dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, they described the medical evidence supporting the other devices as strong or good. The data were collected using an online survey that was incorporated into the regular ESC newsletter. The newsletter was sent to 85 568 members and affiliates of the European Society of Cardiology (ESC) with selected fields of interest (e. g. Acute Coronary Syndromes, Heart Failure, Interventional Cardiology). The factors included in the online survey were selected based on a review of the available literature. Our pre-selection was obtained from an extremely large number of potentially important drivers of the adoption and utilization of medical devices that have been identified in the literature. This pre-selection of factors was validated by a subsequent pilot study with 17 cardiologists from four European countries (Germany, Italy, England, and Slovenia).
That is, we measured the physiciansˈ perceptions of these factors. Other factors were measured using yes/no answer options, choice lists, or open fields. We incorporated the number of devices utilized as a dependent variable, which made it possible to separately analyse the factors that influence adoption and utilization decisions. The online survey was distributed as a part of the ESC newsletter to ESC members and affiliates on 26/09/ 2014. The initial invitation to participate in the survey was followed by three reminders (sent on 23/10/2014, 19/11/2014, 09/01/2015), and the data collection phase was completed on 19/01/2015. Additional environmental-level (i. e. country level) data were collected. The data for GDP and out-of-pocket payments in health systems were obtained from publicly available databases: World Bank Open Data, OECD Health Statistics, and WHO World Health Statistics.
Markov Chain Monte Carlo simulation was used for the multiple imputation procedure because no monotone missing patterns were identified via visual inspection using PROC MI (SAS Institute, Cary, NC). The literature suggests at least five imputations to produce results that appropriately reflect the uncertainty related to the imputation of missing values  (Lansisalmi et al. , 2006). Because the optimal number of imputations is still unclear, we conducted 10. This number balanced more appropriate estimates with acceptable computing times. All analyses were conducted using PROC MI and PROC MIANALYZE in SAS 9. 4 (SAS Institute, Cary, NC, USA), and Stata 13 SE (StataCorp LP, College Station, TX, USA).
Therefore, the estimates and standard errors obtained by these random effects models should not be biased. Empirical sandwich estimators of the covariance matrix were used to obtain robust standard errors. We used Gauss adaptive quadrature to estimate the models. All estimations were conducted using SAS PROC NLMIXED (SAS Institute, Cary, NC, USA). The adoption and utilization of these cardiovascular devices were analysed using the following hurdle model. Let y ij be the number of devices adopted by the i th , i = 1 , …, n i individual in the j th , j = 1, …, mj country: Pr Y ij ¼ y ij ¼ 1 À w ij À Á w ij ; y ij ¼ 0 g 1 À 1 þ αμ ij Àα À1 ; y ij ; > 0 8 > > > < > > > : where μ ij = exp (x ij β), α(≥0) is a dispersion parameter, and x ij is a vector of explanatory variables. Suppose that 0 < w ij < 1, w ij is modeled using a logit link function logit w ij À Á ¼ log w ij 1 À w ij ¼ z ij δ where z ij is a vector of explanatory variables and g ¼ g y ij ; μ ij ; α ¼ Γ α À1 þ y ij Γ α À1 ð ÞΓ y ij þ1 α À1 α À1 μ ij !1 α μ ij μ ij þ α À1 ! y ij is the likelihood function of the negative binomial function.
1. Descriptive results Across all mailings, 56 255 people clicked on the ESC newsletter. A total of 4922 of these clicked on the link to our survey, and 1773 people completed our questionnaire. The response rate based on the number of people who clicked on the ESC newsletter was 3%. The response rate based on the number of people who clicked on the link to our survey was 36%. After data cleansing, our final data set consisted of 1249 observations. Descriptive statistics for all of the included variables are presented in Tables  I  and II. Overall, the observations were clustered into 89 different countries, including all of the EU28 countries. Our sample included 81 countries (91%) which are included in the ESC members and affiliates lists. As expected, approximately 61% of physicians in the sample were chiefs of cardiology departments and senior physicians.
