dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population and clinical outcomes XAMOS was a Phase IV, international, noninterventional, open-label cohort study. Eligible patients were aged $18 years, with planned hip or knee replacement surgery (or fracture-related orthopedic surgery in countries where rivaroxaban is indicated) and in whom a decision on pharmacological thromboprophylaxis had already been made. Exclusion criteria were based on the contraindications in the approved local product information (Summary of Product Characteristics), and written informed consent was obtained from patients in countries where necessary. 7 This study is registered with ClinicalTrials. gov (NCT00831714). The study protocol was approved by the European Medicines Agency (EMA) and the appropriate independent Ethics Committee or an Independent Review Board where required.
The study was conducted in accordance with Good Epidemiological Practice guidance and was supervised by a Steering Committee. The type, dose, and duration of pharmacological thromboprophylaxis were decided by the treating physician prior to patient enrollment. Out of the 17,413 patients included in the safety population, 8,778 patients received rivaroxaban (10 mg once daily) and 8,635 received SOC (including, but not limited to, low-molecular-weight heparins [81. 7%], fondaparinux, and dabigatran etexilate). 7 The types of anesthesia used, the timing of the first dose of thromboprophylaxis, and the use of mechanical prophylaxis (such as elastic stockings and intermittent pneumatic compression [IPC]) were also recorded. Data were collected on adverse events, including symptomatic thromboembolic events and bleeding events, and coded according to the standardized Medical Dictionary for Regulatory Activities (version 14.
0). 7 Symptomatic thromboembolic events occurring during the entire study period were identified and adjudicated in a treatment-blinded fashion. Data collected on bleeding events were differentiated as major and nonmajor bleeding events. The primary safety outcome was major bleeding as defined in the RECORD trials,  5  that is, clinically overt bleeding that was fatal, occurred in a critical organ, necessitated reoperation, or was outside of the surgical site and was associated with a fall in hemoglobin of $2 g/dL or required a transfusion of $2 units of blood. In addition, major bleeding events were defined according to the European Medicines Agency (EMA) guidelines, which are similar to the RECORD major bleeding definition but with the inclusion of bleeding warranting treatment cessation and surgical-site bleeding events associated with a fall in hemoglobin of $2 g/dL or leading to a transfusion of $2 units of blood or packed cells.
Data analyses Cumulative crude incidences were calculated for the rivaroxaban and SOC groups in the safety population, which included patients who received at least one dose of rivaroxaban or SOC. Thromboembolic events were those that occurred within 3 months of surgery, whereas bleeding events presented were treatment-emergent events. In this analysis, data were analyzed according to the timing of the first postoperative dose of rivaroxaban and SOC thromboprophylaxis (ie, #6 hours,. 6 hours to #10 hours, and. 10 hours to #24 hours after surgery), type of anesthesia used (ie, general, neuraxial, peripheral [including peripheral blocks or local infiltration], combinations, and unknown/others), and whether mechanical prophylaxis (eg, compression stockings and IPC) was used concomitantly with pharmacological thromboprophylaxis.
Discussion Published data on the use of DOACs in everyday clinical practice are limited, particularly with regard to the potential influence of timing of the first dose of thromboprophylaxis in the perioperative period, as well as their use under different types of anesthesia and with or without mechanical prophylaxis. Data on these parameters were recorded in the noninterventional XAMOS study, and the outcomes of these subanalyses of XAMOS data are consistent with the overall findings of the study, which confirmed the effectiveness and safety of rivaroxaban in unselected patients in routine clinical practice. 7 revious studies have suggested that the timing of the first dose of thromboprophylaxis may have an impact on clinical outcomes, such as bleeding and thromboembolic events. 9, 12 In a meta-analysis of four randomized trials in patients undergoing major orthopedic surgery, the timing of the first dose of fondaparinux was associated with the incidence of major bleeding events, and patients who initiated fondaparinux within 6 hours after surgery had a higher incidence of overt bleeding.
