dataesr/scientific-paragraphs-categorization huggingface dataset:
The nominal exchange rate is expressed as the number of foreign currency units per domestic currency. An increase therefore corresponds to an appreciation of the domestic currency against the foreign currency. P i,t and P j,t stand respectively for the price index of country i and of country j. The REER is extracted from the EQCHANGE database  (Couharde et al. , 2018). The literature has proposed several methodologies to estimate the ERER. 8 Beyond the Purchasing Power Parity (PPP) approach which presents several drawbacks, 9  three main theories of equilibrium exchange rates can be distinguished  (Jeong et al. , 2010)  : a) the Fundamental Equilibrium Exchange Rate (FEER;  Williamson, 1994) , b) the Natural Real Exchange Rate (NATREX;  Stein, 1994 )   and c ) the Behavioural Equilibrium Exchange Rate (BEER;  Clark and MacDonald, 1998 ).
Growth determinants: A BMA approach In this subsection, we present the results of the BMA methodology. The BMA approach is implemented using different model priors: a random, fixed and uniform prior. As previously mentioned, we consider two different parameter priors are considered: a Uniform Information Prior (UIP) and a Risk Inflation Criterion (RIC). Table  1  below displays the BMA results. Among our initial dataset of 18 potential growth determinants, six variables emerge as robust, e. g having a PIP over 0. 50. As expected, we find strong support for the conditional convergence hypothesis as the real initial GDP per capita (PC) presents a PIP nearly equals to 1. This result is in line with  Durlauf et al. (2008)  and is not affected by a change in the model or parameter space. Furthermore, our results point out that the fertility rate constitutes a relevant growth determinant.
Robustness checks Given the long time span of our panel data set, countries were hit by several currency crisis episodes. More specifically, our sample contains economies belonging to the CFA zone which were hit by a crisis in 1994 due to a modification in parity. Furthermore, there are also Asian economies affected by currency crises in 1997-1998. The presence of such events can impact our previous findings in several ways. Firstly, one symptom inherent to currency crises is the presence of large currency overvaluations  (Kaminsky and Reinhart, 1999)  which are then followed by a sharp devaluation leading generally to an undervalued currency. These crises can affect our results as they are followed by lower GDP growth  (Hong and Tornell, 2005)  in the three years following the collapse. Moreover, the years following these crises are characterized by higher inflation because of the devaluations  (Borensztein and De Gregorio, 1999; Mallick, 2005).
This higher inflation rate could lower economic growth by penalizing the investment rate in the economy (see  Hong and Tornell, 2005). All in all, our results can be impacted by the currency crises. In order to check the robustness of our results to the presence of currency crisis episodes, we use the database proposed by  Laeven and Valencia (2013). A detailed description of the different episodes of currency crises is available in Table  12  in appendix. We control for the currency crises by creating a dummy variable which is equal to 1 during the year of the currency crisis and the following two years, 0 otherwise. 14 As soon as the dummy variable is equal to one, we exclude these observations from our sample. The results are displayed in Table  11  in appendix. As expected, the exclusion of episodes of currency crises from our sample leads to weaken the significance for inflation.
Study population This was a retrospective study using data from the RDPLF. All adults >18 years starting PD in France between 1 January 2006 and 31 December 2015 were included. No patient was lost to follow-up. The RDPLF database has been shown to be reliable, with coverage of 99% in France in 2016  [22, 23]. Age at PD start, gender, underlying nephropathy, diabetes mellitus, previous therapy before PD initiation (HD or renal transplantation), suboptimal starters (defined as a period of <30 days on HD before PD initiation) and centre category (academic, community, non-profit and private) were obtained from the registry  [24]. To evaluate patient comorbidities, the Charlson comorbidity index (CCI) was extracted from the database, and the modified CCI was calculated by subtracting the age subscore.
Conditional probabilities for the event of interest were estimated at specific time points. A bivariable analysis was performed to estimate the association between each variable and the event of interest. The time variable was included a priori in the multivariate analysis; otherwise, variables were included in the model based on the results of the change in the estimate procedure with a cut-off value of 10%. Interactions between time and the other covariates were tested in the multivariate models. Missing data were <5%, enabling us to use a complete case analysis. Analyses were performed with R 3. 4. 3 (R Foundation for Statistical Computing, Vienna Austria, including the survival package).
DISCUSSION Assisted PD programmes are available in different countries around the world, with differences from one country to another  [12]. To our knowledge, the trend in the utilization of assisted PD among incident PD patients over time has not yet been documented at a national level. We hereby provide a description of the utilization of assisted PD over a decade in a country where assisted PD has been available for >30 years and is fully covered by healthcare insurance. The high exhaustivity of the RDPLF makes it a reliable database  [22, 23]. With the CKD population growing older and frailer, it would be anticipated that the use of assisted PD should increase over the years. However, our results show a different pattern; there was a decline in the rate of assisted PD until 2013, while the total number of dialysed patients continued to increase  [19].
