dataesr/scientific-paragraphs-categorization huggingface dataset:
Sources of information An ad hoc network of incountry experts  11  gathered the markers (see full list for paediatrics on p56, LTC on p76, volunteers on p80, primary care on p84 and cardiology on p88 of the 2019 Atlas of Palliative Care in Europe  21  ). Experts were selected based on the following criteria: (1) members of the EAPC Task Forces; (2) authors of technical and scientific documents related to the area (searched through PubMed and Google Scholar using the following keywords:  (3) nominations from the leaders of the EAPC Task Forces. Experts completed an online survey with the indicators per area of expertise. The exception was oncology, where PC integration was measured using an online search in public databases: European Society for Medical Oncology (ESMO),  26  ClinicalTrials.
The country integration score (ICS) is obtained with the sum of the subscores of integrations in each area of the health system explored. †Activity data of each health system area explored, as obtained in the European Association for Palliative Care Atlas survey, are available in online supplemental appendix 1. ‡Source: Böhm et al. Original research 40%-60%, three to 20%-40%, and five to less than 20%. Twelve countries have publications researching PC at Long-term Care Facilities (LTF) and 12 report public funding for their work (online supplemental appendix 1).
PC provision by volunteers Fifteen countries provided data on hospice and PC volunteers. Nearly all reported volunteer training systems. Of the countries, 16% estimated a volunteer workforce of over 1000 and 20% of hospices had volunteer teams. Germany had the highest number of teams (n=1316). Furthermore, some nine countries had databases for PC volunteers. Austria is an example of good practice with 184 volunteer hospice teams, over 1000 PC volunteers, PC volunteer tracking systems and specific PC volunteer training programmes (online supplemental appendix 1).
Acknowledgements We are grateful to all the incountry experts and the  EAPC Task Force  leaders who contributed to this study. Contributors MS-C, NSA-C, EG, SAM and CC made substantial contribution to the concept or design of the work, or acquisition, analysis or interpretation of data. ERBS, DVS, SAM and SM made substantial contribution to data analysis and its presentation. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
ClinicalTrials. gov and Scopus Clinical trials on early integration of PC in oncological treatments. Number of publications on the integration of PC and oncology. Primary care level Professor Dr Scott Murray, Dr Sebastien Percentage of PC patients identified at the primary care level. Moine Existence of incentives to promote early identification of PC patients at the primary care level. Existence of official policy documents regulating primary PC (laws or strategies/plans/ policies) and primary PC education. Long-term care Professor Katherine Froggatt, Dr Lieve van Existence of official documents regulating PC provision at LTCFs. facilities den Block Existence of PC training programmes for staff working at LTCFs. Existence of publications regarding the provision of PC at LTCFs. Collaboration frequency between PC teams and LTCFs (estimate).
Data and empirical evidence We used annual earnings, defined as wages plus self-employment incomes in the American Community Surveys (ACS-PUMS, about 1% of the total United States population) from 2005 to 2017. 2 To be able to draw unambiguous conclusions, highlighting the impact of racial factors that can be observed, we limited our earnings inequality study to the comparison of those determined to be Black or White only; we exclude from our analysis other ethnic groups, as well as those Black and White persons that declared themselves as Hispanic. Individuals are described according to several characteristics: the geographical administrative division where they live (see Appendix A for a list of states per division); their gender; their level of education (lower than high-school certification, high-school graduation plus some college but without a degree-level qualification, undergraduate degree, and graduate degree or more); and cohort (one group every 4 years from 25-29 years old up to 60-64 years old).
Data We collect daily prices for the most traded gold-backed cryptocurrencies from coinmarketcap. com for Digix Gold Token (DGX), Perth Mint Gold Token (PMGT), Tether Gold (XAUT), PAX Gold (PAXG) and the Midas Touch Gold over the COVID pandemic period from March 2020 to August 2021. Our sample period is chosen due to data availability for the gold-backed stablecoins and trading volume of the goldbacked assets available. Gold and Bitcoin prices have been retrieved from Thomson EIKON, and http://data. bitcoinity. org/, respectively. Here, it is noteworthy that there are significant differences between the well-established Bitcoin and gold markets and the rather contemporary stablecoins. While Bitcoin and gold are traded and analysed at an exchange level, stablecoin data is available only at the market level.
This difference in scale and scope, makes any direct comparison of data meaningless. We calculate log returns as: r t = In(P t ) -In(P t-1 ) (1) where r t is the daily return on day t and P t and P t-1 are the prices at day t and day t-1. Descriptive statistics are presented in Table  1. The univariates above reveal an interesting picture of returns. While the lowest possible return (min) during the sample period (-0. 474) is observed for the Digix Gold Token, the highest return for the sample (max) is observed for the Midas Touch Gold (0. 940). In terms of median, the Bitcoin offers the highest  return compared to all other assets studied (1. 806). In terms of mean returns, similar numbers are noted for the studied stablecoins. The highest mean returns are observed, however, for Bitcoin. (See Figs.
