dataesr/scientific-paragraphs-categorization huggingface dataset:
Background and Objectives: Randomized controlled trials (RCTs) are criticized for including patients who are overselected. Health authorities consequently encourage ''real-world'' postmarketing cohort studies. Our objective was to determine the differences between RCTs and observational studies as regards their populations and efficacy/safety results. Methods: A systematic review was conducted to identify RCTs and observational studies including patients with venous thromboembolism receiving direct oral anticoagulants or conventional treatment. Ratios of hazard ratio (RHR) comparing epidemiological studies (prospective and retrospective cohort studies and studies using living databases) with RCTs were computed. Results: Six RCTs (27,121 patients) and twenty observational studies (248,971 patients) were identified and analyzed.
Prospective cohort studies seemed to recruit patients who were no less selected than those of RCTs whereas other types of observational studies may reflect the population treated in real life. Among observational studies, prospective cohort studies yielded the most favorable estimates of treatment effect compared with RCTs. These studies were associated with a nonsignificant 33% increase in efficacy estimate (RHR 0. 67, [95% CI, 0. 39e1. 18]) but no effect on safety estimate. Studies using living databases were associated with nonsignificant trends toward a greater effect on efficacy (RHR 0. 82, [0. 66e1. 01]) and a smaller effect on safety (RHR 1. 33, [0. 96e1. 84]). Discussion: Overall, in this clinical setting, an exaggeration of the treatment efficacy estimate was seen with observational studies compared with RCTs.
Yet this incentive is flawed. First, there is no recommendation to favor one study design over another (prospective rather than retrospective, specific study or use of living databases). Second, as such studies are not randomized, they are subject to confounding, and there is no recommendation to try to address the issue of selection bias and confounders. The statistical analysis of these observational studies may incorporate a correction mechanism based on adjustment or matching, generally using the propensity score (PS), to allow a comparable distribution of measured baseline variables between treated and untreated subjects. However, such methods can only adjust for confounding factors that have actually been measured  [6]. The problem is to determine the observational study design that can best capture all the known confounding factors.
Literature search We performed a systematic review of published RCTs and observational studies comparing DOACs to vitamin-K antagonists (VKAs) for the treatment of VTE with regard to recurrent VTE and major bleeding. An exhaustive literature search, both manual and computer-assisted, was performed from January 1, 2009 (first DOAC studied) to August 30, 2020. The computer-assisted search was carried out in various electronic databases (PubMed, Cochrane Database, and Google Scholar). We constructed a search equation, including a panel of relevant keywords to identify articles in PubMed (Appendix 2). The search was not restricted to articles published in English.
Study selection Two of the authors (MS and SL) independently evaluated studies for potential inclusion, disagreements being resolved by discussion. To be included in the metaanalysis, studies had to meet all the following criteria: (1) pivotal phase III RCTs, as well as observational studies, (2) comparing DOACs vs. VKA administered for at least 3 months for the initial treatment of VTE. The comparisons were limited to DOACs approved or under regulatory review for the treatment of VTE, namely direct thrombin inhibitors (dabigatran) and factor Xa inhibitors (rivaroxaban, apixaban, and edoxaban) vs. oral warfarin or VKA as the comparator. Three subcategories of observational studies were defined a priori in the internal protocol: (1) prospective cohort studies comprising observational studies with prospective recruitment and construction of a specific database for the study, (2) studies using living databases, comprising observational studies with prospective recruitment included in a living clinical database (i.
e. , a registry) or a living health administrative database, and (3) retrospective cohort studies with retrospective recruitment. Studies conducted on the same database were considered duplicates if more than half the recruitment period overlapped with that of another study with a larger enrollment and evaluating the same treatment groups in the same patients. We reviewed every study, and when duplicates were identified, we chose the study with the largest sample size. Studies with objectives not focusing on the effectiveness or safety profiles of anticoagulants, as well as protocols and reviews that did not provide estimates of treatment effects in terms of efficacy or safety profiles, were excluded.
Data extraction Data were independently extracted by two of the authors (MS and SL). In the event of discrepancies between the reviewers, a consensus was reached by discussion. For each study, we extracted details of the study characteristics (year of publication, names of authors, the country where the study was conducted, number of patients in each group), study design, treatment information (type and dose), patient characteristics (mean age, gender, renal insufficiency, history of VTE, cancer) and outcome measures. We extracted treatment effect estimates for DOACs vs. VKA, such as the hazard ratio (HR) or relative risk (RR) for VTE recurrence and major bleeding. Among observational studies, we differentiated prospective cohort studies (conducted specifically to investigate the defined treatments in the defined indication), retrospective cohort studies involving a retrospective collection of available data in patient medical records, and studies using health administrative databases or healthcare registries with the prospective recording of patient information.
