dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, six Activities of Daily Living (ADLs) are systematically collected in patients admitted in post-acute care. Taking head and neck cancer as a case study, we developed a multi-step process to estimate HSU. Steps I and II consist of patient data organization of the French National Hospital Discharge database including selection of incident patients and definition of five core health states over two periods (initial treatment and follow-up). Step III enables utility to be estimated daily from all records of ADLs in post-acute care with use of Item Response Theory  [14]. Step IV enables HSU to be estimated by patient and month of follow-up in the whole patient population after controlling for survivorship and selection in post-acute care.
Data source The data source was the French National Hospital Discharge (PMSI) database in the years 2008 to 2013. The database contains all public and private hospital claims for acute and post-acute care. The standardized discharge summary includes: patient' s demographics (gender, age, postal code of residency); primary and associated discharge diagnosis codes according to the WHO International Classification of Diseases, tenth revision (ICD-10); medical procedures performed; length of stay; and discharge mode (including in-hospital death). In addition, six ADLs are systematically scored at admission in post-acute care and then every week until hospital discharge (Table  1 ). For research purposes, all hospital discharge data of the patient could be traced in 2008-2013 with use of an unique anonymous identifier  [15, 16].
In patient surveys, EQ-5D-3L was mostly (8/9) assessed after initial treatment in relapse-free patients  [8]. In accordance with the usual design of patient surveys, ADLs are recorded in post-acute care in the French National Hospital Discharge database, although we aimed at expanding utility assessment to several health states including a relapse state  [5] [6] [7] [8]. We used a cross-sectional approach to define five health states over two periods: three cancer stages at initial treatment (early, locally advanced or metastatic stage)  [20] ; a relapse state and otherwise a relapse-free state in the follow-up. The initial treatment phase was defined by the first 6 months after diagnosis to encompass various lengths of combined-modality treatments  [21]  and related post-acute care. Cancer stage was identified at initial treatment from medical information that is consistently recorded at hospital  [22] : a metastatic stage was identified by any record of distant metastasis; in absence of distant metastasis, a locally advanced stage was identified by any diagnosis indicating locoregional extension (e.
44 (0. 40) and a median (IQR) of 0. 47 (0. 18-0. 76). ADL-related utility estimates were completed on a daily basis with use of linear interpolation between all assessments of the patient in post-acute care. The final dataset included 1,032,301 daily estimates of ADL-related utility in post-acute care with a mean (std) of 0. 44 (0. 38) and a median (IQR) of 0. 47 (0. 18-0. 74). Step IV: HSU estimation by month of follow-up in the whole patient population Daily estimates of ADL-related utility in post-acute care were averaged into HSU estimates by health state, patient and month of follow-up. Patients initially treated at early stage had surprisingly lower HSU estimates than patients at locally advanced stage and a selection bias in post-acute care was suspected (Fig. 4 ). Considering all patients alive at the beginning of the month in a given health state, two-step selection models were carried out by health state and month of follow-up (parameter estimates are provided at first and last month of follow-up for the 5 health states in Additional file 1: Tables  S5-S14 ).
Discussion Although many Health Technology Assessment bodies (such as the French HAS  [13] ) have deemed QALYs the principal measure of effectiveness, still only a limited number of studies report QALYs based on actual assessments of preference-based, generic HRQoL among a representative sample of patients. The assessment of new immunotherapy for relapsed/metastatic head and neck cancer provides a pressing example  [5] [6] [7]  since few patient surveys were conducted and none provided HSU estimates by cancer stage due to small sample sizes  [8]. In this study, we explored another route than patient surveys to estimate consistent HSU at the country level. On the one hand, all incident patients diagnosed with head and neck cancer in France were identified from the French National Hospital Discharge database.