Variables of interest We focus on three key measurements of the stablecoin market -return, liquidity and volatility. Liquidity is a traditionally important component to ascertain financial market development. It affects returns, transaction costs, market efficiency, and investment decisions in general  (Pastor & Stambaugh, 2003; Acharya & Pederson, 2005; Bekaert, Harvey, & Lundblad, 2007; Chordia, Roll, & Subrahmanyam, 2008; Lee, 2011 etc. ). We use several proxies to calculate stable-coin market liquidity (we cannot use the standard measure of liquidity due to limited data availability). First, we use the high-low range (HLR) following  Chung and Zhang (2014) : HLR t = H t -L t 0. 5 (H t + L t ) (2) where H t and L t are the high and low prices. Second, we estimate volatility over volume index (VoV) of Fong, Holden, and Tobek (2017): VoV t = ln ( H t/ L t ) ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ volume √ (3) Since Eq.
Anomalies in volume Owing to common belief that on account of weak regulation, cryptocurrency volumes are rigged and manipulated  (Ante, 2019; Crypto Integrity, 2019) , we choose to undertake a robustness test to detect anomalies in stablecoin volumes to ensure validity of our liquidity measures that use these volume estimates. Liquidity is one of the main qualities of the cryptocurrency markets. Due to this, manipulation of volumes has the potential to yield large payoffs to exchanges, to the detriment of investors. The test results are presented in Table  5. Table  5  presents results for the null hypothesis that volume data follows Benford's Law. Based on near-zero p-vales obtained, we can reject it for DGX, PAXG and Tether, suggesting deviations from Benford's Law. The distributions are presented in the appendix.
Conclusion In this paper, we empirically analyse the performance of five goldbacked stablecoins during the Covid-19 pandemic and compare them to gold, Bitcoin and Tether. While gold-backed cryptocurrencies have been designed to add stability to the digital asset ecosystem and address excess volatility, our results suggest that to the contrary, their volatility behaviour during the Covid-19 pandemic remained comparable to the Bitcoin. In addition, gold-backed cryptocurrencies did not show safe haven potential comparable to their underlying precious metal, gold. Using a tail copula methodology, we show that gold-backed cryptocurrencies are more sensitive to left tail events in the gold market. Application of quantile unit root test to our data reveals that in their tail behaviour, the gold backed stable-coins are closer to gold than to Bitcoin or Tether.
TVP-VAR methodology (a mean-based setting) confirms this result. Considering the lower and upper parts of the return and volatility distribution, represented by the 25th and 75th quantiles, respectively, results remain qualitatively similar. To further validate our baseline results, we use data measured with different frequencies, such as 30 and 60 min. The results remain intact for the 30-minute frequency, albeit vary at the 60-minute frequency. Thus, while several hedge funds incurred losses from betting against the GameStop stock, this outcome cannot be generalized to the whole population of institutional investors. Despite the overwhelming interest that the GameStop frenzy received in social media platforms in the United States and across the globe, our findings do not provide supportive evidence of financial contagion in the Fig.
Data We source both firm-level and index-level data from Bloomberg. The sample includes the GameStop prices and High Short Interest indices calculated by Barclays Capital. The High Short Interest indices were initially designed as indicative investment strategies for Barclays customers and represent equal-weighted portfolios of U. S. stocks with the top short interest, proxied by the number of shares shorted as a percentage of the total free float. The companies for the portfolios are selected from the Russell 3000 index, which mirrors the behavior of the broad U. S. equity market. We use both an aggregate High Short Interest index and sub-indices representing the most shorted stocks from different industries. The composite High Short Interest index represents the heavily shorted stocks in the U.
The sample period runs from November 2nd, 2020, to July 31st, 2021. technology. 5 Each contains 10% of the firms from the respective sectors included in Russell 3000. All indices are reconstructed and rebalanced monthly. For further information available in Bloomberg, Table  A1  in the Online Appendix provides the precise tickers of all the securities covered in the study. To our knowledge, these novel equity indices have not been explored before. For completeness, we also include the Russell 3000 index in our study. All the market data is expressed in U. S. dollars. To dynamically track the relationship between the behavior of different securities, we use high-frequency data. Specifically, we calculate log-returns estimated over 10-minute intervals. Our study period runs from 02/11/2020-07/31/2021 and overlaps with the recent GameStop frenzy.
