dataesr/scientific-paragraphs-categorization huggingface dataset:
Population Among the 1300 conference participants, 755 (58%) directly completed the questionnaire. In addition, 249 GPs and GP trainees who were contacted by mail also completed the questionnaire. In total, data from 1004 questionnaires (one questionnaire/person) could be analysed. The participants' mean and median ages were 35. 8 and 32 years, respectively (range: 21-71 years); 617 were women (61. 64%), 622 (61. 96%) were practising GPs and 357 (37. 2%) were GP trainees (online supplemental table 1). Participants lived mainly in freestanding houses (52. 15%) or in flats (42. 64%). Only 5. 51% of participants lived in shared accommodations. Moreover, 52. 49% of participants had children or students in the living in the household, and 25. 27% had school-age children (from nursery to high school).
Acknowledgements We acknowledge assistance from  Dr Serge Gilberg  for his help with the questionnaire and  Dr Catherine Plotton  and  Dr Olivier Saint-Lary  for the distribution of the questionnaire. The authors would like to thank  Elisabetta Andermarcher  for providing medical writing and editorial assistance in the preparation of this article. This manuscript is supported by the  French network of University Hospitals HUGO ('Hôpitaux Universitaires du Grand Ouest ').
E. L. D. was partially supported by the  Academy of Finland  ( grant288501  ' Geometry of subRiemannian groups ' and by grant  322898  ' Sub-Riemannian Geometry via Metric-geometry and Lie-group Theory' ) and by the  European Research Council  ( ERC Starting Grant   713998   GeoMeG  ' Geometry of Metric Groups '). S. R. is partially supported by  ANR  Project  SRGI (Sub-Riemannian Geometry and Interactions)   ANR-15-CE40-0018.
Objectives: In randomized-controlled crossover design trials, overall survival (OS) treatment effect estimates are often confounded by the control group benefiting from treatment received post-progression. We estimated the adjusted OS treatment effect in EMPOWER-Lung 1 (NCT03088540) by accounting for the potential impact of crossover to cemiplimab among controls and continued cemiplimab treatment post-progression. Methods: Patients were randomly assigned 1:1 to cemiplimab 350 mg every 3 weeks (Q3W) or platinum-doublet chemotherapy. Patients with disease progression while on or after chemotherapy could receive cemiplimab 350 mg Q3W for ≤108 weeks. Those who experienced progression on cemiplimab could continue cemiplimab at 350 mg Q3W for ≤108 additional weeks with four chemotherapy cycles added.
EMPOWER-Lung 1 study design As previously reported (9), EMPOWER-Lung 1 (NCT03088540) included adults (age ≥18 years) with histologically or cytologically confirmed stage IIIB or IIIC (not candidates for definitive chemoradiation therapy) or previously untreated stage IV squamous or non-squamous NSCLC with PD-L1 expressed on ≥ 50 % of t umor cells , m easured using the PD -L1 immunohistochemistry 22C3 pharmDx assay. Prior adjuvant or neoadjuvant chemotherapy was permitted, provided that patients developed recurrent or metastatic disease >6 months after completing therapy. Prior cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) treatment was allowed, provided that the last dose of such an antibody was ≥3 months before the first dose of study drug. Additional eligibility criteria included an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1; adequate organ and bone marrow function; and presence of at least one measurable lesion per the Response Evaluation Criteria in Solid Tumors version 1.
The simplified two-stage correction was preferred, since the RPSFT method did not allow for adjustment of continued treatment based on standard applications, and\ the IPCW method relies on stronger assumptions and is more prone to bias in relatively small sample sizes  (11). Both the IPCW and simplified two-stage correction methods rely on the assumption of no unmeasured confounders. For this analysis, in cases of missing covariate values over time, the last recorded measurement before a given timepoint was used. Since the IPCW method requires that the covariates be measured consistently over time, there were more instances of using the last recorded covariate measurement which could have introduced more uncertainty from the covariates than in the simplified two-stage correction. All three methods produced consistent treatment effect estimates, demonstrating the results were not sensitive to the adjustment method applied to the EMPOWER-Lung 1 data set.
Data availability statement Qualified researchers may request access to study documents (including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan) that  support the methods and findings reported in this manuscript. Individual anonymized participant data will be considered for sharing once the product and indication has been approved by major health authorities (e. g. , FDA, EMA, PMDA, etc. ), if there is legal authority to share the data and there is not a reasonable likelihood of participant re-identification. Submit requests to https://vivli. org/.
