dataesr/scientific-paragraphs-categorization huggingface dataset:
(2014). The analyses presented here provide further evidence in support of a recommendation to revise UN GHS and EU CLP classification criteria for classification of chemicals as Cat 1 based on persistence of conjunctival effects on day 21. It is strongly recommended that chemicals should not be classified as Cat 1 based on CR and/or CC scores of 1 on day 21, in the absence of any other Cat 1-triggering effects (evidence 7a). The DRD (Supplementary Material 1) also contains several studies with animals for which a CO = 4 was noted that reversed to 0 by day 21 or earlier. These studies are marked in green in the column "Comments" in the DRD (Supplementary Material 1). In the current dataset, a CO = 4 scored anytime during the observation period was noted for 261 animals out of a total of 406 used in those studies.
• Chemicals classified in vivo (Draize eye test) as Cat 1 based on CO = 4 that reversed to 0 before day 21, even if CO = 4 was observed in the majority of the animals, should not be used in either prospective studies or retrospective evaluations (conclusion supported by evidence 9). • Only those chemicals that have full observed data from which a definitive classification can be derived should be selected. Chemicals that have in vivo data that are SCNM should not be routinely used in prospective studies, unless other studies for the same chemical are available in the DRD having a clear classification that appears to be consistent with what was observed in the SCNM study. Chemicals with only a SCNM study but for which a classification can be assumed with some level of certainty, i. e. those that are identified in the DRD as "SCNM (Cat 1)", "SCNM (Cat 2A)", "SCNM (Cat 2)", "SCNM (Cat 2B)", or "SCNM (No Cat)", should still not be used in prospective studies because better options with a clear classification are available in the DRD.
Methods Database The Disease Analyzer database (IMS Health) compiles drug prescriptions, diagnoses, and basic medical and demographic data obtained directly and in anonymous format from computer systems used in the practices of general and orthopedic practitioners. 12 Diagnoses (International Classification of Diseases, Tenth Revision [ICD-10]), prescriptions (Anatomical Therapeutic Chemical classification system), and the quality of reported data are monitored by IMS based on a number of criteria (eg, completeness of documentation and linkage between diagnoses and prescriptions). In Germany, the sampling methods used for the selection of physicians' practices were appropriate to obtain a representative database of primary care and orthopedic practices. 12 rescription statistics for several drugs were very similar to data available from pharmaceutical prescription reports.
12 he age groups for given diagnoses in Disease Analyzer also agreed well with those in corresponding disease registries. 12 [15]  study population For such studies based on anonymous data in Germany no special ethic approval or patient consent is required and hence it was not sought for this study. Patients included in the analysis had been diagnosed with osteoporosis with or without fractures (ICD 10: M80, M81) and started antiosteoporotic therapy (bisphosphonates, denosumab, or strontium ranelate) between 2011 and 2014 in a German GP or OP. The last follow-up was in December 2015. Patients were excluded if they were not followed for at least 365 days after the index date. This exclusion was necessary for the correct calculation of MPR. Patients were also excluded if they were not aged between 40 and 90 years or if the initial prescription was zoledronate.
statistical analyses MPR values and the share of noncompliant patients (MPR ,80%) were estimated in the entire population and in specific subgroups. A multivariate logistic regression model was further used to determine the association between MPR (dependent variable) and age, gender, type of practice, type of osteoporosis treatment, therapy frequency, and history of fracture (covariates). P-values less than 0. 05 were considered to be statistically significant. The analyses were carried out using SAS version 9. 3.
By contrast, we also found that people with monthly treatments were more likely to be noncompliant than those with weekly treatments. This finding must be discussed with caution since the multivariate logistic regression model was not adjusted for potential confounders, which may have a significant impact. With this in mind, a US study published in 2009 suggested that patient compliance and persistence are similar for monthly ibandronate and weekly risedronate dosing. 19 Therefore, even if the difference in compliance is well known between daily and monthly administrations of osteoporosis treatment, the difference between weekly and monthly therapies remains controversial. Few authors in Europe have studied compliance with osteoporosis drugs in real-world settings. A 2012 analysis consisting of more than 265,000 individuals from a representative longitudinal database developed by IMS Health (LRx) has shown that persistence and compliance were insufficient during the time period of 2007-2009.