DISCUSSION This study represents a comprehensive analysis of factors driving the adoption and utilization of cardiovascular medical devices. We investigated drivers at four different levels (i. e. environmental, organizational, individual and technological) and identified patterns across devices, although the results of the random effects hurdle models were multifaceted. Our data were predominantly based on responses from chief and senior physicians but also included responses from attending physicians and physicians (39%). Readers might wonder how adoption decisions can be influenced by attending physicians and physicians. Although it is probably true that chief and senior physicians have a higher formal impact on the adoption decision, lower ranking physicians do influence adoption priority setting processes  (Barasa et al.
We recommend using the latter number because physicians who have not noticed the link to our survey in the ESC newsletter do not represent potential participants. Nevertheless, a low response rate might be a source of non-response bias. Therefore, we conducted visual inspection to compare our data to the ESC data. We compared the numbers of observations from the different countries in our data set to the numbers of members in the ESC member and affiliate societies as published on the ESC website. Although our data set contained more responses from some ESC member societies (e. g. Italy, Spain) and fewer responses from ESC affiliate societies (e. g. Japan, Brazil, Chile, Argentina), the overall differences between both data sets were small which lowers the probability of a substantial non-response bias.
Aims. -The coadministration of alirocumab, a PCSK9 inhibitor for treatment of hypercholesterolaemia, and insulin in diabetes mellitus (DM) requires further study. Described here is the rationale behind a phase-IIIb study designed to characterize the efficacy and safety of alirocumab in insulin-treated patients with type 1 (T1) or type 2 (T2) DM with hypercholesterolaemia and high cardiovascular (CV) risk. Methods. -ODYSSEY DM-INSULIN (NCT02585778) is a randomized, double-blind, placebo-controlled, multicentre study that planned to enrol around 400 T2 and up to 100 T1 insulin-treated DM patients. Participants had low-density lipoprotein cholesterol (LDL-C) levels at screening 70 mg/dL (1. 81 mmol/ L) with stable maximum tolerated statin therapy or were statin-intolerant, and taking (or not) other lipid-lowering therapy; they also had established CV disease or at least one additional CV risk factor.
Indeed, the ongoing ODYSSEY OUTCOMES study (NCT01663402) is evaluating the effect of alirocumab on major CV events in 18,000 patients 4-52 weeks post-acute coronary syndrome, including a significant number with DM  [20]. There is conflicting evidence regarding the possible association of PCSK9 with alterations of glucose homoeostasis. The CODAM study  [21]  found that plasma levels of PCSK9 do not differ between those with recently diagnosed T2DM, normal glucose metabolism and impaired glucose metabolism. In contrast, the ILLUMINATE study  [22]  found that plasma PCSK9 levels were elevated in patients with DM compared with those without DM, with a significant association between PCSK9 levels and LDL-C, TGs, glucose, insulin and homoeostasis model assessment of insulin resistance (HOMA-IR) scores.
ODYSSEY DM-INSULIN (ClinicalTrials. gov identifier: NCT02585778) is a phase-IIIb randomized double-blind, placebo-controlled, parallel-group multicentre trial being conducted in Europe and the US. It is evaluating the efficacy and safety of alirocumab in insulin-treated DM patients at high CV risk with hypercholesterolaemia not adequately controlled with the maximum tolerated LLT (Fig. 1 ). The study planned for a population of approximately 500 subjects, comprising 400 with T2DM and up to 100 with T1DM. Randomization began in November 2015 and ended in August 2016. The study is being performed in accordance with the ethical principles outlined at the 18th World Medical Assembly (WMA) in Helsinki  (1964) , and all the relevant amendments laid down by the WMA and International Conference on Harmonization guidelines for Good Clinical Practice (GCP).