Sources Three distinct topics were considered: one documenting the use of UCHP in the population, a second the UCHP-related aberrations and a third the communication in the media on UCHP. The use of UCHP in the population was based on reviews of recent surveys conducted in France and Switzerland  [14, [21] [22] [23] [24]. These surveys were approved by the respective national and institutional committee and the consents of all subjects and/or their legal guardians were obtained from the survey itself. UCHP-related misconducts were extracted from the yearly reports of the Miviludes and the CIC. Numerical data were available from 2010 to 2021 for Miviludes  [25]  and from 2003 to 2022 for CIC  [26]. Regarding media communication on UCHP, we conducted two complementary analyses. The rst analysis (Analysis 1, see details below), was centered on major French newspapers.
Sample My sample consists of 506 commercial banks established in 28 European countries 1. I construct the interbank networks by including all available commercial, investment and real estate banks in the Bankscope database in each country 2. Therefore, network statistics in this study capture connections of banks in my sample (only commercial) with all possible banks in their respective countries. The selected sample period is from 2001 to 2013. Accounting data (annual financial statements) for individual banks are obtained from Bankscope Fitch IBCA. Bankscope reported balance sheets and income statements for 1,238 commercial banks for the countries considered in this study. After eliminating banks for which Bankscope does not report information on variables of interest, the final sample of banks consists of 506 banks.
Interbank Network This paper aims to examine whether the causal relationship between bank capital and liquidity is influenced by interbank network characteristics. To achieve this, network variables are extracted from interbank lending-borrowing relationships. Construction of the interbank network requires an estimation of the bilateral transactions between each pair of banks in the interbank market. A crucial shortcoming when studying the interbank exposure is the unavailability of bilateral transaction data, as financial authorities do not require banks to report such information in most European countries. The only available data is the aggregate interbank loans and deposits in each bank. The most relevant and applied method for constructing the interbank network based on the aggregate interbank transactions is the minimum density (MD) algorithm, proposed by  Anand et al.
Conclusion Existing literature has thus far neglected to address how the causal relationship between bank liquidity and capital is influenced by its network characteristics. In this paper, I augment traditional capital-liquidity relationship models with network statistics to assess whether and how this relationship depends on a bank's local and system-wide network characteristics in the interbank market. Using a GMM simultaneous equations approach applied to a dataset of listed and unlisted banks from 28 European countries, my study shows that bank capital ratio is not only explained by the macro environment and the individual bank characteristics outlined in the literature, but also by the relationship between liquidity and interbank network topology. My findings suggest that while banks with weak local or system-wide interbank positions improve their solvency when they face higher illiquidity, those with strong local interconnectedness do not substitute capital for liquidity.
, 2021; Wennberg & Anderson, 2020). Our study follows such an explorative approach to examine how late-career entrepreneurship is related to innovation. We use data on solo founders from the representative IAB/ZEW Start-up Panel database on newly founded German ventures between 2008-2017 to develop our understanding of late-career entrepreneurship as potentially innovation-driven business activity. In addressing this, we combine insights from lifespan theory  (Bohlmann et al. , 2017; Gielnik et al. , 2018)  and innovation research in entrepreneurship  (Autio et al. , 2014; Baron & Tang, 2011)  to explore the creation by latecareer entrepreneurs of 'new to the market' and 'new to the firm' innovations. We maintain that innovations 'new to the market' introduce novel products and services that precipitate changes in the markets, whilst innovations 'new to the firm' primarily improve a firm's offering and processes  (Rosenbusch et al.
Data and sample Our dataset combines primary survey data on startups with individual-level register data on the founders and employees. The survey data are drawn from the IAB/ZEW Start-up Panel, a representative panel dataset of new German ventures that is based on a stratified random sample of legally independent new ventures in all industries other than the primary, energy, and public sectors. The IAB/ZEW Start-up Panel is a joint project of the Institute for Employment Research (IAB) of the German Federal Employment Agency, the Leibniz Centre for European Economic Research (ZEW) Mannheim, and Creditreform, Germany's largest credit rating agency. The sampling frame of the IAB/ZEW Start-up Panel is the Mannheim Enterprise Panel, which contains basic information (postal addresses, year of establishment, sector of activity, and legal form) for almost all German firms.
This population database is fed by firm and address data assembled by Creditreform, who automatically scan new firm registries in Germany. Moreover, regional units of Creditreform thoroughly search for otherwise undetected, newly entering firms and assemble their basic firm and address data (for a detailed description, see  Bersch et al. , 2014). The IAB/ZEW Start-up Panel data are collected via computer-supported telephone interviews with the companies' founders and provide information on founders' essential characteristics (educational background, experience in managerial and entrepreneurial leadership) and venture characteristics (details on employment, innovation and R&D activities). In preparation of the interviews, the firms' major owners (and therefore usually the managing founders) are identified as contact persons for the interviews in the data of the Mannheim Enterprise Panel.
