dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion of Results The results were plotted as maps through Tableau software, in order to aid the Decision Makers in better interpreting the GAHPSort II outputs. Figure  6  (see Appendix) illustrates the maps of the evaluations performed by each DM. DM1 sorted most of the municipalities between the classes "poor" and "medium" for the real estate dimension. The real estate prices of properties and terrains increase in certain zones of this wine region (e. g. Asti), due to their strong economic attractiveness and to their agriculture value, determined by the presence of vineyards of certified grapes. The remaining municipalities are satellites of the main poles, thus influencing the sale prices. DM2 and DM3 sorted several municipalities between the "good" and "medium" classes. The tourism performance in this wine region is consolidated by traditions, food and wine and cultural events all year and is also encouraged by the UNESCO brand.
Background: The Hospital Frailty Risk Score (HFRS) has made it possible internationally to identify subgroups of patients with characteristics of frailty from routinely collected hospital data. Objective: To externally validate the HFRS in France. Design: A retrospective analysis of the French medical information database. Setting: 743 hospitals in Metropolitan France. Subjects: All patients aged 75 years or older hospitalised as an emergency in 2017 (n = 1,042,234). Methods: The HFRS was calculated for each patient based on the index stay and hospitalisations over the preceding 2 years. Main outcome measures were 30-day in-patient mortality, length of stay (LOS) >10 days and 30-day readmissions. Mixed logistic regression models were used to investigate the association between outcomes and HFRS score.
Results: Patients with high HFRS risk were associated with increased risk of mortality and prolonged LOS (adjusted odds ratio [aOR] = 1. 38 [1. 35-1. 42] and 3. 27 [3. 22-3. 32], c-statistics = 0. 676 and 0. 684, respectively), while it appeared less predictive of readmissions (aOR = 1. 00 [0. 98-1. 02], c-statistic = 0. 600). Model calibration was excellent. Restricting the score to data prior to index admission reduced discrimination of HFRS substantially. Conclusions: HFRS can be used in France to determine risks of 30-day in-patient mortality and prolonged LOS, but not 30-day readmissions. Trial registration: Reference ID on clinicaltrials. gov: ID:.
However, the implementation of such tools remains difficult in busy urgent care settings, in which screening can be achieved in 52% of patients at best  [11]. Thus, there is widespread interest in using electronic health records and administrative datasets to mine Big Data, derive frailty prediction models and help direct holistic care to patients most likely to benefit  [12] [13] [14]. Other key applications include service planning, benchmarking, evaluating health policies and developing markers for epidemiological research. Frailty, as a multi-dimensional construct, is not routinely documented in electronic health records and has not yet been captured by a single diagnosis code  [15]. Accumulated deficits and high resource use are commonly recognised by clinicians as key indicators of frailty in acute care settings  [16].
The Hospital Frailty Risk Score (HFRS)  [17]  was recently developed and validated in England using national hospital episode statistics data, building on the results of a preliminary cluster analysis to identify a subgroup of older patients displaying characteristics of frailty (of whom 48% had died over the 2-year follow-up period). Intermediate and high levels of frailty were significantly associated with increased risks of 30-day mortality, prolonged hospital length of stay (LOS) and 30-day readmission  [17]. A key strength of this score is that it has the potential to be implemented in administrative datasets using International Classification of Diseases, 10th revision (ICD-10) codes internationally. However, given the risk of potential discrepancies in terms of routine coding practices, this requires prior validation to confirm that the model achieves satisfactory discrimination and calibration in the specific context of application  [18, 19].
Study design and participants We conducted a retrospective analysis of routinely collected secondary care data from the French nationwide Medical Information System database (Programme de Médicalisation des Systèmes d'Information [PMSI], source: ATIH). Inclusion criteria were replicated from the initial HFRS validation study  [17]. We included all patients aged 75 years and older (on the day of the index admission) hospitalised as an emergency in Metropolitan France over a period of 1 year between 1 January and 31 December 2017. In case of multiple admissions of the same patient, only the first (index) admission was used for the analysis, in order to maintain population homogeneity. Considering their low frequency, patients with missing data for city of residence median income or medical accessibility were also excluded from the study population as described in the flowchart (Figure  1 ).