9 The label for rivaroxaban recommends starting rivaroxaban 6-10 hours after surgery when hemostasis has been established. 13 These subanalyses of the data from XAMOS showed that, in routine clinical practice, only 56. 8% of patients received their first dose of rivaroxaban within the recommended time frame of 6-10 hours after surgery. 13 oreover, patients who initiated rivaroxaban #6 hours after surgery did not have a higher incidence of major bleeding  events (RECORD definition: 0. 3%) compared with those who had the first dose. 6 hours to #10 hours after surgery (0. 5%). Similarly, in the SOC group, the incidences of major bleeding were similar between patients who received their first dose #6 hours after surgery (0. 4%) and. 6 hours to #10 hours after surgery (0. 4%). However, owing to the small patient numbers in this category and the nature of the noninterventional study, a definitive conclusion that earlier initiation of pharmacological thromboprophylaxis (ie, within 6 hours after surgery before hemostasis is established) has no  impact on the risk of bleeding cannot be made based on these data.
1 In routine clinical practice, mechanical methods, such as stockings, are commonly prescribed in addition to pharmacological thromboprophylaxis. Unlike pharmacological thromboprophylaxis, clinical evidence on the benefit of using mechanical prophylaxis for the prevention of VTE is less clear and is questionable. In the XAMOS study, more than half of all patients used mechanical thromboprophylaxis in routine clinical practice, but use varied markedly between regions. Data from this subanalysis showed that the use of mechanical methods (in conjunction with rivaroxaban or SOC thromboprophylaxis) had no apparent influence on the incidence of symptomatic thromboembolic events compared with using rivaroxaban or SOC thromboprophylaxis alone. No additional reduction in the incidence of symptomatic thromboembolic events was observed with the use of mechani cal methods.
However, there might be specific reasons for the chosen mechanical method, but no further information is available in XAMOS. Nevertheless, the effectiveness of rivaroxaban compared with SOC was retained irrespective of the use of mechanical thromboprophylaxis. Data from this subanalysis are consistent with a recent meta-analysis of data from four studies in 1,171 patients undergoing trauma and elective orthopedic surgery, in which no significant benefit of anti-embolic stockings was shown in reducing the risk of VTE when used together with pharmacological thrombo prophylaxis compared with pharmacological thrombo prophylaxis alone. 17 Therefore, data from this subanalysis raise further questions regarding the added value of mechanical prophylaxis used in combination with pharmacological prophylaxis.
, 2013; Bamber et al. , 2013; Morlighem et al. , 2014). These data products provide essential boundary conditions for numerical models of ice sheets (e. g. Gillet-Chaulet et al. , 2012; Cornford et al. , 2015)  and enable investigation of a diversity of topics related to ice sheet dynamics. By contrast, despite many notable regional studies (e. g. Oswald and Gogineni, 2008; Jacobel et al. , 2009; Fujita et al. , 2012; Schroeder et al. , 2016) , ice-sheet-wide data products for bulk basal material properties, such as quantifying regions of basal melt do not exist. As contemporary models of ice sheet dynamics have been demonstrated to be highly Published by Copernicus Publications on behalf of the European Geosciences Union. T. M. Jordan et al. :  An ice-sheet-wide framework for radar attenuation sensitive to basal traction  (Price et al.
Moreover, these radar algorithms are not tuned for automated application over the scale of an ice sheet. In this study we introduce a new ice-sheet-wide framework for the radar inference of attenuation and apply it to IPR data from the Greenland Ice Sheet (GrIS). A central feature of our approach is to firstly estimate the spatial variation in the attenuation rate using an Arrhenius model, which enables us to modify the empirical bed-returned power method. Specifically, the estimate is used to (i) constrain a moving window for the algorithm sample region, enabling a formally regional method to be applied on a ice-sheet-wide scale and (ii) to standardise the power for local variation in attenuation within each sample region when deriving attenuation using bed-returned power. We demonstrate regions of algorithm solution convergence for two different input temperature fields and for independently analysed IPR data.
The coverage provided by the algorithm is a trade-off with solution accuracy, and we suggest that the algorithm can be "tuned" for basal melt discrimination in restricted regions, primarily in the southern and eastern GrIS. This is supported by the decibel range for the basal reflection coefficients (∼ 20 dB for converged regions). Additionally, we show that the attenuation rate solution can be used to infer bias in the depth-averaged temperature field of thermomechanical ice sheet models. 2 Data and methods.