Statistical analyses First, using descriptive statistics, we compared the populations enrolled in RCTs and observational studies, including the assessment of DOACs in the treatment of VTE. The characteristics of the patients enrolled in these studies were compared before and after the application of the PS. Categorical variables extracted as percentages, as well as continuous variables extracted as means, were summarized as an unweighted median for each study design. Second, to compare the treatment effect of DOACs vs. VKA between RTCs and observational studies (prospective cohort studies, studies using health administrative databases, or retrospective cohort studies), we calculated the pooled HR and its 95% confidence interval (CI) for VTE recurrence and major bleeding for each type of study.
Study Identification The search procedure identified 413 potentially eligible references, of which 366 were excluded after review of their titles and abstracts (Appendix 3). After the assessment of the full texts, a further 17 references were excluded. Finally, 30 references were considered eligible for inclusion in the meta-analysis. These studies included two duplicate entries in a Danish database with a large overlap of the recruitment period  [18, 19]. As the second publication concerned a subgroup of 5,004 patients with unprovoked venous thromboembolism within the study reported in the first publication (including 12,318 patients), only this larger study was retained  [18]. Five studies conducted on the same American database (US Truven Market scan) were found [20e24]. Three studies [20e22] were excluded because they evaluated the same patients as two other larger studies  [23, 24] , and more than half of their enrollment periods overlapped with those of the other studies.
Finally, 26 studies (totalling 276,092 patients) were included: 6 RCTs [25e30], 4 prospective cohort studies [31e34], 12 studies using living databases (2 using living clinical databases  [35, 36]  and 10 using living health administrative databases  [18,23,24,37e43] ), and 4 retrospective cohort studies [44e47].
Characteristics of patients treated with DOACs The characteristics of DOAC-treated patients by type of study are described in Table  1. The DOAC-treated patients in prospective cohort studies were similar to those included in RCTs regarding disease severity, the median age being 58 and 56 years, respectively (Table  1 ). In contrast, compared to the patients enrolled in RCTs, the DOACtreated patients included in retrospective cohort studies or studies using living health administrative databases were 7 years older in terms of median age at baseline and presented more comorbidities.
Comparison of treatment effect estimates: studies using living databases vs. RCTs In studies using living databases, treatment with DOACs was associated with a 27% decrease in the efficacy endpoint (HR 0. 73, [95% CI, 0. 63 to 0. 83]) and a 19% decrease in the safety endpoint (HR 0. 81, [95% CI, 0. 72 to 0. 90]) (Figs. 2  and 3 ). Compared to RCTs, studies using living databases showed a nonsignificant 18% increment in estimated treatment effect with respect to the efficacy endpoint (RHR 0. 82, [95% CI, 0. 66 to 1. 01]) (Fig. 3 ); the magnitude of this increment was similar after the exclusion of studies that did not use any method of adjustment for confounders  [35]  (RHR 0. 82, [95% CI, 0. 67 to 1. 01]). This trend was reversed as regards the safety endpoint (RHR 1. 33, [95% CI, 0. 96 to 1.
Discussion In this metaepidemiological study, although the patients included in prospective cohort studies were close to those included in RCTs in the defined indication, these studies overestimated the magnitude of the treatment effect for both efficacy and safety endpoints compared to RCTs. In contrast, the patients included in retrospective studies and studies using living databases were closer to the real-life setting, with patients about 10 years older than those included in RCTs. However, we also observed a nonsignificant exaggeration of the efficacy estimate but an underestimation of the safety estimate in these studies. To the best of our knowledge, this is the first metaepidemiological study to differentiate subcategories of observational studies based on their design in terms of recruitment and data collection, the aim being to test the consistency of the results.
In contrast, studies using living databases have the advantage of being exhaustive but do not allow flexibility with regard to the variables analyzed, as this is necessarily constrained by the range of variables already collected in the database. In such studies, a lesser selection of the patient population is observed, as already shown in other clinical settings. Indeed, previous metaepidemiological analyses comparing RCTs and observational studies also found systematic differences in the baseline characteristics of the patients included, similarly showing that the patients included in RCTs were younger and presented a shorter disease duration compared to those enrolled in observational studies  [48]. Finally, retrospective cohort studies permit neither exhaustivity nor the choice of confounding factors to be measured and should not be considered as having an acceptable design for pharmacoepidemiological purposes.
