dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the Editor and the anonymous referees for very useful remarks that substantially improved the paper, as well as the audiences at  Barcelona GSE Summer Forum on Structural Microeconometrics, PUC Chile, SOLE , the  UCL-Cemmap Workshop on Dynamic Structural Models, University of Chile , and the  Cowles Summer Conference on Structural Microeconomics. Sara Compton Meade  provided assistance proof-reading the text. Financial support from the  Agence Francaise de Developpement (AFD)  and the  Inter-American Development Bank (IDB)  is gratefully acknowledged.
Study population & data collection Since its initial development, the PFET has been progressively deployed in a number of low-to middle-income countries (N = 20 as of 2019) for a wide variety of drugs prescribed in the field of chronic diseases or cancer (N = 57 as of 2019). A dedicated database platform has been developed in parallel with the use of the PFET to enable routine collection of patient economic data as well as to enable internal testing to adjust the tool and optimize its ability to quantify patients' ability to pay. For the present analysis, data were extracted from three countries (Thailand, UAE and Mexico), which were chosen based on the availability of data within the database and because the size of the informal economy varies across them  [7]. Consequently, these three countries provide an excellent sample population in which to evaluate interactions among the component metrics of the PFET in distinct economic environments.
Anonymized data were extracted for patients enrolled in 12 different drug treatment programs between May 2013 and February 2017. Information used for the present analysis included the following variables: normalized (0-100) PFET summary components for income, assets and SoL based on the linear weighted combination of all individual items relating to these topics, and detailed quantitative items expressed in purchasing power parity dollars to allow cross-country comparisons, including patients' household and economic unit income, monthly household expenditures, financial assets and, when applicable, vehicle price, value/rental of primary dwelling, business turnover and education fees. Qualitative information collected within the PFET regarding household equipment, method of cooking, water source or dwelling floor material was not included because of the variability of the subcategories used across countries.
For illustrative purpose, a Gabriel's biplot  [10]  was created to project the patients from the three countries along the principal components (PCs) axes from a PC analysis based on the three PFET metrics. Cluster solutions were then mapped on the biplot by coloring patients according to their cluster membership. Descriptive quantitative results are presented as means (± standard deviation). Comparisons between groups identified from clustering analysis were conducted using one-way analysis of variance (ANOVA) or Kruskall-Wallis tests for continuous data, as appropriate. A p-value < 0. 05 was considered significant. Analyses were performed using Stata 15. 1 (StataCorp, TX, USA) for descriptive analyses and between-groups comparisons, and R 3. 4. 2 statistical software (Vienna, Austria; Numeroand pca3d packages) for clustering analyses and visualizations.
Results Overall, anonymized economic data from 1404 patients were extracted for the present analysis, including 947 from Thailand, 347 from UAE and 110 from Mexico. Mean normalized (0-100) PFET summary component scores for income, assets and SoL were 10 ± 19, 16 ± 14, 19 ± 11 for Thailand, 13 ± 20, 3 ± 7, 22 ± 13 for UAE and 31 ± 21, 10 ± 12, 25 ± 13 for Mexico, respectively. Figure  1  shows the results from the clustering analysis using the SOM methodology in Thailand (A), UAE (B) and Mexico (C). For each series of SOMs by country, patients identified as globally similar in terms of PFET income, assets and SoL component scores are grouped in 'districts' represented as numbered polygons placed at fixed positions on the maps. The more those specific patient subgroups resemble each other in terms of PFET values, the closer they are on the map and conversely, subgroups with fewer commonalities are further apart.
Finally, our study has limitations worth discussing, including the lack of comparison to other asset-based wealth indices such as the International Wealth Index  [11]  or the Comparative Wealth Index [12], as data collection was not initially designed to capture the same items as those required for calculation of these indices and we consequently lacked targeted or sufficiently detailed information in our database to draw comparisons. Second, we did not evaluate the correlation between PFET results and macroeconomic development measurements at the national level (e. g. , Gross National Income per capita or poverty indicators from World Bank such as Poverty Headcount Ratios  [11] ) to provide additional validation of the predictive ability of PFET. Such analysis would have been limited by the number of countries analyzed in the present work (n = 3) and could have yielded potentially misleading results given the selection of our sample, which was not constituted based on a representative sampling methodology but based on the real-life enrollment of patients in drug access programs.
Study design We conducted a prospective and multicenter cohort study in France between 2014 and 2015. The main inclusion criteria were having a lymphedema, being aged 7 years or more, living in metropolitan France, and being able to use Internet and email. Lymphedema patients were included in a convenience sample of 27 centres, among which 11 were public or private hospitals and 16 were individual physicians or physiotherapists working in a private practice. Each centre included all the consecutive patients who met the inclusion criteria during the study period. Because we anticipated a high drop-out rate due to the data collection burden, we ran a 1-month pre-inclusion phase. Only patients who agreed to participate at the end of the preinclusion phase were included in the study. All patients (or their legal representative) signed a written informed consent prior to inclusion.