At the beginning of each interview, interviewers confirm that the respondent is a founder actively working in the company, asking to be redirected should this not be the case. Once a new venture participates in the survey it is subsequently followed for up to seven years. The sample of the IAB/ZEW Start-up Panel is stratified by industry 1  and year of foundation. 2  We control for stratification by including dummy variables for the stratification cells in all regressions and by using sampling weights in (unconditional) graphs, i. e. , when we do not include control variables. We refer to  Fryges et al. (2010)  for a detailed description of the sampling design of the panel dataset, and to  Vaznyte and Andries (2019)  for a recent application of the survey data that includes a discussion of the survey's sample response, which they rate as sufficiently good.
3 To exclude the possibility that team composition--including members of different ages--drives our results, we excluded all startups founded by teams from our main analyses. We include data on team founders in the online appendix for robustness-check purposes; and we find that our results remain consistent. The survey data are enriched by additional, individual-level data on the founder(s) and employees drawn from employment statistics provided by the German Federal Employment Agency, which contain details on individuals' characteristics, such as age, gender, and work history (wages, qualification levels, start and end dates of employment in a given firm, professions, and occupational status in terms of full or part-time employment). In Germany it is mandatory for establishments that employ individuals who are subject to social security contributions to report these data on their employees.
Information on all spells of both employment and unemployment is collected by the federal employment agency and merged into an employment biography that allows an individual to be tracked on a day-by-day level. The founders' employment biographies are matched to firm-level data via the founders' names and dates of birth. The employees' employment biographies are matched via their employing establishments' names and addresses. 4   Through the combination of firm-level and individual-level data sources, our data allow us to link information on founder characteristics, founder motives and entrepreneurial orientation, firm therefore an additional stratification criterion of the sample. As for the other stratification criteria, we control for this stratification in any statistical analyses. 3 Detailed information on response rates of the individual survey waves of the panel dataset, including the reasons for non-response, are provided in technical reports to each wave on www.
gruendungspanel. de. 4 We were able to match labor market histories of about 80% of the founders from the IAB/ZEW Start-up Panel. Since we do not expect all founders to have been previously employed in reportable employment, i. e. , employment subject to social insurance in Germany (because, for example, they have always been selfemployed or have immigrated), we consider this to be a very high ratio of matched individuals. In addition, we were able to match establishments to about 90% of those firms that reported any employees subject to social insurance contributions in one of the interviews via a text-search algorithm. To exclude the possibility that selection effects induced by the data-matching affect our results, we double-checked our results with plain survey data where possible (see section on robustness checks and the online appendix).
3. Most prominently, a potential caveat concerning our innovation measures is that they are self-reported and, therefore, might be subject to a degree of social desirability bias. If such a bias is more pronounced for older than for young individuals, this might confound our results. To address this problem, we use sales arising from 'new to the market' innovations from previous years as an additional indicator for checking the robustness of capturing innovation quality based on founders' subjective assessments. The main independent variable in our models is (a), the age of the founder, measured in years. We use age in the year of innovation as a measure; and we include repeated innovations of entrepreneurs (available due to the panel dimension of our dataset) in order to include all information available and correlate it with the founder age that lies closest to the innovative activity.
Our results remain fully robust if we exclude unmatched firms instead of imputing values. To minimize the need to impute wage data for employees, unlike in the case for founders, we also consider parttime working spells for employees. For comparability of wages across employees, and in order to account for a lack of information on the exact hours worked, we multiply daily wages during part-time employment by two, and daily wages during minor employment ('mini-jobs') by five. In no case do we impute any information on the founders themselves. 8 The panel is unbalanced as firms might cease to exist or, when firms continue to exist, due to non-response and an end to their participation in the survey. We perform consistency checks to test for the possibility that older founders are more likely to participate in the survey for longer and, therefore, might be more likely to report certain types of innovations in the Results section.
For instance, do factors of health, specific skills, particular and idiosyncratic life-events, or social circumstances and groupings--both in the private domain as well as in business--support the development of entrepreneurial innovations at different stages of an entrepreneur's career? How do older, and younger, entrepreneurs cope with potential social discrimination  (Kibler et al. , 2015)  and develop an age-based self-image  (Kautonen et al. , 2015)  that supports achieving those innovation goals which are significant to them  (Brieger et al. , 2021 )? Our study built on a panel dataset that predominantly consisted of male entrepreneurs; it follows that further research could analyze how different generations of people--and in particular women, who will come to be increasingly prevalent in an aging workforce--relate to the production of different types of innovations over the courses of their lives  (Strohmeyer et al.