Altogether, it suggests that EQ-5D-3L social value sets exhibit a poor responsiveness to change during treatment in head and neck cancer  [37]. The strengths of this nationwide study outline its limitations. Indeed, this study is a secondary analysis of the French National Hospital Discharge database and therefore all measurements relied on administrative records with possible misclassification. Regarding health state definition, TNM cancer staging is not recorded in the standardized discharge summary and we constructed a composite variable to identify three cancer stages at initial treatment. Overall, 37,508 (70. 4%) of 53,258 patients were identified at a late stage at initial treatment (Fig. 1 ), in agreement with previous reports of cancer registries  [19]. However, we could no longer estimate HSU related to the treatment modalities since this information was already used to construct the composite variable of cancer stage.
Conclusions HSU estimates in head and neck cancer were primarily driven by age at diagnosis, comorbidities, and time to assessment of cancer survivors. This feasibility study highlights the potential of estimating HSU within and across severe conditions in a systematic way at the national level. While the multi-step process to estimate HSU was developed with use of the French National Hospital Discharge database, it may generalize to other Hospital Discharge databases including a systematic assessment of ADLs for billing purposes. study design, data collection, analysis, and interpretation of data, in the writing of the report and decision to submit the manuscript.
Acknowledgments The  EPICORL (EPIdémiologie des Cancers ORL) Study Group includes: Sylvain Baillot, MSc, Translational Health Economics Network (THEN ), Paris, France;  Mélina Bec ,  MSc, Health Economics & Outcomes Research department, MSD France ;  Lynda Benmahammed, MD ,  Medical Advisor Oncology, MSD France ;  Caroline Even, MD, PhD ,  Department of Head & Neck Surgical & Medical Oncology, Institut de cancérologie Gustave Roussy, Villejuif, France ;  Lionel Geoffrois, MD, PhD ,  Department of Medical Oncology, Institut de cancérologie de Lorraine -Alexis Vautrin, Vandoeuvre Les Nancy, France ;  Florence Huguet, MD, PhD ,  Department of Radiation Oncology, Hôpital Tenon, AP-HP, France; Béatrice Le Vu, MD, MSc, Stratégie et Gestion Hospitalière ,  UNICANCER Fédération Nationale des Centres de Lutte Contre le Cancer, Paris, France & Translational Health Economics Network (THEN) ,  Paris, France ;  Laurie Lévy-Bachelot, PhD ,  Health Economics & Outcomes Research department, MSD France ;  Stéphane Luchini, PhD ,  CNRS ,  GREQAM-IDEP, Marseille, France & Translational Health Economics Network (THEN), Paris, France ;  Yoann Pointreau , , MD, PhD ,  Department of Radiation Oncology, ILC-Institut inter-régionaL de Cancérologie, Centre Jean Bernard-Clinique Victor Hugo, Le Mans, France; Camille Robert, PharmD, Health Economics & Outcomes Research department, MSD France ;  Luis Sagaon Teyssier, PhD ,  AMU/Inserm/IRD ,  UMR 912 ,  Marseille ,  France & Translational Health Economics Network (THEN) ,  Paris, France ;  Antoine Schernberg, MD, MPH ,  Department of Radiation Oncology, Hôpital Tenon, AP-HP, Paris, France ;  Michaël Schwarzinger, MD, PhD ,  Translational Health Economics Network (THEN) ,  Paris, France ;  Stéphane Temam, MD, PhD ,  Department of Head & Neck Surgical & Medical Oncology, Institut de cancérologie Gustave Roussy, Villejuif, France.
Chiappori gratefully acknowledges financial support from the  NSF  (award # 1124277 ), and  ANR  (grant Famineq). Galichon gratefully acknowledges funding from  NSF  grant  DMS-1716489 ,  ERC  grants  FP7-295298 ,  FP7-312503 ,  FP7-337665 , and  ANR  grant Famineq.
Clustering To identify common management strategies, the sets of control variables found to be optimal were further analysed using a cluster analysis. Prior to clustering, the nine inorganic fertiliser application values were summarised into 3 values; the total amount of N applied, the number of N applications (i. e. number of non-zero values), and the timing of the first application. The cluster analysis was then performed on sets of variables representing the three values summarising nitrogen fertiliser and the amount of FYM applied. The cluster analysis used a minimum variance, hierarchical clustering approach following the  Ward (1963)  method. This was implemented in MATLAB (MATLAB and Statistics Toolbox Release, 2018) using the standardised Euclidean distance. To aid visualisation, the mean profitability factor for each cluster was calculated, and only the most profitable strategies were highlighted in the trade-off curves.