Acknowledgements O. M. would like to thank  Université Lyon 1 ,  Université Jean Monnet  and  Institut Camille Jordan  for material support. H. D. N. is funded by  Australian Research Council  grants:  DE170101134  and  DP180101192. This work was supported by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
Pan-Cancer Across the entire range of cancer -"pan-cancer" -there is also rich potential in the integration genomic profiling, matching the right targeted therapy to the detected genetic aberrations, and evaluating treatment outcome. Applications combining testing using NGS with artificial intelligence and machine learning may ultimately help chart entire clinical pathways  [42, 43]. This would require a clinical infrastructure that could turn fit-for-purpose real-world data into real-world evidence to help close evidence gaps, overcoming the deficiencies of existing datasets, that are frequently incomplete, non-standardised, retrospective, non-accessible, siloed and local  [44] [45] [46]. The concept of a global or even pan European pan-cancer registry, focussed on NGS testing, treatment selection and outcome, would provide a large and high-quality data set and could offer valuable, much needed insights to help chart more personalised clinical pathways and also provide valuable learning on what is needed to share data across borders.
The appropriate governance framework would need to be addressed but could build on initiatives such as Million European Genome initiatives as well as the European Health Data Space. Building such a large data set that links diagnosis including NGS and biomarker testing, treatment and outcome data creates a new realm of opportunities, not just in regards to the insights generated by the data itself but also in regards to taking learnings from the challenges of setting up such an initiative. These insights are important on many levels, they will help identify critical data variables and point to important data standards and demonstrate where local regulations prevent or make it difficult to share data across borders. In the context of data insights, valuable information pointing to current use of NGS technology, its impact on clinical decision-making and ultimately impact on patient outcomes is equally important as well providing insights on how treatments were accessed by patients, outcomes, in terms of real-world overall survival and progression-free survival, and best clinical response.
• Member states should each create a national system to validate and set up tests, and Europe should move towards data centralisation, to permit the creation of robust data sets across the continent to generate cost-benefit evidence. This should include clear minimum criteria and formats for reporting in terms of standardised metrics taking account different stages of testing. • Data sharing policies across member states should be revisited to facilitate European multi-institutional and multi-national studies every 3 years, with appropriate attention both to the movement of data and relevant privacy legislation. Europe-wide initiatives already underway for centralising genomic data may provide some models. A repository for haematological malignancies for a 100,000-molecule database is under construction as part of the Innovative Medicines Initiative's "Big Data for Better Outcomes" project, to bring biomarkers back to the clinic  [82].
12 ). Accredited testing facilities for ISO 15189 (or equivalent) complemented with the preanalytical ISO/CEN standards is needed for reproducibility and generation of useful datasets and registries. In addition, scientific medical studies based on biomaterials derived from patients should also use the ISO/CEN pre-analytical standards to collect the samples. This can best be organised in centralised biobank facilities in every academic medical centre, which are for the collection of the biomaterials integrated in the existing diagnostic pathways and accredited for ISO 15189 (or equivalent) and the complementary pre-analytical ISO/CEN standards. This way, the scientists use standardised samples to discover biomarkers which are better reproducible in the validation process needed for the test to become a diagnostic test  [14].
Routine use of comprehensive genomic sequencing, computational power and advances in bioinformatics would allow the analysis of complex genome datasets in real time, generating a dynamic pool of curated genomic and clinical data, drawn from multiple sources. New applications of NGS, e. g. RNA sequencing, would enable the of gene expression in tumour samples supply additional information to DNA sequencing. This, together with, for example, proteomic approaches will add further insights into the functional role of genomic alterations. Some applications combining testing using NGS with artificial intelligence and machine learning may ultimately have the potential to help chart entire clinical pathways  [17]. Artificial Intelligence may provide advice for a given specific patient to the clinician, based on the analysis and processing of a multitude of data of a multitude of similar patients.
In healthcare, its evolution is marked not only by the emergency of the COVID-19 pandemic, which has largely monopolised the attention of all the major EU institutions throughout most of the early months of the year, but also by the constant increase in morbidity, which now affects more than its ageing population. 2020 was already designated as the year for action against cancer, with two parallel initiatives, and for the initiation of a European Health Data Space and a new Pharmaceutical Strategy. It is an appropriate moment also for it to review its approach to testing as an intrinsic element in an integrated health strategy. In these straitened circumstances, a reappraisal of the significance and potential of biomarkers can provide the EU and its citizens with a much-needed immediate up-tick in quality and accuracy of care, and a change in policy that holds out the prospect of a radical transformation of care in coming years as the full benefits of an optimum approach to the use of biomarkers begin to be felt.