First, controlling for confounding (using adjustment or matching) might, for the most part, be based on variables that are primarily prognostic factors for efficacy. Lack of data on specific variables related to bleeding risk is an issue in these studies, so calculating a Fig. 3. Quantification of the differences in the estimated treatment effect between observational studies and RCTs based on the ratio of hazard ratios (RHR). HAD, health administrative databases; 95% CI, 95% confidence interval; RCTs, randomized controlled trials; RHR, ratio of Hazard Ratios; VTE, venous thromboembolism; DOACs, direct oral anticoagulants; VKA, vitamin-K antagonists. * An RHR !1 would imply that the estimates of treatment effect in observational studies were higher compared to RCTs, whereas an RHR O1 would imply that the estimates of treatment effect were lower.
Data compilation and review For the trade domain, initial data collection and review was undertaken based primarily on the Chatham House's Resource Trade dataset (Chatham House, 2020; that in turn uses UN Comtrade statistics; UN Comtrade, 2020) which provides a comprehensive overview of international trade (in physical and financial units). The EU27 was chosen as the focal importing bloc. In order to consider dependency on overseas versus domestic production, we supplemented the trade statistics with production statistics (from FAO and Eurostat) for products produced within the EU27. Other sources of information included data on specific logistics types (from Eurostat), and the Chatham House 'chokepoints' report  (Bailey and Wellesley, 2017). Data analysis for the human security, development and foreign policy domain includes assessment of data that summarises the asylum dynamics and migration into, and overseas development assistance (ODA) spending by, the EU27.
Sources for this data include the United Nations and OECD. Analysis of financial exposure is based on a number of sources, including the International Monetary Fund (IMF) which provides data (in $, USD) for individual EU countries on outward foreign direct investment (FDI) and cross-border portfolio investments. Additional data on industry-breakdowns is provided by the Eurostat database (available only for the EU28 rather than EU27, and provided in euros, EUR) and contains classifications for selected countries only: Brazil, Canada, China, Hong Kong, India, Japan, Russia, Switzerland and the United States). No equivalent open-access sectoral classifications are available for portfolio investments. In contrast to the literature review, our data exploration focuses primarily on EU-linked connections.
Acknowledgements This work was conducted in the European Commission H2020-funded CASCADES (CAScading Climate risks: towards ADaptive and resilient European Societies) project, number  821010. We thank  Francesco Bosello  for early informal review of material contained within this manuscript, and  Lirong Liu  for assistance with initial data collection. We are grateful to the  ECCA-organisational Team  and the guest editors for organising this Special Issue.
In any case, costs arising from plant production stop should transparently be taken into account in estimating the costs of CO 2 capture. The economic impact of production stops for retrofit is exemplified below for three different industrial plants: a cement plant  (Gardarsdottir et al. , 2019; Voldsund et al. , 2019) , a pulp mill  (IEAGHG, 2016) , and an a adt and hrc stand for air-dried ton and hot-rolled coil, respectively. b Costs converted from $2010 to €2014 using the Chemical Engineering Cost Plant Index (CEPCI) and average currency exchange rates. integrated steel mill  (IEAGHG, 2013b). Key data for these industrial plants is listed in Table  3. The added cost of CO 2 avoided due to a production stop for CO 2 capture retrofit for the three industrial plants are exemplified in Fig.
Patients and Treatments This report is a post hoc analysis of the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) (clinicaltrials. gov, NCT01663402)  (6, 19) , which compared the effects of alirocumab or placebo in 18,924 patients with recent acute coronary syndrome and persistent dyslipidemia despite intensive or maximum tolerated statin treatment. The protocol was approved by the institutional review board at each site, and all patients provided informed consent. Qualifying patients were hospitalized for acute myocardial infarction or unstable angina 12 12 months prior to randomization and had LDL-C $70 mg/dL, non-HDL cholesterol $100 mg/dL, or apolipoprotein B $80 mg/dL despite treatment with atorvastatin 40280 mg daily, rosuvastatin 20240 mg daily, or the highest tolerated dose of one of these statins.
, 2010). We run the model long-term to give an idea of possible trends, but this does not necessarily infer an equilibrium or give quantitatively realistic results. The ODE model was run through MATLAB using odesolver 113. Parameter values, initial conditions and a range of scenario parameters are given in the appendix. The restoration scenario involves the active conversion of degraded or unused land (U j ) back into natural land (N j ) by individuals in the local region (P j , includes both subpopulations within the region). There is a minimal natural conversion and a minimal anthropogenic conversion, however this scenario also looks at restoring natural land at 50 to 100 times the natural rate of restoration. By contrast, conservation is used to describe natural land (N j ) being set aside -taking N j and maintaining it in the conserved state (C j ), such that individuals and land cover are provided with non-provisioning services, but the land is not available for harvest or manipulation.