Ice-penetrating radar data The airborne IPR data used in this study were collected by the Center for Remote Sensing of Ice Sheets (CReSIS) within the Operation IceBridge project. Four field seasons from 2011 to 2014 (months March-May) have been analysed in this proof of concept study. These field seasons are the most spatially comprehensive to date, with coverage throughout all the major drainage basins of the GrIS and relatively dense across-track spacing toward the ice margins (Fig. 1 ). The radar instrument, the Multichannel Coherent Radar Depth Sounder (MCoRDS), has been installed on a variety of platforms and has a programmable frequency range. However, for the data used in this study, it is always operated on the NASA P-3B Orion aircraft and uses a frequency range from 180 to 210 MHz, which, after accounting for pulse shaping and windowing, corresponds to a depth-range resolution in ice of  4.
3 m (Rodriguez-Morales et al. , 2014; Paden, 2015). The data processing steps to produce the multilooked synthetic aperture radar (SAR) images used in this work, are described in  Gogineni et al. (2014). The along-track resolution after SAR processing and multilooking depends on the season and is either ∼ 30 or ∼ 60 m with a sample spacing of ∼ 15 or ∼ 30 m respectively. The radar's dynamic range is controlled using a waveform playlist which allows low-and high-gain channels to be multiplexed in time. The digitally recorded gain for each channel allows radiometric calibration and, in principle, enables power measurements from different flight tracks and field seasons to be combined. This is in contrast to pre-2003 CReSIS Greenland data sets, which used a manual gain control that was not recorded in the data stream.
From this test we conclude that the M07 model provides a suitable estimate for our algorithm, and unless stated we use it in all further attenuation estimates. The temperature fields for GISM and SICOPOLIS were used to estimate the spatial variation in the depth-averaged attenuation rate for the GrIS and were interpolated at 1 km grid resolution. Both the GISM and SICOPOLIS models provide temperature profiles as a function of relative depth, and these were vertically scaled using the 1 km Greenland Bedmap 2013 ice thickness data product  (Bamber et al. , 2013). For the SICOPOLIS temperature field it is necessary to convert the (homologous) temperature values from degrees below pressure melting point to units of K (or • C) using a depth correction factor of -0. 87 K km -1  (Price et al. , 2015).
Constraining the algorithm sample region Radar inference of the depth-averaged attenuation rate, using the relationship between bed-returned power and ice thick- ness, requires sampling IPR data from a local region of the ice sheet  (Gades et al. , 2000; MacGregor et al. , 2007; Jacobel et al. , 2009; Fujita et al. , 2012; Matsuoka et al. , 2012b ). An implicit assumption of the method is that the depth-averaged attenuation rate is constant across the sample region (Layberry  and Bamber, 2001; Matsuoka et al. , 2010a). However, as was shown in Sect. 2. 4, the depth-averaged attenuation rate is predicted to have pronounced spatial variation, and therefore an ice-sheet-wide radar attenuation algorithm must take this into account. In our development of an automated framework we use the spatial distribution of < B > (the prior Arrhenius model estimate) to constrain the size and shape of the sample region as a function of position (a "moving target window") by estimating regions where the attenuation rate is constant subject to a specified tolerance.
Gridded maps The attenuation rate solution from the radar algorithm, < B >, is at a 1 km grid resolution and arises as a consequence of the scan resolution of the moving target window described in Sect. 2. 5. It is defined on the same polar-stereographic coordinate system as in Fig. 1  and the gridded thickness data from  Bamber et al. (2013). Subsequently, a gridded data set for the two-way loss can be calculated using [L] = 2 < B > h. For grid cells that contain IPR data, the mean [P C ] value is calculated and, using Eq. (  6 ), an along-track map for the gridded relative reflection coefficient, [R], is obtained. Due to the definition of relative power in Eqs. (  3 ) and (  4 ), the values of [R] are also relative. As described in Sect. 2. 3 the averaging procedure for the basal waveforms means that the effective resolution of the processed IPR data varies over the extent of the ice sheet.