An increase in the negative dynamic connectedness indicates that institutional investors sought to close their open short positions, which triggered a rise in the demand and, thus, the price of the GameStop stock. The ensuing fluctuation in the GameStop price translated into higher return volatility. We ran several robustness checks to validate our findings. First, we used 30-minute and 60-minute data to estimate the static and dynamic return and volatility connectedness. Our main results remained qualitatively similar, particularly for the 30-minute frequency (please see the Online Appendix). Second, we estimated tri-variate VARs for each short-interest index, which include: the GameStop stock, a short-interest index, and the Russell 3000 index. Again, the results remained supportive of Hypothesis 2.
Group-based lending is a form of loan provision for individuals and households in rural areas for production and consumption purposes. Proceeding from the imbalance between demand and supply on small-scale capital, and also Government policies on sustainable poverty reduction, the Vietnam Bank for Agriculture and Rural Development (Agribank) has coordinated with its local authorities and socio-political organizations to provide loan services via group lending. The purpose of this paper was to evaluate the status of group lending development of the Agribank-Bac Giang Branch II. The paper used secondary data collected from the branch and primary data gathered from 50 representative customers and 10 credit officers related the group lending of the branch. Data analysis methods consisted of descriptive statistics and comparative analysis, incorporated with the measurements for bank performance and quality.
Data collection In order to evaluate the performance of group lending activities, this research used both secondary and primary data. The secondary data were obtained from the published financial statements and performance reports of the Agribank-Bac Giang Branch II to analyze and evaluate the development aspects of group lending on the width, depth and timeline from 2016 to 2018. The primary data were collected from 50 representative customers of the groups and 10 credit officers of the branch who closely work with the lending groups about their evaluations on service quality of the lending group, using 5-point Likert scale structured questionnaires.
Evaluating the service quality of the group lending In this section, we use primary data collected from 50 representative customers and 10 credit For the 50 representative borrowers of the groups: Quality of the group lending product with 6 observed variables. Calculation results of Cronbach's Alpha coefficients in Table  4  show that Cronbach's Alpha is satisfactory (>0. 6) and the observed variables have a total correlation coefficient of over 0. 3. Therefore, it can be concluded that the variables used in this study are appropriate and reliable  (Nunnally & Bernstein, 1994).
An extraction grid was developed to collect information such as the name of the product, country and HTA agency, name of the report, year of publication, indication, key model assumptions, key scenario analysis, cost of the evaluated drug, cost of the comparator, incremental quality-adjusted life-year (QALY), incremental costs, incremental costeffectiveness ratio (ICER), limitations, perspective, type of the model, data extrapolation, discount rate, and sensitivity analysis (Appendix 1). Extraction was made by one analyst and the quality was checked by a second analyst. In case of any discrepancy, a senior co-author was consulted. Two types of analysis were performed for the purposes of this paper -a comparison of HTA assessments of the same product across countries, and a comparison of HTA assessments for different products within the same country.
Identified economic evaluations Overall, 983 publications were identified; 219 duplicate records were removed; and 722 papers were excluded after screening. After screening the table of content and introduction or abstract, further 27 records were excluded. Finally, 17 studies were included for the full-text review and data extraction. The Prisma diagram is shown in Figure  1. A limited number of documents were available from Germany and France. No evaluations were found for Tecartus probably because it is a relatively new therapy. Also, Glybera, because of the withdrawal, had no recent assessments. The HTAs are listed in Table  2. A part of the gathered information is presented in Table  3. Full extraction is available in Appendix 1.
YESCARTA A partitioned survival model was used in economic evaluations of Yescarta in every country; however, the ICER-US used, in addition, a decision tree before partitioned survival modelling was started  [7, [12] [13] [14]. The main differences in the models concerned applied discount rates and assumptions about costs, initial age, adverse events, data extrapolation, and clinical trials. According to the company's preferences, a mixture cure model for overall survival extrapolation and parametric curve for progression-free survival were used. Except for ICER, base-case survival was derived using parametric survival modelling from the direct extrapolation of progression-free survival and overall survival curves for 5 years after therapy completion. Alternative scenarios and exploratory analyses were considered.
CADTH The economic evaluation was conducted from the Canadian healthcare system with a lifetime time horizon (44 years) and a discount rate of 1. 5% for costs and benefits. Yescarta is recommended with the incremental cost-utility ratio of $84,030 per QALY gained for the manufacturer base case. However, CADTH highlighted the uncertainty in the cost-effectiveness model connected with a lack of head-to-head comparator, generalisability of the patient population, small size of the sample in the clinical trials, and long-term duration of remission. CADTH made a reanalysis taking into consideration alternative assumptions regarding costs, duration of an adverse event, initial age, and data extrapolation. The ICER was estimated to be $226,131 per QALY gained  [3].