Acknowledgments This work was carried out in the context of the  Laboratory of Excellence on Financial Regulation ( Labex ReFi ) supported by  PRES heSam  under the reference  ANR-10-LABX-0095. It benefitted from  French  government support managed by the  National Research Agency (ANR)  as part of the project  Investissements d' Avenir Paris Nouveaux Mondes (invesiments for the future, Paris-New Worlds ) under the reference  ANR-11-IDEX-0006-02.
In 2009-11, RoW growth rebounded to pre-crisis growth rates, and then declined somewhat. EA and US GDP growth too rebounded in 2009-11, but remained below pre-crisis growth rates. After the 1 Simpler multi-country DSGE simulation models of the role of energy for international adjustment have been developed by  Sachs (1981) ,  McKibbin and Sachs (1991) ,  Backus and Crucini (2000)  and  Gars and Olovsson (2018)  and  Bornstein al. (2018). 2 Forni et al. (2015) analyze the effects of oil shocks; by contrast, the paper here considers shocks to a broader bundle of commodities. 3 RoW includes the 58 major developed and emerging economies other than the US and the EA member countries (see Data Appendix). The US, EA and RoW together account for more than 95% of world GDP, in 1999-2016 (as reported by the IMF World Economic Outlook database).
We follow the empirical DSGE literature (e. g. ,  Kollmann et al. (2015 Kollmann et al. ( , 2016)) ), and select observables such that each shock has at least one associated observable that is strongly impacted by the shock. All shocks have a sufficiently distinct impact on observables so that shock identification is possible. The observables are not demeaned or detrended prior to estimation. The model is estimated on first differences of logged real GDP, real demand components, and price indices, and on nominal ratios of aggregate demand components, trade flows and trade balances to GDP. We use the DYNARE software  (Adjemian et al. (2011) ) to solve the linearized model and to perform the estimation. GDP in world GDP are set at 17% and 25%, respectively. The steady state trade share (0. 5*(exports+imports)/GDP) is set at 18% in the EA and 13% in the US.
Bilateral trade flows for US-EA and US-RoW are from the BEA International Trade database; EA-RoW trade flows are from the Eurostat COMEXT database. The sum of COMEXT product categories 'raw materials' and 'mineral fuels and related products' closely matches the 'industrial supplies' product category in the BEA database. For simplicity, we refer to the sum of COMEXT 'raw materials' and 'mineral fuels and related products' as.
We show that the pattern of correlations exists in the data. Specifically, we consider regressions of the form y it = α i + β 0 nf a it (1 + β 1 y t ) + γ 0 q it (1 + γ 1 y t ) + it (25) where y it is a measure of domestic activity relative to potential (the output gap or the negative of unemployment or the unemployment gap, measured at yearly frequency in the IMF WEO database) and y t is its global average, nf a it is the ratio of net foreign assets to GDP (from the  Lane and Milesi-Ferretti (2011)  database) and q it is the terms of trade, taken from the Penn World Tables version 9. 0, with a depreciation defined as an increase. Our model predicts that the interactions between global output and both the net foreign assets ratio and the terms of trade will be negative-a more positive net foreign asset position will raise employment, as will a depreciation in the terms of trade (defined as an increase in q), by more when the global economy is depressed.
This may have important policy implications, since important regional differences make place-based policy recommendations more attractive. We examine four industry-specific knowledge production function relationships using a balanced space-time panel consisting of 94 French departments. In the case of France and many other countries, knowledge production is concentrated in a relatively small group of regions. Conventional spatial econometric investigations require a large group of (contiguous) regions to produce reasonable estimates of the role played by spatial dependence/interaction between regions. This leads to conventional panel data models, that produce a set of parameters describing the relationship between the different explanatory variables and the dependent variable based on N regions over T time periods.