Consequently, the number of data points that are arithmetically averaged in each grid cell varies according to both this resolution variation and the orientation of the flight tracks relative to the coordinate system. For a single flight line (i. e. no intersecting flight tracks), the number of points in a grid cell typically ranges from ∼ 4 in thick ice to ∼ 16 in thin ice. Initially, maps for the four field seasons were independently processed, which enables crossover analysis for the uncertainty estimates. Joint maps were then produced by averaging values where there were grid cells with coverage overlap.
Attenuation solution convergence To demonstrate the convergence of the attenuation solution for different input temperature fields (convergence is defined here as a normally distributed difference distribution about zero), we compare the solution differences for the (input) Arrhenius models, < B(T GISM ) > -< B(T SIC ) > and [ L(T GISM )] -[ L(T SIC )], with the corresponding (output) radar-inferred solution differences, < B(T GISM ) > -< B(T SIC ) > and [L(T GISM )] -[L(T SIC )]. As [L] = 2 < B > h, it is necessary to consider the thickness dependence of the solution differences and the consequences for a thicknesscorrelated bias in basal reflection values. We focus on the south-east GrIS, corresponding to target window centres that are located in drainage basin 4 Fig. 1a. This region is selected post-ice-sheet-wide processing, and the IPR data from neighbouring drainage basins are incorporated in the linear regression plots for the target windows that lie close to the basin boundaries.
We hypothesise that the algorithm convergence for different input temperature fields occurs because the local differences in the Arrhenius model attenuation rate field that are used as an algorithm input (i. e. < B(x, y) > -< B(x 0 , y 0 ) >) are more robust than the absolute values. This is broadly equivalent to saying that the horizontal gradients in the depth-averaged temperature field of the ice sheet models are more robust than the absolute values of the depth-averaged temperature. Similarly, our use of local differences for the attenuation rate estimate is also robust to systematic biases in the Arrhenius model. We have yet to consider an explicit classification of the subglacial materials and quantification of regions of basal melting. In future work, we aim to combine IPR data from preceding CReSIS field campaigns to produce a gridded data product for basal reflection values and basal melt.
It is anticipated that, as outlined by  Oswald and Gogineni (2008, 2012)  and  Schroeder et al. (2013) , the specularity properties of the basal waveform, and how this relates to basal melt detection, could also be incorporated in this analysis. As the regions of algorithm coverage are sensitive to uncertainty, we suggest that these data products could have spatially varying uncertainty incorporated. Additionally, for the basal reflection and basal melt data sets, uncertainty in the measurements of [P C ] will have to be incorporated in the uncertainty estimate for [R]. Establishing a procedure for the interpolation of these data sets where either: (i) the algorithm coverage is poor due to low attenuation solution accuracy, or (ii) the IPR data are sparse, will form part of this framework.
Regions of lower solution accuracy generally correspond to the interior of the ice sheet where spatial variation in the attenuation rate is much less pronounced (primarily the northern interior). Due to this lower spatial variability (and despite the caveats in the paragraph above), these regions could potentially have their basal reflection values derived by using a forward Arrhenius temperature model for the attenuation. Finally, we envisage that the framework introduced in this paper could be used for radar inference of radar attenuation, basal reflection and basal melt for the Antarctic Ice Sheet. Given that for high solution accuracy the radar algorithm requires high topographic roughness and relatively warm ice, we suggest that IPR data in rougher regions toward the margins should be analysed first (refer to  Siegert et al.
, 2005  for an overview of topographic roughness in East Antarctica). Additionally, the prediction of the model temperature field bias using the attenuation rate solution could be extended to the Antarctic Ice Sheet. are the molar concentrations of the chemical constituents (µM)  (MacGregor et al. , 2007 (MacGregor et al. , , 2015b)). The model parameters are summarised in tabular form by  MacGregor et al. (2015b)  for both the M07 model and W97 model. Following the assumptions in Sect. 2. 4 for the GrIS temperature field, ionic concentrations, and ice thickness data set, it is possible to obtain the spatial dependence of the attenuation rate, B(x, y, z), where (x, y) are planar coordinates and z is the vertical coordinate. The two-way attenuation loss for a vertical column of ice, [ L(x, y)] (dB), is then obtained via the depth integral [ L] = 2 h 0 B(z)dz.