HAS Construction of health state, limited data for utilities and small sample of patients in the clinical trial were the key uncertainties impacting the assessment of the economic analysis. A Markov model was conducted from a collective perspective with a lifetime time horizon (85 years). In the analysis, 4% for costs and benefits was used. The approach to modelling survival was the same as in the NICE evaluation. In the HAS assessment, adverse events that were not connected to visual ability were included. Indirect costs were also taken into consideration, but only in the scenario analysis. Also, additional ICER per blindness-free years was calculated. Luxturna was considered to have a high clinical benefit. With an ICER of €191,811 per QALY, HAS recommended Luxturna for use in inherited retinal dystrophies caused by RPE65 gene mutations  [18].
NICE Six economic evaluations of gene therapies were assessed by NICE. Most of the identified models were based on data for which no long-term follow-up was available, hence, extrapolation was necessary  [8, 9, 13]. Thus, this extrapolation and, in particular long-term survival, is one of the main reasons for uncertainty. Also, the necessity of different assumptions regarding the health states utilities considered in various models was often noticed by NICE. For example, in the Strimvelis model, health states utilities were derived from people with congenital hearing loss and may not reflect patients with ADA-SCID whose hearing loss is acquired during infancy  [4]. NICE does not have any specific framework for the assessment of gene therapies. Two drugs, Luxturna and Strimvelis, were appraised using a NICE interim process and methods of the highly specialised technology programme.
This uncertainty cannot be abolished by any robust clinical data in the near future, while the reimbursement decisions must be made already now. HTA bodies tend to take a risk and recommend using gene therapies with a relatively high threshold. They are trying to use already available frameworks for rare diseases or lifesaving treatments. This approach can be a temporary solution for now when only several gene therapies are available. The need for the development of a new assessment framework specific for gene therapies seems to be necessary looking at the long-term perspective. Our review found that similar economic models were submitted for given drugs in different countries. Some differences were identified especially in the case of models assessed by ICER-US. ICER-US is a unique HTA body that is developing its own economic models.
Basic Features of Included Guidelines European Association of Urology The EAU guideline  [6]  latest version was published in 2020 and is updated annually. Members of the Panel incorporated into the guidelines for ease of evidence acquisition and synthesis strictly systematic reviews with metaanalysis, RCTs, and prospective non-randomized comparative studies. All collected data are assessed and graded according to the GRADE approach. Based on these forms, the team determined how strong the rating was and assigned each recommendation a "strong" or "weak" rating.
American Urological Association The AUA guideline  [7]  latest version was published in 2019, an update of the previous one released in 2010. The evidence review team identified eligible RCTs, clinical controlled trials (CCTs), systematic reviews/meta-analyses, and observational studies. The team also used the Cochrane Collaboration's tool for assessing risk of bias. Overall quality of evidence was evaluated using the GRADEpro software. The quality of evidence (graded from A-C) and Linking statement type (strong, moderate and conditional recommendations, clinical principle and expert opinion) are assigned to each recommendation.
We thank  Joaquín Gómez-Miñambres  for his very helpful comments. We acknowledge financial support from the  GREEN-Econ research project  (grant  ANR-16-CE03-0005 ),  Ministerio de Economía y Competitividad and Feder  ( PGC2018-094348-B-I00 ),  Generalitat de Catalunya  ( 2017SGR-711 ), and  ICREA  under the  ICREA Academia programme.
Extending the previous versions AHPSort I, AHPSort II and GAHPSort, the GAHPSort II optimizes multi-stakeholder evaluations on large databases by reducing the number of comparisons. Moreover, the GAHPSort II method is proposed as a novel spatial decision support system because it combines a set of economic indicators for landscape and GIS methods for aiding the Decision Makers to better understand the case study and to support the definition and localization of policies and strategies of landscape planning and management.
Evaluation The GAHPSort II method was employed following the 11 steps described in section 3. 2 with the aim to sort the municipalities of this wine region, according to their landscape economic performance. 1) A multidisciplinary group of experts was involved in a survey to collect relevant information on the case study and investigate the importance of the elements of the decision problem. First, the set of indicators and the dataset were illustrated to the five experts (Table  6 , see  Appendix). Each expert answered a questionnaire, where each section referred to specific expertise; 2) The experts agreed with the four classes of landscape economic value; 3) In the questionnaire, the individual expert has defined a limiting profile for each indicator of the evaluation model. As an example, Table  1  shows the limiting profiles identified by the urban planning expert (DM4) for the indicator "Tourists presence".
All the experts' responses were imported into the Expert Choice software to derive a set of weights. Table  2  shows the pairwise comparisons attributed by DM4 to the tourism dimension and indicators. An overview of the pairwise comparisons attributed by the remaining experts is provided in Table  3 ; 5) Subsequently, the experts selected a number of welldistributed representative points. Figure  4  illustrates the representative points by DM4 about the indicator "Tourist presences". The representative points were selected from a range between 0 and 180,000, which are the minimum and the maximum values recorded in the dataset. The clustering and joining point methods reduced the number of comparisons between limiting profiles and representative points  [8, 9]. Both limiting profiles and representative points were divided into two clusters (Fig.