Recommendations to Member States • Member states to agree on a federated structure of national databases. • Member states should ensure that new validated biomarker tests are rapidly made available to patients. • Member States to incentivise the development and uptake of biomarkers of limited interest to commercial companies (early detection biomarkers and risk biomarkers). • Member states/EMA to better synchronise CDx approval processes with drug approval. • Member states to provide clarity on funding routes for biomarker testing. • Member states to allocate resources specifically for discovery and validation of biomarkers, and promote engagement between payer organisations, biomarker developers and the wider healthcare stakeholder community (vertical integration and horizontal integration).
Recommendations to Member States•Member states to agree on a federated structure of national databases. • Member states should ensure that new validated biomarker tests are rapidly made available to patients. • Member States have incentivized the development and uptake of biomarkers of limited interest to commercial companies (early detection biomarkers & risk biomarkers). • Member states/EMA to better synchronise CDx approval processes with drug approval. • Member states to provide infrastructure for CDx evaluation and demonstration of clinical utility. • Member states to provide clarity on funding routes for biomarker testing. • Member states to allocate resources specifically for discovery and validation of biomarkers, and promote engagement between payer organizations, biomarker developers and the wider healthcare stakeholder community (vertical integration and horizontal integration).
Matrix of the E-Health Data Solutions. Governance Legislative and non-legislative measures on governance and rules for primary and secondary use of data, respecting GDPR Free movement of digital health services AI framework White paper (incl. AI and liability Fig. 6. Horgan et al. : Bringing Greater Accuracy to Europe's Healthcare Systems: Quality of data Infrastructure Capacity building Better healthcare Better policy making Better research and innovation Uptake and develop the EEHRxF FAIR-ification of health data for primary and secondary use MyHealth@EU ERN-CPMS 1+mil genomes Darwin (regulators) Link different repositories (e. g. registries: cancer, ERN, transplantation, other national databases) Images database Research infrastructures Digitalisation of healthcare systems NCPeH Tele-health AI Digital skills The Unexploited Potential of Biomarker Testing in Oncology for immediate improvements, and -more idealistically -developing a vision of what dramatic further contributions biomarker testing could deliver with greater resources and policy commitment.
At that time, some countries in Europe were already largely impacted in terms of mortality rate (i. e. , the front-line countries), while the remaining countries were less affected (i. e. , the second-line countries). In addition, we assess the short-term forecast performance of the mixture model briefly introduced above with COVID-19 data (the forecast made at a given time being compared to actual data after this time). These data allow us to compute the performance of our approach in diverse real situations instead of synthetic simulated situations. Moreover, the mixture model is compared to a SIRD compartmental model including susceptible, infectious, recovered and dead individuals using two distributional assumptions for the observations (Poisson and negative-binomial) and to a log-linear regression model.
Worldwide COVID-19 mortality data Mortality data are obtained from the Johns Hopkins University CSSE  [3]  and The Covid Tracking Project (https://covidtracking. com), which provide, in particular, daily mortality data at the level of countries, states or provinces, grounded on diverse data sources including the World Health Organization and national government health departments. Here, we use data from the 15 European countries having at least 200 registered deaths by April 12, 2020 (namely, Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Netherlands, Poland, Portugal, Romania, Spain, Sweden, Switzerland, United Kingdom), and from Hubei province in China. Hubei was the first COVID-19 hotspot.
The methodological quality of each study was evaluated using the Mixed Methods Appraisal Tool (MMAT). 18 In our review protocol, the inclusion criteria were established so as to be as exhaustive as possible. These criteria included studies analyzing health preferences regardless of the method used, eg, discrete choice experiment (DCE), qualitative studies, mix method design, ranking studies, swing weighting studies, analytical hierarchy process, and best-worst scaling. We also used studies referring to acute or chronic pain treatments in the low back region. Exclusion criteria were: preferences other than those of patients, sub-studies of other studies, studies about utilities associated with any health condition, studies combining data from patients with pain other than in the low back region, and studies that only referred to surgical treatment (ie, a study could compare surgical treatment with non-surgical treatment, but could not compare two surgical treatments).
There was no limitation of language. As per protocol, inclusion and exclusion criteria were established before conducting searches in the electronic database and were applied to the final search field. The search engines used in this systematic review were PubMed, ScienceDirect, and Scopus. In addition, to consider unpublished studies we completed the review by scanning references of included studies and contacted the authors who had performed a literature review prior to conducting their research. However, we did not perform a specific search in the grey literature. The search was conducted without date limits through June 21, 2018, using combinations of key search terms such as: "low back pain, " "lumbosacral region, " "health preference, " "patient preference, " "stated preference, " "stated choice, " and "treatment.