Data collection Data collection was standardized using a paper questionnaire filled in by health care professionals and an electronic questionnaire filled in by patients on a dedicated and secured Internet website. The professionals provided demographic and clinical data, and patients reported socio-economic and clinical data, health care services utilization, cost and reimbursement, and health-related quality of life measures. Health care service utilization was collected weekly. To ensure high-quality data, we accompanied patients and professionals with initial training, realtime data quality monitoring and active email and telephone communication. We collected the following socio-economic data: employment status and category  [19] , monthly net post-tax household income (using an 11-category scale), VHI and special schemes entitlements (CMUc, ACS and ALD).
Indeed, we were able to prospectively collect socio-economic and clinical data as well as unmet medical needs and reimbursements by VHI that are not available in standard retrospective analyses. The national, multicenter and primarily outpatient-based pre-screening and screening certainly made the sample representative of the whole lymphedema population. As an example, the Gini coefficient for income in our sample (0. 286) is very close to that of the French general population measured in 2013 (0. 294;  [34]. Finally, the data quality check process allowed us to control the impact of missing and inaccurate data.
Restrictions apply to the availability of the full data set that support the findings of this study because they contain potentially identifying variables (i. e. , gender, age, ZIP code, income, profession, and clinical data). However, we have made a de-identified version of this dataset available as a Supporting Information file. This de-identified version of our data set is sufficient to support all the findings of this study. The original full data are nevertheless available on request for researchers who meet the criteria for access to confidential data after approval by the Commission Nationale Informatique et Liberte (https://www. cnil. fr/en/home).
The optimal spatial configuration for each strategy is found with integer linear programming using a methodology adapted from the optimisation software Marxan with Zones  [35, 36] , using the package prioritzr  [37]  developed in R programming language  [38]. Codes and data are available at https://doi. org/10. 6084/m9. figshare. 8153777. It should be noted that, contrary to many conservation planning studies, we did not include the connectivity of protected areas in the optimisation process. In our case, the total area of one grid cell is around 11 000 km 2. At this scale, the additional benefit of connected grid cells is difficult to quantify and interpret, although connectivity also has implications at large landscape scales.
Acknowledgments We thank all TmFOmembers who contributed data and participated in the discussions related to this paper, and especially the  Instituto Boliviano de Investigación Forestal , as well as  David P Edwards  and two anonymous reviewers for suggestions that improved the manuscript. This study was partially funded by the  GFclim  project (FEDER 20142020, Project  GY0006894 ), two  Investissement d'Avenir grants of the ANR: CEBA  ( ANR-10-LABEX-0025 ), the  REsilience of Managed Amazonian FORests  project funded by  LabEx Agropolis  ( ANR-10-LABX-0001 ), and  Embrapa , by  Brazilian National Council of Science and Technology-CNPq  ( Processes 309319/ 2018-8 ) and the  Foundation for Research Support of the State of Sao Paulo-FAPESP  ( Processes 2013/ 16262-4 ,  2013/50718-5 ). The study was carried out in the framework of the Tropical managed  Forests Observatory (TmFO) , supported by the  Sentinel Landscape program  of  CGIAR (Consultative Group on International Agricultural Research) Forest Tree and Agroforestry Research Program.
3 he International ChoLesterol management Practice Study (ICLPS) was a multinational, cross-sectional, observational study to investigate the achievement of European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) guideline LDL-C targets  10  and their determinants in real-world clinical practice in countries outside Western Europe. 11 In this article, we present data from a subgroup of ICLPS participants with definite or probable FH according to DLCN criteria. Although it is likely that not all the patients included had FH, this group represents a population of patients with severe hypercholesterolemia who are at high risk of ASCVD.
Acknowledgments The authors thank the patients, their families, trial staff, and all investigators involved in this study. They also thank  Gemma Grundy, BSc , from  Sanofi  for coordinating the development of the ICLPS subanalyses and this article. Medical writing assistance and editorial support, under the direction of the authors, were provided by  Fiona Van, PhD , and  Elke Sims , MLangTrans, both of  Prime (Knutsford, UK ), funded by  Sanofi  according to Good Publication Practice guidelines (https://annals. org/aim/fullarticle/2424869/goodpublication-practice-communicating-company-sponsoredmedical-research-gpp3). Sanofi was involved in the study design, collection, analysis, and interpretation of data, as well as data checking of information provided in the article. However, ultimate responsibility for opinions, conclusions, and data interpretation lies with the authors.