Data source and outcomes We used anonymised data from the French PMSI database, which contains routinely collected data from all public and private hospitals in France. Each in-patient stay is allocated to a single Diagnosis-Related Group based on standard discharge abstracts containing compulsory information about the patient, primary and secondary diagnoses using the ICD-10 codes  [20]. The main outcome was in-hospital mortality within 30 days from the beginning of the index admission. Other outcomes included long LOS (>10 days) and emergency readmissions up to 30 days from discharge from the index admission (excluding patients who had died in hospital). Patient covariates considered included age, sex, Charlson comorbidity index categories (0, 1, 2 or 3+, as previously described in Gilbert et al.
Strengths and limitations of this study This study was based on a very large population of patients admitted in any public or private hospital in metropolitan France, for a whole year, taking into account hospital variation and seasonality. The population is representative of all older subjects admitted to EDs in France or other comparable countries. Both discrimination and calibration were assessed in the validation process, and results were robust to several sensitivity analyses. This brings a good level of confidence in the applicability of the score to all French hospitals. However, a few limitations need to be highlighted. First, due to issues regarding authorisation to access and linking of database to national mortality registries, only inpatient mortality data were directly available from the PMSI database, which may impact validity and generalisability of our results.
Conclusions and implications This study confirms the validity of HFRS for use on French national data in relation to mortality and prolonged LOS. This is consistent with the results observed in other countries and paves the way for the conduct of international studies using equivalent datasets in populations of older subjects admitted from EDs. The HFRS is mainly applicable as a marker of frailty risk for service planning and epidemiological studies using hospital data, but in keeping with other frailty scores, not suitable for individual patient decision-making. Supplementary Data: Supplementary data mentioned in the text are available to subscribers in Age and Ageing online.
This paper investigates the relationship between energy consumption and economic growth with over eighty decades of Italian dataset. The wavelet analysis is applied to decompose series into different time scales whereas the frequency domain technique is used to examine time-specific shocks. Results of both unit root and stationarity tests indicate all series are integrated of order one, however, no evidence of long-run relationship is reported between energy consumption and economic development. We observe that the causal flow from economic growth to energy consumption becomes dominant at lower scales (up to 4 years), while at higher scales the strength of causality from energy use to growth declines. Therefore, the influence of energy consumption on economic growth can significantly be detected only at lower scales.
Based on  Geweke (1982)  null hypothesis (M) that M y→x (ω) = 0, where the frequency ωϵ(0, π), and adjusted null hypothesis (H) can be formulated as: H 0 : R(ω)β = 0 (6) with β denoting the vector related to y coefficients and R(ω) = cos(ω) cos(2ω). cos(lω) sin(ω) sin(2ω). sin(lω) (7) The null hypothesis (no predictability) posits that Y does not cause X at frequency ω 0. For validation,  Breitung and Candelon (2006)  suggest 5% critical values for all frequencies in the interval (0, π). Frequency ω is related to period t as t = 2π /ω. The empirical analysis uses yearly data of real GDP and primary energy consumption from 1926-2008. The time span is constrained by data availability. Both variables were retrieved from the ISTAT data source. Per capita GDP (RGDP) is measured in constant 2005 US$, and per capita energy consumption (EC ) is measured in tons of oil equivalent.
Natural logarithms of the series are used in the analysis. Fig. 1  illustrates the evolution of the variables from 1926 to 2008. From both panels, we observe a negative peak at 1943, which was due to the effects of WWII. In the second post-war period, an upward trend emerges in the logseries. Contrary to the trends of the original series, we observe that EC presents more oscillations than RGDP, which is confirmed by inspection of Fig. 2. In Table  2 , we present some exploratory data analysis. The mean and median values of each variable are positive and quite similar. The two variables exhibit a negative skewness, which implies the distribution is skewed towards the left, with more observations on the right. For each variable, the 10-Trim values are near to the mean, whereas the Inter-Quartile Range (IQR) reveals the absence of aberrant observations in our dataset.