We take a Bayesian approach that draws on this idea. For each β i we specify a normal prior, where for notational simplicity in this development we subsume the intercept term in the matrix x it and vector of coefficients β i : p(β i ) ∼ N ( βi , Σβ,i ), i = 1,. , N (5) 3 These are patent applications in the fields of chemistry, pharmaceutical, mechanics or materials based on the location of the inventor. Source: OECD, REGPAT database, June 2012. 4 Private R&D expenditure in the field (thousands of euros). Source: R&D Survey, French Ministry of Research. This variable was lagged by two years, and smoothed over a two-year period. For example, if the dependent variable reflected patents smoothed over the 1990 and 1991 years, the R&D variable was based on the years 1988 and 1989. 5 Number of scientific publications in the field (affiliation's location).
They roughly correspond to a city and its suburbs, since French departments were historically defined in relation to the distance surrounding a central town, based on one day travel time by horse. Patents come from the REGPAT-OECD database recording patent applications at the European Patent Office. They are located according to the location of the inventor, ensuring a proper reflection of the geographical area where the innovation was produced. The dependent variable includes less than 10% of zeros (702 observations over 7 896 observations). This share however varies according to the industry under consideration. It is very weak in Mechanics and Materials (less than 5%) but more substantial in Chemistry (9%) and Pharmaceutical (20%). An interesting point regarding the HSAR specification used here is that we are fitting different distributions to each observation, producing in essence a mixture of normals.
It is well-known that mixtures of normal distributions can fit arbitrary distributions. The means that the zero issue that plagues homogeneous coefficient models that fit a single distribution to all observations of the dependent variable are lessened by the HSAR specification. Private R&D expenditure come from the French R&D survey conducted by the Ministry of Research and Technology. Public R&D expenditure is not available at the NUTS3 level. Public research effort is thus proxied by the number of scientific publications (source : PASCAL-INIST database). It is worth noting that map-The map shows island regions 2A and 2B for Corsica which were excluded from our sample. ping industry-specific innovation inputs and output required these various databases to be aligned. The patents are split into technical fields (IPC codes).
Private R&D data is collated according to the French Activities Classification (NACE) and scientific publications are recorded in various scientific disciplines. These classifications are not nested, each of them relying on its own logic. Making these databases mutually coherent was completed using an original classification produced previously (see Autant-Bernard and LeSage, 2010 for more details). 8 ased on this classification, four separate industries were examined: chemistry, pharmaceutical, mechanics and materials. These four industries allows us to cover various technological levels and various knowledge production systems, in which public and private research play different parts.
7 7 7 They roughly correspond to a city and its suburbs, since French departments were historically defined in relation to the distance surrounding a central town, based on one day travel time by horse. Patents come from the REGPAT-OECD database recording patent applications at the European Patent Office. They are located according to the location of the inventor, ensuring a proper reflection of the geographical area where the innovation was produced. The dependent variable includes less than 10% of zeros (702 observations over 7 896 observations). This share however varies according to the industry under consideration. It is very weak in Mechanics and Materials (less than 5%) but more substantial in Chemistry (9%) and Pharmaceutical (20%). An interesting point regarding the HSAR specification used here is that we are fitting different distributions to each observation, producing in essence a mixture of normals.
Study design The ODYSSEY KT study (NCT02289963) was a randomized, double-blind, placebo-controlled, parallel-group, and multicenter study  [22]. The Korean cohort study was conducted in 16 study centers across Korea between January 2015 and April 2016. The protocol was approved by the relevant independent Institutional Review Boards (Keimyung University Dongsan Medical Center IRB 2014-09-009). The study was performed according to the principles of the Declaration of Helsinki and applicable amendments and the International Conference on Harmonisation guidelines for Good Clinical Practice. All participating patients provided written informed consent.