Acknowledgements. This study was supported by  UK   NERC  grant  NE/M000869/1. We would like to thank our reviewers  J. Macgregor  and  M. Wolovick  for their valuable comments which greatly improved this manuscript, and  K. Matsuoka  for handling our manuscript submission. We would also like to thank  J. Macgregor  for supplying ice core temperature profiles. Edited by: K. Matsuoka Reviewed by:  J. MacGregor  and  M. Wolovick.
Data Description Our cross-country macroeconomic data are drawn mainly from the World Bank's World Development Indicators. This includes the main controls: the investment share of output, and the net rate of depreciation (which is the sum of the population growth rate and 0. 05, as discussed in Section 2). We supplement these with data from several other sources. Our primary measure of the human capital stock is the  Barro & Lee (2001)  dataset on educational attainment, which captures the average educational attainment of the population aged 15 and older. Our supplementary educational data-such as the pupil-teacher ratio and educational expenditures-were mainly from the UNESCO Institute for Statistics' Global Education Statistics database. Our primary governance 10 System GMM allows for control of (weak) endogeneity using internal instruments, which is a distinct advantage for addressing possible endogeneity in the governance variable.
24 This is reported in column (Z2 ). As before, our results are largely unchanged. In the specifications listed in Table  1 , we shied away from using achievement data (in the form of test scores). By and large, the international comparability across different test types and time periods is suspect, and where comparable data are available, they are often only for a very limited set of (mostly developed) countries. Moreover, our instrumental variables strategy already accounts for issues of mismeasurement, conditional on our instruments satisfying the necessary exclusion conditions. Nonetheless, we use a recently-compiled database of comparable achievement data  (Altinok & Murseli 2007)  to examine how our results change when we utilize a more accurate measure of human capital quality. The results are reported in column (Z3 ).
Following the literature, we assumed that trust was time-invariant, and so countries with more than one survey were collapsed into a single score by simple averaging. For alternative variables used in the robustness section: Parental authority is the father's share of parental authority, which ranges from 0 (half share) to 1 (full). This was obtained from the OECD's Gender, Institutions, and Development database. Ability was calculated average national IQ estimates, adjusted to account for time differences as a result of the  Flynn (2007)  effect. This was due to  Lynn & Vanhanen (2002). Achievement is the sum of the student performance in math and reading tests, adjusted for cross-country and cross-test comparability, from  Altinok & Murseli (2007). We used adult schooling as an alternative measure of family input; this is the mean schooling of the population aged 25 and over, and it serves as a proxy for parental education as a family input into the education process.
LMA measurements Leaf mass per area was measured from April to November 2013 in Peru and from March to May 2014 in Brazil. In both countries, based on the most recently available census and diameter data, a sampling protocol was adopted wherein species were sampled that maximally contributed to plot basal area (a proxy for plot biomass or crown area). We aimed to sample the minimum number of species that contributed to 80% of basal area  (P erez-Harguindeguy et al. 2013) , although in the diverse lowland forest plots in Peru, we only sampled species comprising 60-70% of plot basal area. In Peru, within each species, 3-5 individual trees were chosen for Table  1. Environmental and sampling data for the 10 plots in Peru  (Malhi et al. 2016 and unpublished data)  and the four plots in Brazil  (Marimon et al.
Leaf area index measurements In Brazil, leaf area index (LAI) measurements were taken as a proxy for light availability. LAI values and gap fraction per plot were calculated from 25 hemispherical pictures per plot taken in the center of each 20 9 20 m subplot with a Nikon 5100 camera and a Nikon FishEye Lens of 8 mm (Nikon U. K. Ltd, Surrey, U. K). Images were processed using the software Hemisfer (registered version,  Schleppi et al. 2007; Thimonier et al. 2010 ). LAI measurements were not available for Peruvian sites at the time of these analyses.