The reported studies suggest that the policy of conserving energy may be implemented with little or no adverse effect on economic growth, such as in less energy-dependent economies. The overall results do not confirm the growth and neutrality hypotheses -as heterogeneous tools and datasets are used in literature with different variables and target countries  (Magazzino, 2014; Ozturk, 2010; Payne, 2010). The validity of outputs is supported by a battery of tools, working in both time and frequency domains. While the classical Granger method is mixed with wavelet technique to explore the direction of causality at different frequency bands, the pure frequency domain causality tool increases the quality of findings by performing both shortand long-run approaches for the whole time-period without sample splitting.
Load reduction during flood is estimated at 0. 14 kg PÁha À1 Áyr À1 for the Nahe, 0. 05 for the Stever, 0. 50 for the Azergues and 0. 01 for the Bresse from local reported flood events and concentrations. The two retention mechanisms are normalized to floodplain area. Concentrations and loads for the Nahe fromIttel and Saelzer (2015), for the Stever from the ELWAS database (www. elwasweb. nrw. de), for the Azergues fromBarry and Faure (2011), and for the Bresse from GayEnvironnement (2016).
ACKNOWLEDGMENTS We thank all our colleagues in OSCAR for the positive collegial cooperation, and the workshop participants in Mainz, M€ unster, and Lyon for their constructive contributions. Work on this paper funded through the  2015-2016 BiodivERsA COFUND call for research proposals , which is supported by the national funders  German Federal Ministry of Education and Research  ( 01LC1618A ),  Agence National de Recherche  ( ANR-16-EBI3-0015-01 ), and  Research Council of Norway  ( 268704/E50 ). The first author also wants to acknowledge his colleagues in the ecosystem services team of the  Nordic Centre of Excellence BIOWATER , which is funded by  Nordforsk  under project number  82263 : Discussions with Bart Immerzeel,  Eija Pouta , and  Artti Juutinen  were very useful. Erik Aschehoug helped with the writing style.
Load reduction during flood is estimated at 0. 14 kg PÁha À1 Áyr À1 for the Nahe, 0. 05 for the Stever, 0. 50 for the Azergues and 0. 01 for the Bresse from local reported flood events and concentrations. The two retention mechanisms are normalized to floodplain area. Concentrations and loads for the Nahe fromIttel and Saelzer (2015), for the Stever from the ELWAS database (www. elwasweb. nrw. de), for the Azergues fromBarry and Faure (2011), and for the Bresse from GayEnvironnement (2016) Service (CICES 5. 1 codes) Description Explanation, sources Provisioning Crops (1. 1. 1. 1) Net farm gate revenue arable farms (154-1152 €Áha À1 [cropland]Áyr À1 ) Dairy, meat, hides, Net farm gate revenue dairy wool fleeces (1. 1. 3. 1 and 1. 1. 3. 2) farms (201-1054 €Áha À1 [grassland]Áyr À1 ) Fish from ponds, mainly trout Gross income minus costs per km stream length (0-14 €Áha À1 Áyr À1 ) (1.
Further support was given to us by the French National Research Agency (Research Project PROFAM, ANR-17-CE26-0019), allowing us to fund support staff, which was of great assistance in producing the final manuscript. Since English is not the mother tongue of all the contributors to this book, we are especially grateful for the help we received from colleagues who are able to express themselves in the language of Shakespeare: Eric Marlow, Juliette Rogers, and Joanne Walker. Katia Barragan from DCS corrected the reference formats and Eric Marlow followed us through all the steps of editing and compiling of files into a final manuscript. They both saved us precious time! Lastly, we are grateful to the editors of the Work and Welfare series at Palgrave Macmillan, especially to Denis Bouget, who supported our book project from its inception to the end, taking the time to read every chapter carefully, providing detailed comments, which much improved the final manuscript.