Target achievement for LDL-C was analyzed by multiple imputations followed by logistic regression. The ITT population included all randomized patients with LDL-C values available at baseline and at least one other post-randomization time point till the end of study. The modified intent-to-treat (mITT) included the randomized patients who had received at least one dose or part of the study with an evaluable primary endpoint during the treatment period and within one of the analysis windows. All randomized patients who received at least one dose or part of a dose of the alirocumab comprised the safety set. Safety data were analyzed by descriptive statistics and were focused on the TEAE period. All statistical analyses were conducted using SAS version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Acknowledgments This study was supported by  Sanofi  and  Regeneron  in data collection and analysis, assistance with statistical analysis, and critical review of the article. Medical writing and editorial support in the preparation of this publication was provided by  Satyendra Shenoy  from  Describe Scientific Writing & Communications  and  Derek Ho  who were paid for by  Sanofi  and  Anahita Gouri  and  Rohan Mitra  from  Sanofi. The authors, individually and collectively, are responsible for all content and editorial decisions. Chang-Wook Nam ,  Dong-Soo Kim ,  Chong-Jin Kim  received payment from neither  Sanofi nor Regeneron Pharmaceuticals, Inc. directly or indirectly (through a third party) related to the development/presentation of this publication.
8 Since our data are three-year averaged, a country is considered as democratic for the corresponding period if it has a democratic regime during all three years, and as autocratic if not. 5 Countries and time periods in our sample were selected based on data availability (Table A in the supplementary material presents the list of countries in our sample). In particular, we stopped in 2007 for our results not to be polluted by the recent crisis, which is associated with a large increase in EGV in many countries in our sample. Besides, due to lack of data on some forms of democracy and control variables, the final number of countries may be lower (for example, 131 countries when considering the various forms of democracy in Table  1 ). 6 This is particularly important when measuring EGV, since a comparable average EGV among different countries in a cross-sectional setting may cover rather different dynamics over time (see  Yang, 2007).
First, eight of our political institutions variables are related to constitutional arrangements of democracies. In line with  Persson (2005) , observations for democracies characterized by a specific constitutional arrangement (e. g. parliamentary governments) are equal to 1, while observations for democracies with the alternative constitutional arrangement (e. g. semi-presidential and presidential governments) and dictatorships are equal to 0. This way, we obtain three sets of constitutional arrangements variables: (i) three binary variables of government forms, equal to 1 if in a democracy the government form is parliamentary, semi-presidential, or presidential, respectively; and equal to 0 otherwise (data used to create these variables come from the database of  Cheibub et al. , 2009) ; (ii) three binary variables of electoral rules, equal to 1 if in a democracy the electoral rule for electing members of the Lower House of Parliament is majoritarian, mixed, or proportional, respectively; and equal to 0 otherwise (data used to create these variables come from the database of Bormann and Golder, 2013); 9  (iii) two binary variables of state forms, equal to 1 if in a democracy the state form is unitary, or federal, respectively; and equal to 0 otherwise (data used to create these variables come from the overlap of two sources: the 2013 World Factbook database from the CIA, and political data from each country sheet from the website Perspective Monde of Sherbrooke University).
In addition to these constitutional arrangements variables, we use the same logic to build two additional political institutions variables, related to the current and historical functioning of democracies: (iv) a veto-players variable, equal to the average number of veto-players by three-year period if the political regime is democratic; and equal to 0 otherwise (data used to create this variable come from the Database of Political Institutions of Keefer, 2010); (v) an age of democracies variable, equal to the average number of years by threeyear period since a political regime is democratic and was not reversed until the end of our sample (data used to create this variable come from Persson and Tabellini, 2003, and from our calculations based on the Polity2 index, for old and recent democracies, respectively).
Alternative measures of main variables Regarding EGV, estimations performed using an alternative measure (the standard deviation of the output gap by three-year period, computed using the Hodrick-Prescott filter), and when abstracting from EGV outliers are consistent with our baseline results (to save space, these results are available upon request). Regarding political institutions variables, our measure of democratic regimes is based on the Polity2 index from the PolityIV database. However, as indicated by  Acemoglu et al. (2014) , since this measure could be polluted by measurement errors, it could be worthy to draw upon several sources to better document genuine changes in democratic scores (see also  Papaioannou and Siourounis, 2008). Using  Acemoglu et al. (2014) 's coding of democratic regimes for all political institutions variables used in our analysis, Table  F  in the supplementary material shows that our results are robust to this alternative measure of democracies.