Primary analyses The primary efficacy analysis population is the ITT population, defined as all of the randomized participants with a calculated LDL-C value at baseline and at least one value within one of the analysis windows up to week 24, regardless of treatment adherence. Separate analyses will be performed for patients with T1DM and T2DM, with an overall analysis of all participants for some efficacy endpoints. The percentage change in calculated LDL-C from baseline to week 24 will be analyzed using a mixed-effect model with repeated measures (MMRM) approach to account for any missing data, using all available post-baseline data within the analysis windows (weeks  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]. The model will also include fixed categorical effects of treatment group, time point and treatment-by-time interaction, as well as the continuous fixed covariates of baseline LDL-C value and baseline value-by-time point interaction.
In addition to 440 insulin-treated participants with T2DM, the study has recruited 77 patients with T1DM, whose inclusion is important as randomized controlled data on LLT in such a population are limited. Moreover, specific evaluations of safety in those with T1DM and T2DM are important in the context of concomitant insulin injections with a monoclonal antibody. A pooled analysis of data from 3499 participants in five placebocontrolled phase-III trials of alirocumab (COMBO I, FH I and II, HIGH FH and LONG TERM) indicated that TEAEs arise overall at a similar frequency to alirocumab in those with or without DM  [26] , but only a relatively small proportion of these patients were taking concomitant insulin. In analyses of both pooled data and individual studies, fewer injection-site reactions were reported in patients with than without DM.
Acknowledgments The authors are grateful to the study investigators and committee members listed in Appendix A. Medical writing support was provided by  Melanie Jones of Prime (Knutsford, Cheshire, UK) , supported by  Sanofi  and  Regeneron Pharmaceuticals, Inc. The following people from the study sponsors reviewed and provided editorial comments on the manuscript:  Robert Pordy ,  Robert J. Sanchez  and  William J. Sasiela  (all Regeneron). The authors were responsible for all content and editorial decisions, and received no honoraria related to the development/presentation of this publication. The design and conduct of the study, as well as analyses of the study data and opinions, conclusions and interpretation of the data, are the responsibility of the authors.
Data The two main variables of interest in this paper are output and prices in the US over the period of 1800-2014, i. e. , 215 observations. Output is measured by the real gross domestic product (GDP) at constant 2009 prices, and prices by the consumer price index (CPI). The series have been obtained from the Global financial Database and the website of Professor Robert Sahr (http://liberalarts. oregonstate. edu/spp/polisci/research/inflationconversion-factors-convert-dollars-1774-estimated-2024-dollars-recent-year), respectively. As the DCC analysis to be valid has to be performed on stationary variables, we convert the real GDP series to real GDP growth and CPI series to inflation by taking the first difference of the natural logarithm of each series. 1 1 However, as discussed below, different transformation of these series does not affect our main results.
We also control for (1) the short-tem interest rates, obtained over the period of 1800-1870 from  Homer and Sylla (2005) , and beyond that from the data segment of the website of Professor Robert J. Shiller (http://www. econ. yale. edu/∼shiller/data. htm), and (2) output growth uncertainty and inflation uncertainty as exogenous variables in the conditional mean equations of output growth and inflation, respectively. Interest rates are accounted for so as to capture any potential impact of US monetary policy on the output growth-inflation relationship. Output growth uncertainty, which is proxied by the conditional volatility of output growth, is included in the conditional mean equation of output growth. The motivation for this is based on the vast, albeit ambiguous, theoretical literature on the relationship between economic growth and its volatility.
We will assess feasibility and acceptability by: 1) collecting data about the instruments prior to collection; 2) interviewing screeners after data collection; 3) conducting a pilot survey with a 10% sample of participants. Ethics and dissemination The Torrens University Higher Research Ethics Committee has approved this study. We will disseminate findings via publication in peer-reviewed journals and presentation at relevant conferences. IntroductIon Background Population ageing is proceeding at an unprecedented pace throughout the world,  1  with frailty among its most challenging manifestations. 2 3 Frailty is associated with decreased quality of life,  [4] [5] [6]  disability,  4 5 7 8  increased healthcare utilisation,  4 5 7 9  falls,  9 10  institutionalisation  9 11  and death. 7 9 12 Despite the importance of frailty, there is currently no consensus regarding its prevalence.