Discussion Prior to this study, the effect of SEOs on share prices in Thailand has been investigated only once, in a dissertation  (Lerskullawat 2011). The sample for that study covers SEOs between 1999 and 2006-a period preceding the financial crisis of 2007-08. The current study uses Thai stock market data for the post-crisis period between 2009 and 2019. Few previous SEO studies considered specific firm categories, such as sound firm corporate governance  (Becker-Blease and Irani 2008) , firm book-to-market value  (Brav et al. 2000) , and firm liquidity  (Intintoli and Kahle 2010). The current study is the first of its kind in SEO literature to apply a categorization based on the timing (early, mid, and grown) and volume (small, medium, big, and super) of SEOs in relation to the IPO. Thus, this study attempts to provide a benchmark for the assessment of future SEO research trajectories.
The effect of SEOs on share price has rarely been examined for the Thai market (only once in the form of a dissertation dating back to 2011; Lerskullawat 2011), and such a timing and volume categorization has never been applied. Hence, the possibilities for discussion are still restricted. This study examines the post-crisis period between 2009 and 2019 and does not cover any influence of the COVID-19 pandemic on the SEO market. Research in the coming years can validate our results with the stock market data observed during and after the pandemic.
In contrast, countries taking more stringent measures (high FL) considerably vary in the enforcement mechanism they use. For example, Belgium and Portugal decided of very stringent restrictions (FL 9/8), yet without relying on exceptionally coercive enforcement means (DC 0). In contrast, Poland or France opted for restrictions of a similar stringency and relied on exceptional means to enforce them (DC 3). We use these indicatorss to explore the determinants of restrictive policies in European Union countries. Due to the cross-sectionalism of our data and to our limited number of cases, we are not able to perform any sophisticated statistical analyses. We instead rely on preliminary, bootstrapped bivariate analysis-a method also employed in public health research  (Pickett and Wilkinson, 2010) -to assess the association of our output variables with the factors identified in our three policy-making scenarios.
The Trade off Scenario Our first scenario holds that crisis policy responses depend on the severity of the crisis, the crisis-management capacities of a country and the level of compliance policy-makers expect from people. As current research on COVID-19 cases suggests that the reporting of cases highly depends on the quality of the monitoring systems in place at the national level  (Lau et al. , 2020) , we rely on several indicators to analyze the relationship between the magnitude of the public health crisis and the types of policy responses implemented. We use three direct and two indirect indicators. First, building on past research on the determinants of the declaration of state of emergency during the COVID-19 crisis  (Bjørnskov and Voigt, 2020) , we use the number of positive cases in a country, relying on the dataset published by Our World in Data based on the curated estimates from the European CDC  (Ritchie, 2020).
Second, as the implementation of the measures is mainly left to the police, we also include an assessment of the level of trust in the police. Both indicators vary from 1 a great deal of confidence to 4 none at all. As the management of the COVID-19 pandemic implies a high degree of compliance by the population, we also include estimates of the level of interpersonal trust, using a question focusing on the degree of confidence respondents have in other people (1. most people can be trusted, 2. you can't be too careful in dealing with people). Finally, the level of satisfaction with the political system (1 not satisfied at all, 10 completely satisfied) is usually associated with trust and compliance  (Zmerli et al. , 2007). While these data are not collected just before the sanitary crisis, they have the advantage to be comparative, complete and based on large samples.
The Ideological Scenario Political culture encompasses a large set of different attitudes, beliefs and preferences related to a political process that gives to this concept a rather fuzzy nature  (Welch, 2016). Our analytical framework is especially concerned with a specific set of attitudes, namely the tolerance for authoritarian rule that varies across countries. We proxy these attitudes by collecting data on the political past of a country. Our argument claims that countries that have experienced authoritarian rule in their recent past are more prone to relapse into it when faced with a crisis. We hence count the consecutive years of current regime type (based on  Boix et al. , 2012)  to capture the consecutive years spent under the current regime (CYCR, ranging from Croatia 21 years to the United Kingdom 136 years).
