emphasis here is on detailed scalability results and improved performance above that in  [30]. The hypre software library  [2]  is designed for the iterative solution of linear systems of equations on large parallel machines. Hypre allows the use of multi-grid preconditioners, including a structured grid interface that is designed for stencil-based p. d. e.
This work was supported by the National Science Foundation under subcontracts No. OCI0721659, the NSF OCI PetaApps program, through award OCI 0905068 and by DOE INCITE awards CMB015 and CMB021 for time on Jaguar and DOE NETL for funding under NET DE-EE0004449, Uintah was written by the University of Utah's Center for the Simulation of Accidental Fires and Explosions (C-SAFE) and funded by the Department of Energy, subcontract No. B524196. We would like to thank the hypre team for offering assistance and insight into maximizing the full capabilities of hypre. We would like to thank all those previously involved with Uintah and TACC, NICS and Oak Ridge for access to large numbers of cores.
This work was supported by the National Science Foundation under subcontracts No. OCI0721659, the NSF OCI PetaApps program, through award OCI 0905068 and by DOE INCITE awards CMB015 and CMB021 for time on Jaguar and DOE NETL for funding under NET DE-EE0004449, Uintah was written by the University of Utah's Center for the Simulation of Accidental Fires and Explosions (C-SAFE) and funded by the Department of Energy, subcontract No. B524196. We would like to thank the hypre team for offering assistance and insight into maximizing the full capabilities of hypre. We would like to thank all those previously involved with Uintah and TACC, NICS and Oak Ridge for access to large numbers of cores. Solver Time Components for Helium Plume 1. 0. 4. 5. 3. 2. 1 Time (sec). 05. 01 pfmg. 005 pfmg m=. 13 semi-restrict semi-restrict m=.
The vision of The Cochrane Collaboration is that healthcare decision-making be informed by high-quality, timely research evidence. Now, 20 years on from when it was established, the organization is making a substantive contribution globally to realizing this vision. Systematic reviews prepared and maintained by members of The Cochrane Collaboration are published in the Cochrane Database of Systematic Reviews (CDSR). As one of the seven databases in The Cochrane Library (www. thecochranelibrary. com), it includes all completed Cochrane Reviews and Protocols outlining the methods of reviews in progress. Both Protocols and full versions of the Reviews follow rigorous methodology and are peer-reviewed. They differ from other (narrative) types of literature reviews that are common in the medical literature (Table  S1 ) but share features of good quality with many systematic reviews published elsewhere.
Launched in 1995, the CDSR evolved from previously existing databases  [1]. The first issue included 36 Cochrane Reviews and 16 Protocols. The CDSR has grown steadily to more than 5000 full Reviews and over 2000 Protocols across all areas of healthcare, most focusing on the effects of interventions. In parallel, global access to the CDSR has more than doubled since 2006 (Figure  1 ). Over the past two decades, The Cochrane Collaboration has broadened the international reach of its membership: from some 77 people from nine countries, who met for the first Cochrane Colloquium in 1993, to more than 28,000 contributors in over 100 countries in 2013. This international growth did not happen by chance: The Cochrane Collaboration's strategy from the start has been to train people for evidence synthesis and to build research capacity, principally through Cochrane Centres and Branches located in Europe, the Americas, Asia, Africa, and Australasia.