Further studies are necessary to evaluate the potential reasons for noncompliance in this therapeutic area. One possible explanation may be the gastrointestinal side effects. 24 ur study had several limitations. First, no information was available regarding the procedure by which osteoporosis diagnoses were assessed. Second, no detailed documentation on treatment side effects was available. Moreover, data on the socioeconomic status and lifestyle-related risk factors were also not available. The strength of this study was the inclusion of large number of patients and using multivariable regression analysis.
Data Our analysis is based on two daily variables: stock-market returns and an index of terror attacks for the G7 (Canada, France, Germany, Italy, Japan, UK, and U. S. ) countries. Using stock-market returns ensures that the dependent variable is stationary -a requirement for our causality analysis. 3 The terror index (discussed below) is stationary by design. Stock-market returns are measured in terms of the first-differenced of the natural log of the stock-market index of each of the G7 countries, where we obtained the data on the stock-market indexes from Bloomberg. Like Eckstein and Tsiddon (  2004 ) and  Arin et al. , (2008) , the daily terror index is defined as the natural logarithm of (e+number of human casualties+number of people injured+number of terrorist attacks), that occurred each day and where e denotes the exponential function.
As in  Arin et al. , (2008) , terror attacks which occurred during a weekend were summed up to the previous Friday's figure. 4 Data on the terror attacks were collected from the RAND Database of Worldwide Terrorism Incidents (RDWTI) 5  and the Global Terrorism Database (GTD) 6. The RDWTI and GTD integrate data from many important terrorism resources. Figure  1  displays the stockmarket indexes and the data on terror attacks and Table  1  shows some summary statistics of the data. As can be seen, both variables are non-normal with heavy tails, which, in turn, provides some preliminary justification for using the causality-in-quantiles test. --Please insert Figure  1  and Table  1  about here. --Given data availability on stock-market indexes and terror indexes, with the former driving the start date and the latter the end date of our samples, our samples cover the following periods after matching the dates of the two variables, Canada: 3 rd , January, 1977 to 31 st December, 2014 (9,566 observations); France: 10 th , July, 1987 to 31 st December, 2014 (6,950 observations); 2 The SIC is known to select a parsimonious number of lags and, thereby, prevents overparameterization problems associated with nonparametric approaches.
Hence, the formal discussion of the the recovered conditional volatility from univariate GARCH models have been avoided here to prevent extending the length of the paper, but these results are available upon request from the authors; (iv) Finally, to accommodate for the possibility of an important omitted variable like the VIX, we undertook an indirect approach of testing the robustness of our causality-   2011 )). Our indirect approach involves two steps: First, we estimate a linear causality model with VIX only in the regression; and second, recover the residuals from this model and apply our nonparametric causality-in-quantiles test on these residuals. So, the idea here is to create a filtered series for the stock returns, whose movements are now no longer due to the VIX. Note that, using the VIX implies that we can start only in 1986 at the earliest, with the data sourced from the FRED database of the Federal Reserve Bank of St.
Acknowledgment The contribution of the first author was completed while visiting  Aix-Marseille University  with a GREQAM AMSE grant. The work of the second author was supported by the  National Foundation for Science and Technology Development of Vietnam (NAFOSTED)  under grant no. 101. 01-2014. 62. The authors are grateful to the anonymous referee for his/her helpful remarks, which allowed us to improve the original presentation.