We also updated, in the same database, the past democratic breakdowns (PDB) variable. This variable takes into account all the democratic history and shows little variation across our sample, since only eight countries have experienced a democratic breakdown and only two have done so more than once (France and Greece). The variables are not correlated with each other (r -0. 3, p > 0. 1). Both variables are correlated with FL in the expected direction (CYCR r -0. 43 p < 0. 05, PDB r 0,63 p > 0. 01). Only CYRC is significantly correlated with DC (r -0. 56, p < 0. 01), while PDB slightly exceeds the conventional 10% level (r 0. 36, p 0. 11). These results hold when bootstrapping techniques are applied. Figure  9  shows that the CYCR is associated with depth of control because isolates Northern European countries that both are long-established, stable democracies and did not increase the depth of control during the pandemic.
We especially intend to collect data on seven dimensions of political exceptionalism 3 covering both the decision taken and their enforcement mechanisms based on a combination of automated and manual coding of policy decisions 4. We expect these data to further allow unpacking how governments respond to crises and how each of the factors of our theoretical framework influences different types of measures. For each event, the emergency instrument used [both at the national and subnational (regional) level], its coverage, target groups as well as its degree of implementation (degree of constraints, enforcement mechanisms such as fines, jail sentences. ) are identified. We expect such data to allow to better understand the impacts and determinants of political exceptionalism in a context where societies are increasingly exposed to various types of crises.
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
The dataset This paper uses daily price data of 18 cryptocurrencies, Bitcoin (BTC), Etreum (ETH), Ripple (XRP), Litecoin (LTC), Monero (XMR), Stellar (XLM), Dash (DASH), Ethereum Classic (ETC), NEM (XEM), Dogecoin (DOGE), Decred (DCR), Lisk (LSK), Was (WAVES), MonaCoin (MONA), DigiByte (DGB), Steem (STEEM), Siacoin (SC), and DigixDAO (DGD). The data sample spans July 25th, 2016 to April 1st, 2020 which covers various financial, economic, and pandemic events. Our sample period covers various events that can shape investors and markets of cryptocurrencies. These include four categories, split, regulation, exchange, and hacking. Table  1  summarizes those events, their dates, and the sign (positive/negative) of their impacts. Regarding the data, they are extracted from https ://coinm arket cap.
com. 5 We consider the three-factor pricing model proposed by  Shen et al. (2020a) , SMB representing the return spread of small minus large stocks, WML representing the equal-weight average of the returns for the two winner portfolios for a region minus the average of the returns for the two loser portfolios, and MKT representing the return spread between the capitalization weighted stock market. These daily factors are calculated using trading data of 1967 cryptocurrencies (12) for the sampled period. Cryptocurrency price data are collected from https ://coinm arket cap. com/. The T-Bill rate is used as a proxy for the risk-free asset and obtained through the US Department of the Treasury. The detailed procedure to compute these factors is given by  Shen et al. (2020a). S g k all→i (h) = 1 n n j = 1 i � = j θg k,ij (h).
Pharmacoepidemiologic studies on large observational longitudinal cohorts on the risks and potential benefits of medication use are the only way to bridge the knowledge gap, using and linking health/demographic/hospital data routinely collected, and already available to and accessible by researchers, RWD. In recent years however, large national administrative databases or registries, big data, have been increasingly used in the field of pharmacoepidemiology, recognizing the importance of large size longitudinal population-based cohorts. Healthcare electronic records, registries, medical claims, pharmacy data, and data from wearable and mobile technology have also become a cornerstone in the process of assessing performance and providing feedback to improve quality of health care delivery and outcomes at a population-level  (Klonoff, 2020 ).