They conclude that c = 0 is often the best choice. This is not so surprising, since the logarithmic term is here to prevent from large deviation events, which do asymptotically not exist for deterministic games. MoGo uses c = 0 and ad hoc constants a and b. This version of MCTS is the one presented in Section 9 and the one we used to test our algorithm. Let us mention the more recent theoretical work by Buşoniu,  Munos and Páll [6] , as well as the paper of Garivier, Kaufmann and Koolen  [13]  who study in details a bandit model for a two-round two-player random game. The survey paper of Browne et al. [7]  discusses many tricks to improve the numerical results and, of course, all this has been adapted with very special and accurate procedures to particular games. As everybody knows, AlphaGo  [22]  became the first Go program to beat a human professional Go player on a full-sized board.
Empirical power-laws in foreign debt-to-public revenue ratio Let us look at the data in Figures  1 to 3. We plot the ratio of external debt over public revenues for some major emerging countries. The data are quarterly from 1980 (1990 for some countries) to 2014 and are taken from Oxford Economics and national sources (central banks and statistical institutes). Foreign debt data do not only comprise the general government's external debt but also include external debt issued by the private sector. Public revenues data correspond to central and local government revenues in local currency, including different types of taxes, interest receipts and dividends from state owned enterprises. To compare them with the ratio of external debt on GDP, these public revenues are then divided by nominal GDP (at market prices, in local currency).
Using a scale of possible values for x min , the "optimal" x min is finally computed so as to make the distributions of the empirical data and of the best fitting power-law as close as possible: xmin = argmin x min D * (x min ). (5) It minimizes D * on a range of possible values for x min. The mean and standard errors of the estimated parameters xmin and α(x min ) are then obtained by a bootstrap procedure. This consists in drawing N draws (N=1000) uniformly at random from the original dataset, estimating α and x min again for each of these series.
Data Collection The REIN registry includes the following patient data: age, sex, residence postcode, clinical characteristics at RTT initiation (primary renal disease, serum albumin, and hemoglobin), comorbidities (diabetes, cardiovascular diseases, cancer, chronic respiratory disease, mobility) and modalities of ESRD management (vascular access, hemodialysis or peritoneal dialysis). However, it does not contain information on the pre-dialysis care trajectory. Therefore, the REIN registry was linked to the French national healthcare database (SNDS). This administrative database covers almost 99% of the population and collects data on ambulatory care (consultations, laboratory tests, drug prescriptions) and hospital stays (duration, diagnostic codes, procedures)  [26]. The linkage of these two database types (clinical and administrative data with healthcare utilization) will give a valuable tool for public health research, especially for the study of chronic and multifactorial diseases, such as CKD.
Indeed, it will increase the quantity of information available at the individual level, enabling a more comprehensive understanding of the risk factors and outcomes  [27] [28] [29] [30] [31]. An iterative and deterministic approach will be used to link the 2015 incident patients included in the REIN registry with the SNDS database, using the following indirect identifiers: patient's sex, age, residence postcode, RRT center identification number, month and year of dialysis start, month and year of death. In the framework of our quantitative component, this linkage will allow us to retrieve and piece back together the pre-dialysis care trajectory of each patient, along with their demographic and clinical characteristics. The French national guidelines recommend starting the patient's preparation for RRT (arteriovenous fistula, catheter for peritoneal dialysis procedures and other hospitalizations related to preparatory care for dialysis) at least 1 year before its foreseeable occurrence (Table  1 ).
Study Population A previous spatial analysis in the Bretagne region of France found that the western and eastern parts of this region have higher and lower risk of ES, respectively  [34]. These geographical areas will be our investigation field. All adult patients with ESRD who will start dialysis in emergency and live in and around these areas in 2019 will be recruited. Patients will be identified using the REIN registry inclusion data. On the basis of the available REIN data, we expect to include about 40 patients (20 for each area). However, recruitment will be stopped upon reaching data saturation (i. e. , when no new information will arise from the interviews)  [35]. Beforehand, the study will be presented to the medical teams of the dialysis centers where the included patients are treated.
The medical teams/staff will then seek the patients' consent, using an information notice, and will schedule an appointment. Patients will be interviewed in-person during a dialysis session. The interviewer will ask again for their consent before starting the interview. During the interview, patients will be asked to give the name of their GP(s) and nephrologist(s) who will be interviewed in the second part of this study. To favor consistency, the same interviewer will conduct all interviews. This qualitative study is compliant with the methodological frameworks defined by the French Data Protection Authority and has been registered by the French National Institute of Health Data (n 0609030619).