01-0. 20, none to slight; 0. 21-0. 40, fair; 0. 41-0. 60, moderate; 0. 61-0. 80, substantial; and 0. 81-1. 00 was almost perfect agreement. Data were extracted by 1 reviewer (MB) and a second reviewer (TGP) checked and completed this data for accuracy. Any additional information added in the extraction grid was discussed between the 2 reviewers and disagreements were solved by the arbitration of a third reviewer. The main variables of interest in this systematic review were the preferences attributes and their levels. The following variables were also systematically collected: country, type of study, type of treatment, numbers of patients and their characteristics, results as a ranking or a size effect, type of statistical analysis, and other available characteristics, such as the recruitment process and the nature of the treatment experienced.
Results In total, 390 studies were identified after the removal of duplicates, 37 of which were fully read to assess their eligibility. A total of 13 studies were selected to be included in the systematic mixed studies review. The Cohen's kappa coefficient was 0. 7937 in the first phase of the selection process (screening of both titles and abstracts) and 0. 9217 in the second phase (full-text readings). The reasons for excluding 24 studies that were fully read were as follows: the study was a systematic review without original data (n = 3)  [19] [20] [21]  ; the study did not consider the preferences of patients (n = 4)  13, 14, 22, 23  ; the study analyzed preferences but not for treatment characteristics (n = 11)  [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  ; the pain site was somewhere other than in the low back or data were aggregated with other sites (n = 4)  [35] [36] [37] [38]  ; the study was a sub-study of another one (n = 1)  39  ; and data was not available even after contacting the authors (n = 1)  40.
This could be interpreted as a methodological limitation for this review and could impede the comparability between results. Second, not all studies used the same attributes, which makes the comparison of attributes between studies even harder. Third, we indirectly assessed the risk of bias of the included studies using the MMAT which is imperfect considering that this tool mostly evaluates the quality of mixed-methods studies. However, we are not aware of specific tools to assess the risk of bias in preference studies. Fourth, all reviews, including the present one, is limited by the search strategy and the selection of databases, which may have led to some missed studies. Fifth, preferences may vary across populations with disparate demographic characteristics, but due to limited data provided in the studies we were not able to assess if these characteristics have an impact on patients' preferences.
Data sources and sample This study used two secondary data sources. Advertising data were collected from Advertising Age, and performance and control variable data were collected from Compustat. US government were removed due to incorrect fit or missing performance data. Organizations that appeared only once or twice in the top advertisers list were also removed. Data for some advertising media, and sales performance data for some organizations for some years, were unavailable. Removing firms with missing data was necessary because the data envelopment analysis (DEA)-based Malmquist productivity index (MPI) is sensitive to missing data. The total number of observations corresponding to each variable is shown in Table4 later. All variables were winsorized at the 1% and 99% percentile to address potential outliers.
, 2020). The focal organization is productive over time if the organization can lessen in-put utilization while maintaining the same level of output. Otherwise, the organization is unproductive  (Tavana et al. , 2020). Productive organizations in the sample form the best practice frontier, which dynamically benchmarks other organizations' level of productivity  (Luo and Donthu, 2006). The set of firms in the dataset that are most productive in using their advertising inputs to produce a certain level of advertising outputs are considered the best in managing their advertising programmes. Other organizations that are not part of the productive frontier follow these best-practice firms to heighten productivity. The MPI is a product of two components: organization-specific efficiency change and industry-wide change that affect advertising innovation improvement or otherwise (usually termed 'technology change').
The research and development (R&D) activities of the organization can affect stock market-based performance measures  (Hughes et al. , 2019). We controlled for R&D using R&D expenses divided by total assets. We controlled for capital intensity because it can impact performance  (Huselid, Jackson and Schuler, 1997). Capital intensity was measured as invested capital divided by the total number of employees. We controlled for firm growth, operationalized as the yearly growth rate of total assets. Table  3  describes the variables used in this study and their data sources.
Spells from the steady states before and after a policy change In this subsection we consider empirical inference if the data collection leads to two samples: one in which Pr(S = 0) = 1 and one in which Pr(S = ∞) = 1. In the context of policy reform evaluation, these samples originate from two subpopulations. One sample is drawn from the inflow into the state of interest after the reform and thus satisfies Pr(S = 0) = 1. Notice that this assumes that the reform is universal. The other sample is drawn from the inflow before the reform. Using the notation S = ∞ for the latter sample requires some explanation. Even if the inflow occurred a long time before the reform, some spells may not be completed at the reform date. This means that in fact the individual is exposed to the reform at a finite elapsed duration.