We will assess the feasibility and acceptability of the index tests from three perspectives: 1) feasibility of implementation within the context of a busy general practice environment; 2) acceptability to health service providers; 3) acceptability to consumers. Within health research, passive case-finding (eg, using medical records) has been promoted as potentially advantageous over active case-finding due to lower cost and burden on study participants. 28 It therefore could be viewed as a viable alternative to the active and rather intensive screening approach adopted in our study. However, passive case-finding is unviable within this context given that routinely collected medical records would be unlikely to capture the range of data required to detect frailty (with the possible exception of the construction of a frailty index) and are subject to high variability, a globally observed finding.
Other measures In addition to the index tests collected at the screening appointment, the practice nurse will also collect the Nottingham Extended ADL scale (NEADL). 67 The NEADL is a valid and reliable measure of functioning that has been widely applied among older populations. 68 ta management We will collect data manually, verifying it where required against clinical records. We will identify participants by a unique identifier, with linkage keys to be stored separately from the data. Data will be entered into an SPSS database. We will subject 5% of the records to a random audit by a second researcher to test data quality. We will store all deidentified study data on a password-protected drive.
We will determine the extent of agreement between the index tests and reference standards by calculating Cohen's kappa. To analyse the relationship between the psychosocial variables and frailty, we will define the presence of frailty as a binary variable (frail/not frail) according to the Fried criteria as measured in the Frailty Phenotype. We will use binomial (binary) logistic regression to analyse the association strength (OR) for the frail state with the psychosocial variables using the non-frail state as the comparison category. We will consider a p value of <0. 05 to be statistically significant. The data will be analysed using the latest available version of the SPSS statistical software (version 24. 0 SPSS, Chicago, Illinois, USA). To test inter-rater reliability, 8 subjects tested by each screener (32 in all) will be asked to repeat the index test session including self-reported tests with a second blinded screener within 48 hours after the initial rating.
123). 71 Mixed methods have increasingly been applied within the health sciences in recent years, where a more complete understanding of an issue is allowed than by a quantitative or qualitative approach alone. 72 ta collection and management Feasibility We will measure the feasibility of the index tests by collecting the following information about each test: Acceptability to health service providers We will ask the screeners to complete a standardised form to capture initial impressions of the instruments during the data collection period. We will also request that screeners participate in an interview conducted within Open Access a week after data collection has concluded to gather their overall impressions of the instruments. We will rate each instrument against a 1-10 Likert scale measuring ease of implementation as well as including a number of open-ended questions.
Acceptability to consumers We will pilot an acceptability questionnaire with 26 participants during data collection (13 to be recruited from each research site, respectively). This figure represents just over a 10% sample of participants, as well as exceeding the n=25 threshold recommended by Herzog in her discussion of appropriate sample sizes for aims related to instrumentation. 73 We will ask every second participant participating at each research site in the main study to participate in the acceptability component. Sampling will proceed until the site quota is reached. After applying each index test the screener will ask participants their impressions of the instrument and record the response. We will also ask participants to complete a 1-10 Likert-scale questionnaire measuring perceived ease in completing each instrument.
data analysis Feasibility We will use descriptive statistics and tables to describe the results of the feasibility analysis, structuring these according to themes drawn out in our early focus groups. Acceptability to health service providers Two researchers will employ a thematic analysis approach to code the data contained within the screener transcripts. We will use these codes to develop acceptability categories and themes. Codes and themes will be reviewed with a third researcher experienced in qualitative methodology to promote rigour. We will create joint displays (ie, side-by-side comparison tables) of ranking results for the screening instruments and qualitative comments about screener impressions of the instruments to facilitate data integration.