Data To get a first idea of the impact of the tax enforcement reform on reported contributions, we calculate aggregate time-series of charitable contributions reported in income tax files, from the annual statistical publications of the Ministry of Finance (Etats 1921). In the econometric analysis, we use individual data from four cross-sections of tax returns  (1975, 1979, 1984, 1988) , drawn every 4 to 5 years by the French tax administration for research purposes. Each sample contains around 50,000 taxpayers, with oversampling of high income households. The data therefore contains information on source of income, deductions claimed, income taxes paid, transfers received, plus standard information on marital status and number of children. We observe the total amount of reported contributions for each taxpayer and, from 1984 onwards, we also have information on receipts sent.
In order to assess to what extent the drop in contributions is due to a change in real giving behaviour or to reporting effects, we also use an external source of information on charitable contributions. There is unfortunately no comprehensive data on actual charitable contributions in the early 1980s in France, apart from the information collected in tax files, but we had access to the Annual Financial Reports of the Fondation de France (from  Pavillon [1995] ). The Fondation de France is an umbrella foundation collecting money for a large number of smaller foundations and associations. In 1979, contributions to the Fondation de France represented 7% of all contributions to foundations reported to the tax administration. The consistent financial information available for this charity from 1977 on allows us to follow the evolution of contributions around the reform date.
FRANCE (1975 -1979 VS 1984 -1988)   4 ). The sample includes all taxpayers below the 95 th percentile of the income distribution. All models include controls for the logarithm of disposable income interacted with year fixed effects, income decile fixed effects, marital status fixed effects and number of children fixed effects. The dependent variable is log(gift+100) in columns (1) and (2) and log(max(gift, 100), in column (3) and (4). Column (5) presents cell-level results after collapsing the dataset into income group x family type x year cells, where the dependent variable is log(mean gift). In order to control for the endogeneity of the price of contributions with respect to the size of a taxpayer's contribution, we instrument the log price of giving by the log of price at the first euro of charitable contributions.
Methods The economic model was adapted to the French setting in accordance with the Haute Autorité de Santé guidelines using a model previously developed for NICE. A cohort of Relapsing Remitting Multiple Sclerosis patients was simulated over a 30-year time horizon. Twenty one health states were taken into account: Kurtzke Expanded Disability Status Scale (EDSS) 0-9 for Relapsing Remitting Multiple Sclerosis patients, EDSS 0-9 for Secondary Progressive Multiple Sclerosis patients, and death. Estimates of relative treatment efficacy were determined using a mixed-treatment comparison. Probabilities of events were derived from the dimethyl fumarate pivotal clinical trials and the London Ontario Dataset. Costs and utilities were extracted from the published literature from both the payer and societal perspectives.
Data sources Probability of events. Natural history (without treatment): Disease progression transition probabilities. To fully evaluate progression of disease, three independent transition probability matrices were used in the model: • Transition matrix of movement between EDSS states within RRMS (EDSS 0-9) • Transition matrix of RRMS to SPMS (EDSS 1-9) • Transition matrix of movement between EDSS states within SPMS (EDSS 1-9) These matrices are based on the DMF clinical trials placebo arms for EDSS states up to (and including) 7 and the London Ontario dataset  [17, 18, 19]  for EDSS states 8 and 9 because of the limited number of observations beyond EDSS 8 in the DMF clinical trials. The probability of converting from RRMS to SPMS in each cycle was dependent on current EDSS. These probabilities, as well as EDSS transition matrix for patients with SPMS, were based on data from the London Ontario dataset.
Natural history: annualized relapse rates (ARR): ARR per person per year were sourced from pooled baseline data from the DMF trials, which documented the annual relapse rate in the 12 months before enrolment in the studies. Mortality rates: Age-and sex-specific all-cause mortality rates for the general population were sourced from the French mortality tables [20]. These mortality rates were then adjusted using the relative risk of death in an MS population, as compared to the general population  [21]. Efficacy: Estimates of relative treatment efficacy were determined using a mixed-treatment comparison including all available and reimbursed DMTs in France ( Adverse events (AEs): Only the DMF related AEs (identified in the DMF clinical trials) are taken into account in the model. The treatment-specific annual incidence of AEs was calculated from the independent systematic review distinguishing serious and non-serious AEs.