). For the health professionals, the interview will explore (1) the everyday practice regarding CKD (As a GP, what do you think your role is towards patients with CKD? What does CKD co-management mean to you?. ); (2) their knowledge and implementation of the CKD care management guidelines (What do you think about these guidelines. ); and (3) the process of transition from CKD to ESRD (How is discussed the need to start dialysis with a patient? Have you ever had patients who started dialysis in emergency, and if so what happened?. ). The interview guides were discussed with GPs and nephrologists and tested with patients and health professionals in real conditions. Appropriate adjustments were made (mainly rephrasing) and additional tests were performed to ensure that the data collected on the trajectory were precise enough for analysis.
Analysis First, a thematic analysis will be carried out to analyze the interview transcripts  [36]. In this method, the researcher first becomes immersed in the data by reading the full transcripts and writing down notes to grasp the overall sense of the interviews and the information they bring. Transcripts are then read again, word by words, and data are associated with as many categories as necessary to describe all aspects of the content. This process of category generation is called "open coding". Similar categories are progressively merged to produce a final list of categories. Coding and category generation will be performed by two different researchers to increase coherence and validity. The results of the thematic analysis will provide a preliminary understanding of the pre-dialysis care trajectories of ES patients.
Integrating the two sets of results can be considered the main methodological challenge. It should be noted that data produced by registries and healthcare databases require a delay of consolidation (i. e. , data on incident patients in 2015 were available only from 2018). Interviewing patients who started dialysis a few years before could introduce a major recall bias because some patients might not be able to recall precisely what happened at that time. In addition, and unfortunately, many dialyzed patients will not be alive any longer by the time their clinical/administrative data become available. For these reasons, the study population cannot be the same for the two components (i. e. , patients who started dialysis in 2015 for the quantitative study, and incident patients in 2019 for the qualitative study).
However, we do not think that the causes of emergency dialysis start or the patients' characteristics will change during this short interval of time, and affect the integration process of the quantitative and qualitative results  [15]. Nevertheless, it is possible that the integration process might reveal contradictions between result sets that could become the interest of additional investigations and provide new insights into the topic and generate new research questions. Although the French national guidelines recommend initiating RRT preparation at least one year before its foreseeable start, we will assess two years of healthcare utilization data for our quantitative component, for a broader description of the trajectory. However, this study period should be considered along with the fact that the start of RRT of some CKD patients is rather unpredictable and their RRT preparation might have started several years before the actual dialysis start.
Questionnaire design and target population Data was collected through an online survey, using the LimeSurvey platform. A questionnaire was administrated to a convenience sample of Generation Y consumers from Italy and France. Generation Y was selected as the target population since they are likely to represent the most relevant market segments for the wine industry in the near future  (Agnoli et al. , 2011; Atkin & Thach, 2012) , due to their increasing wine consumption both in volume and frequency  (Mueller & Charters, 2011). In addition, several researchers highlight the growing tendency of younger generations to change their drinking-style towards more unhealthy habits  (Agnoli et al. , 2018; Istat, 2015). Finally, according to other studies these individuals are extremely proficient with computer-based surveys  (Vecchio, 2013; Szolnoki & Hoffmann, 2013).
Results from the DCE The choice experiment data were analysed using Latent-GOLD 5. 1. Results reported in Table  3  show the estimated preferences of the full sample. Although the most important element driving choice is the warning logo (61. 4%), utility is higher with the no logo option (p <. 001), showing that consumers prefer to have any warning logo on a bottle of wine. Their utility is negatively influenced by the presence of the logo depicting the negative effects of alcohol on the brain. The second attribute in order of importance is the position of the warning logo (19. 3%) and consumers prefer to see the logo on the back-label, where it is less visible. With respect to the warning message (11. 1% of total utility) coherently with the logo, they prefer to have no message at all on the label, and if a message is present and negatively framed it depresses their choice utility.
Study design and participants The details of the study design and patient population of UNCOVER-3 (NCT01646177), a randomised, double-blind, multicenter, phase 3 study in patients with moderate-to-severe plaque psoriasis, were reported previously. 2, 3 In the current study, patients randomly received either placebo, 80-mg IXE every 2 weeks (Q2W), 80-mg IXE every 4 weeks (Q4W; both IXE groups had a starting dose of 160 mg) or etanercept 50 mg twice weekly. At week 12, all patients were switched to IXE Q4W dose for the long-term extension (LTE) period. After week 60 and at the investigator's discretion, patients could receive a dose adjustment to IXE Q2W. This report primarily focuses on the data from patients who received the clinically recommended dosing regimen (initial 160 mg, IXE Q2W up to week 12 and IXE Q4W thereafter).