(for model equations, see Supplementary Material). Model simulations for this article were run using the SimBiology toolbox of MATLAB (rel. 2016a, MathWorks, Inc. , Natick, MA, USA). The QSP model is an ODE model which simulates the behavior of lipoproteins, regulatory proteins, cholesterol, LDLR, plaque lipid and cellular components, and drugs localized in 5 main generalized areas within the body. These areas are represented as lumped compartments in the model; protein and cell levels are tracked separately for each compartment but are assumed to be well-mixed within each compartment. Compartments include plasma, gastrointestinal (GI) tract, hepatocytes, peripheral tissue, and plaque. The plasma and the GI tract are general representations, whereas hepatic and peripheral tissue cells contain subcompartments to describe intracellular, nuclear, and endosomal localizations within the cells.
Calibration of baseline model parameterization and virtual patients to represent therapeutic responses of interest The QSP platform was developed and calibrated in JDesigner (Systems Biology Workbench, Inc. , Claremont, CA, USA )  76  using published data in accordance with Rosa's Model Qualification Method. 16, 65 The base parameterization of the model, VP0, is representative of a statin-naïve patient with non-FH as described in the phase 3 study published by Roth et al. 7  Initial parameter values were obtained from animal and clinical studies in the literature (see Supplementary Table  1  for full list of parameter descriptions and references/guidelines used for initial estimates). Many values were taken from an existing model of cholesterol metabolism which does not include PCSK9. 11 Known biological constraints were also used to guide the baseline parameterization, such as the relative affinity of LDL-C and PCSK9 for LDLR in neutral (at the cell surface) vs acidic (endosomal) pH, the approximate half-life of LDLR, and the synthesis rate and pool size of PCSK9 in plasma.
Use and perception of the Gorla Maggiore park The first part of the questionnaire has been dedicated to collect data related to the way the Gorla Maggiore park is used and perceived by the respondents. On average, each respondent has visited the park a little bit more than 25 times over the last 12 months (SD is 31. 89). In our sample, the annual number of visits varies from 0 (for 5 respondents) to more than twice a week (for 7 respondents). The average typically size of the group when the respondent goes to the park is 2. 43 (SD is 1. 27), varying from 1 (for 14 respondents) to more than 5 (for 4 respondents). Respondents typically live in the proximity of the park. The average distance to the park is 1. 38 km (SD is 0. 74). For 27 respondents the distance to the park is less than 1 km. Next, each respondent has been proposed a list of 8 possible recreational activities, see Table  2.
Building on this theoretical perspective, the study advances three hypotheses that operationalize the theory. The hypotheses are tested using data from 277 firms that searched for equity crowdfunding between 2012 and 2015 on Crowdcube-a leading UK equity crowdfunding platform-and two matched samples of firms that did not list on crowdfunding platforms but were similar in terms of firm industry, age and size. A first matched sample is based on a random set of firms (that do not necessarily have a need for external finance); a second matched sample is based on a set of firms that raised debt (and thus had a demand for external finance). We find general support for our hypotheses using both samples. Specifically, firms that are more unprofitable and thus lack internal funds are more likely to search for equity crowdfunding.
, 2009). The pecking order theory suggests that because of information asymmetries, entrepreneurs prefer to use internal financing whenever possible; next they will raise external debt financing; and, finally, when their debt capacity is exhausted, they will raise external equity financing, but only as a last resort. Thus, firms are more likely to search for equity crowdfunding, a source of external equity financing, when they lack internal funds and additional debt capacity. To test these claims, we use data on 277 UK firms that are listed on Crowdcube-a major UK equity crowdfunding platform-and 277 matched firms that were not listed on any equity crowdfunding platform. 1 The findings provide support for our claims. This study contributes to the crowdfunding literature. Existing crowdfunding studies focus on the factors that explain the funding success of firms that launched an equity crowdfunding campaign, rather than exploring why some firms search for equity crowdfunding and others do not.