e. , the assets that remain to the firm after bankruptcy proceeding) than to keep going and obtain Fig. 1  Bankruptcy rate, %. Source: Firms' balance sheets' and bankruptcies' databases (Banque de France) and authors' calculations the expected profits from continued operations. The empirical part of our study provides an evidence on firms' exit behavior due to bankruptcy based on these theoretical considerations. 7  Based on a rich microeconomic data set comprising firm balance sheet information merged with the firm bankruptcy recordings database during the period 2001-2012, our econometric analysis concentrates on firms' bankruptcies in the French food industry and compares them with other manufacturing firms. This paper is organized as follows. First, we outline the general theoretical framework and selected findings from the relevant literature on firm exit.
Data description and descriptive statistics The sample is obtained by merging data from the Banque de France balance sheet database (Fiben Bilan) covering the period 1998-2012 and the firm bankruptcy database covering the period 2001-2013, also available from the Banque de France. Our analysis separately considers the food industry, the manufacturing industry excluding the food industry, and the entire manufacturing industry. Merging these two databases, we get a sample containing over 492,000 firm-year observations covering the period 2001-2012. This is an unbalanced panel data set with an average of approximately 35, 000 manufacturing companies per year. Notably, the Fiben Bilan database includes a very small number of bankrupt firms (only approximately 0. 5 % of the total number of observations over the period  [2001] [2002] [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012].
From 2001 to 2012, the French courts registered almost 4400 bankruptcies of manufacturing business units per year, but each year fewer than 250 of these bankrupt firms were in the balance sheet database the year of the failure, and nearly 1000 bankrupt firms were observed at most 3 years before the failure. Our analysis is based on the following firm economic and financial characteristics. 11 Firm productivity or total factor productivity (TFP) is estimated at the establishment level as the residual of an estimated production function using labor, capital (tangible assets), and intermediate inputs as inputs and production level as the output. Our approach is based on the semi-parametric estimator proposed by  Olley and Pakes (1996)  and  Levinsohn and Petrin (2003). According to this method, the observed input choices are used to instrument for the unobserved productivity to control for simultaneity bias and selection bias, as in the case of  Olley and Pakes (1996).
As these firms experience difficulties, the demand for external financing increases. Therefore, bankruptcy is characterized by a heavy debt burden. Firms close to bankruptcy may have greater demand for credit to cover their temporary financial difficulties rather than for investment projects. Indeed, in the year of the failure, for example, the average short-term debt is approximately two times higher than it is for healthy firms. For bankrupt firms without balance sheet provided to the Banque de France for the year of their bankruptcy, we assume that their size category and sector are the same as in the last available balance sheet in Banque de France databases at most 3 years before their bankruptcy. Source: Fiben Bilan and firms' bankruptcy database, Banque de France Independent SME not subsidiary of a corporate group, very small firm total assets ≤1 million euros, small firm total assets one million to two million euros, medium firm total assets two million to five million euros, large firm total assets >5 million euros Higher financial charges are associated with bankruptcy.
Sample description Merging data from the balance sheet database (Fiben Bilan) and the firms' database The sample obtained by merging data from Fiben Bilan and the firm' bankruptcy database includes only a small number of bankrupt firms. One of the main reasons is that a company subject to a court-administered proceeding (reorganization or liquidation) does not always provide its balance sheet for the year of failure. Thus, we link the failure occurred during the specific year by mobilizing balance sheet data from previous years. In more than 80 % of cases, companies do not provide data about their balance sheet during the year of failure, and almost 50 % do not provide these data 1 year before their failure as well. Thus, we use data on the latest non-missing balance sheet collected as long as it relates to the firm's situation during the year of the firm failure or at most 3 years before the failure.