Female sample In Niger, PMA used a multi-stage stratified cluster design with probability proportional to size sampling of 103 geographic areas to obtain a nationally representative sample of 3,515 households (98. 8% response rate) including 3,633 females aged 15-49 (95. 4% response rate). Geographic and household selection occurred between December 2020 and April 2021. For this analysis we used Phase 2 female data that were collected between January and May of 2022. The sample followed households and women from the first round of data collection as well as a random sample of replacement households in geographic clusters with more than 10% loss to follow-up. The final Phase 2 sample included 3,696 females ages 15-49 (96. 3% response rate). Surveys were administered face-to-face with participating women in French or local language, and responses were recorded on smartphones via the ODK platform.
Analysis We first examined the distribution of facilities in Niger by facility type and region. We then estimated the proportion of facilities providing each of the signal functions, overall and by facility type. Next, we estimated the proportion of facilities with all basic and comprehensive PAC signal functions overall and by facility type and region. Lastly, we generated a readiness score for basic and comprehensive PAC for each facility, which we calculated as the percentage (out of 100%) of signal functions available; for basic PAC this was out of five signal functions and for comprehensive PAC this was out of 10 signal functions. We averaged the scores to produce readiness scores overall and by facility type and region. To examine disparities in the geographic accessibility of PAC services as measured by proximity, we linked facility data to the representative sample of women aged 15-49 using geospatial information.
We estimated the proportion of women living within 5 km of a facility that provided any PAC, a facility with all basic PAC signal functions, and a facility with all comprehensive PAC signal functions, overall and by women's sociodemogrpahic characteristics. We then ran bivariable and multivariable logistic regressions to examine the association between sociodemographic characteristics and within-5 kmaccess to any, basic and comprehensive PAC. All analyses were conducted in Stata version 16, and analyses involving female data were weighted to account for the complex survey design, including probability of selection and nonresponse, and adjusted for clustering.
Design and sample A cross-sectional was used to baseline data of SAGE  (2007) (2008) (2009) (2010). This study was an initiative from the World Health Organization (WHO) to determine the factors that impact aging in six low-and middle-income countries (LMICs): Ghana, Mexico, India, South Africa, Russia, and China. It includes a set of harmonized questionnaires from different dominions (e. g. , health, physical function, demographics, etc. ), which was applied to a representative sample of adults aged 50 or older from the participant countries. Complete description of objectives and procedures is available elsewhere  (19). For purposes of this report, only participants with complete baseline information from all the countries were included. Ethical approval. The SAGE study was approved by the World Health Organization's Ethical Review Board (reference number RPC149) and the Ethical and Protocol Review Committee of each country.
The CFS decision tree, could aid in identifying those at risk not only of increased risk of adverse health-related outcomes  (23, 38, 39) , but also of those that could be in the verge of presenting overt dementia, and benefit from early intervention for frailty that could impact cognitive health. We need to acknowledge the limitations of our work. First, as a crosssectional study, is difficult to address causality; though, longitudinal studies have shown that this might be the case. The SAGE has advantages, but there is no availability of follow-up (only for Mexico, currently), and some years have passed since the baseline data was released. Cognitive status was not assessed with the state-of-the-art evaluation, but similar approaches to testing cognition have been used in epidemiologic studies.
Most analyses of the impact of heterogeneous environmental policy stringency on the location of industrial firms have considered the relocation of entire activities -the well-known pollution haven hypothesis. Yet international enterprises may decide to only offshore a subset of their production chain -the so-called pollution offshoring hypothesis (POH). We introduce a simple empirical approach to test the POH combining a comprehensive industrial mergers and acquisitions dataset, a measure of sectoral linkages based on input-output tables and an index score of environmental policy stringency. Our results confirm the impact of relative environmental policy stringency on firms' decisions to engage in cross-country M&As. Our findings also indicate that environmental taxation have a stronger impact on international investment decisions than standards-based policies.
Mergers and acquisitions We obtain data from the Thomson-Reuters Securities Data Company (SDC) Mergers and Acquisitions dataset. This dataset reports all transactions occurring between both public and private companies since 1980. It encompasses all sectors of economic activity. In this paper, we use a subset of that extensive database, and thus observe the universe of all industrial merger and acquisition transactions taking place between the years 2000 and 2015. This dataset includes 63,596 transactions across 41 countries and encompasses both domestic and cross-border deals. For each transaction, we observe the sector of activity of the acquiring and target firms at the 4-digit level of the 1987 revision of the Standard Industrial Classification. We perform a translation of these sectoral codes to the more recent International Standard Industrial Classification (ISIC), revision 4.