Econometric sample construction The sample obtained from merging data from the balance sheet database (Fiben Bilan) and the firms' bankruptcy database was further modified for the empirical analysis. First, our econometric model continuous variables' outliers were dropped from the sample. To do that, we deleted all observations that are lower (higher) than the threshold value calculated as Q1 -5 * IQR (Q3 + 5 * IQR) where Q1 (Q3) is the first (third) quartile and IQR is the interquartile range. Second, as we decided to use the information at most 3 years before bankruptcy, for each firm, new observations were completed for the missing years on the basis of the past available information concerning the variables that were used in our analysis. In particular, for bankrupt firms without balance sheet provided to the Banque de France for the year of their bankruptcy, we assume that their size category and sector are the same as in the last available balance sheet in Banque de France databases at most 3 years before their bankruptcy.
Concerning the continuous variables, we calculated new variables, defined as the average of their past available observations at most 3 years before the bankruptcy. Concerning firms for which only one observation or two observations are available in the past at most 3 years before the bankruptcy, new variables are calculated respectively as equal to the 1-year lagged observation or as the average of their two past available observations. These new variables were particularly used in the econometric part. The table describes this new extended database (Table  9 ). Bankruptcy rate and method of computation of bankruptcy rate in Fiben Bilan Companies that are subject to collective insolvency proceedings rarely provide the de France with recent accounting documentation. We take into account this fact when calculating the failure rate using the database of firms' financial statements.
We calculate the failure rate on a population of firms that have provided their balance sheet data during the previous 3 years to the year N. We select all the healthy firms on 1 January of year N for which the data for previous 3 years are available in the database: N -1, N -2, or N -3. Then, we count, among these firms, those who start a judicial reorganization or liquidation between 1 January and 31 December of year N. The ratio of these two quantities is equal to the annual failure rate of the firms with a balance sheet available information during the past 3 years. H 0 : β 1 ð Þ t fp ¼ β 2 ð Þ t fp ; chi-square ¼ 7:24: H 0 : β 1 ð Þ CostCredit ¼ β 2 ð Þ CostCredit ; chi-square ¼ 0:33: H 0 : β 1 ð Þ BankCredit ¼ β 2 ð Þ BankCredit ; chi-square ¼ 2:34: IV instrumental variable, WEML weighted exogenous maximum likelihood weight *p < 0.
Standard errors in parentheses. Panel-robust standard errors. Notes: Choice-based sampling is realized on observation-level. Sampled firm-year observations were completed with available observations for the same panel from the original database. WEML weights were calculated for each year separately, but this table presents only the average WEML weights over our analysis period. Choice-based sampling is realized on the full sample of manufacturing industry. The sub-samples of the food industry and of the manufacturing industry (excluding food industry) were extracted from the manufacturing industry choice-based sample IV instrumental variable, WEML weighted exogenous maximum likelihood weight *p < 0. 05, **p < 0. 01, ***p < 0. 001.
Parameter estimates, probit regression of firm bankruptcy using WEML on choice-based samples, food industry, and other manufacturing industries (excluding food industries) Standard errors in parentheses. Panel-robust standard errors. Notes: Choice-based sampling is realized on observation-level. Sampled firm-year observations were completed with available observations for the same panel from the original database. WEML weights were calculated for each year separately, but this table presents only the average WEML weights over our analysis period. Choice-based sampling is realized on the full sample of manufacturing industry. The sub-samples of the food industry and of the manufacturing industry (excluding food industry) were extracted from manufacturing industry choice-based sample.
Standard errors in parentheses. Panel-robust standard errors. Notes: Choice-based sampling is realized on observation-level. Sampled firm-year observations were completed with available observations for the same panel from the original database. WEML weights were calculated for each year separately, but this table presents only the average WEML weights over our analysis period. Choice-based sampling is realized on the full sample of manufacturing industry. The sub-samples of the food industry and of the manufacturing industry (excluding food industry) were extracted from manufacturing industry choice-based sample. Comparison of parameter estimates between (1) and (2).