We used historical data to calibrate the higher and lower income technology variables and differential equations. Technology growth rate up until present should lie between economic growth rate, which has grown at the same pace  (Motesharrei et al. , 2016)  and technological growth, measured by advances in computer processing and inventions, which is 10 times faster than human population growth. In the model, technology is a major driver of population dynamics via resource accessibility. It is estimated that higher income regions are more developed than lower income regions, in terms of education, medicine, machinery, etc. (The World Bank, 2019a). Therefore, we include two technology variables, one for each region (T j , j = L, H ) with different growth rates (t j ). The technology growth curve is a function of population size (i.
This comprehensive review was conducted by searching PubMed database and Google scholar. We included English articles and official documents (e. g. , guidelines, white papers, and reports) of regulators, HTA bodies, or other government organizations that were published from 2012 onward, because this was the year with the first ATMP, Glybera R , being approved in the EU  (2). The combinations of following keywords were searched: (1) terms for products of interest: "ATMPs, " "cell therapy, " "gene therapy, " or "regenerative medicine, " (2) terms for activities of market access: "research and development, " "clinical trials, " "market authorization, " "regulation, " or "HTA, " and (3) terms for collaboration and harmonization: "partnership, " "involvement, " "engagement, " "cooperation, " "parallel consultation, " "joint activities, " or "cross borders.
While the adequate supports of a suitable screening platform for biomarkers (e. g. , alterations in gene or molecule) will improve efficiency and expedite the clinical developments  (27). Currently, the master protocol is still in infancy, there are many unanswered questions and challenges to address, so as to the feasibility of implementation in the ATMPs filed will be explored further  (27). For example, as shown in the clinicaltrial. gov, no umbrella trials were identified for ATMPs, and only one basket trial (NCT02509507) for ATMPs, that investigates talimogene laherparepvec (Imlygic TM ) with a combination of pembrolizumab in several forms of hepatocellular carcinoma or non-hepatocellular carcinoma, is currently underway. Without doubts, the implementation of such innovative clinical trials strategy must be driven by the tight coordination between patient groups, pharmaceutical companies, and experts in genetics, translational science, and clinical research  (28).
This will possibly trigger the "treatment tourism" of patients to foreign countries for receiving ATMPs that are not authorized in their own countries  (38). However, the long-distancing travel will exhaust already vulnerable patients, in addition to the potential safety risks and high expenses of unauthorized ATMPs treatments. Therefore, it could be meaningful to establish international coordination on ATMPs regulation to uniform the terminology, to standardize the criteria for ATMPs that are exempted from market authorization, and to clarify the minimal requirements of the dataset needed for the initiation of clinical trials and market authorization  (74). Although one-size-fits-all regulation is unrealistic, it could at least help minimize the ambiguity and avoid conflicts in the regulatory requirements.
D. Anderson Cancer Anakinra IL-1 inhibitor National Cancer Institute Reduction of CAR-T toxicity Safety NCT04432506 Center (NCI) in R/R large B-cell lymphoma Yescarta ® ; Incyte Corporation Itacitinib JAK1 inhibitor Several cancer centers and Prevention of cytokine Safety NCT04071366 Kymriah ® universities release syndrome Yescarta ® ; Jonsson Comprehensive Anakinra IL-1 inhibitor Not available Prevention of neurotoxicity Safety NCT04205838 Kymriah ® Cancer Center in R/R large B-cell lymphoma Kymriah ® University of Tocilizumab IL-6 inhibitor Children's Hospital of Cytokine release syndrome Safety NCT02906371 Pennsylvania Philadelphia CAR, chimeric antigen receptor; DLBCL, diffuse large B-cell lymphoma; MoA, mode of action; R/R, relapsed or refractory.
Methods: The observational study (#NCT04367337) enrolled 6064 adults residing in Australia, Canada, China, France, Gambia, Germany, Israel, Italy, Malaysia, Poland, Portugal, Romania, Singapore, and Switzerland. Data on handwashing adherence across 8 situations (indicated in the WHO guidelines) were collected via an online survey (March-July 2020). Individual-level handwashing data were matched with the date-and country-specific values of the 6 indices of the trajectory of COVID-19 pandemic, obtained from the WHO daily reports.
Study aims This study aimed to assess whether self-reported adherence to handwashing guidelines (as recommended by the WHO [1]) is associated with the trajectory of the COVID-19 pandemic as documented by the WHO  [17]  for the period prior to data collection. The following indicators of the trajectory of the COVID-19 pandemic were used: (i) total cases of COVID-19 morbidity and mortality accumulated since the onset of the pandemic, (ii) new cases (COVID-19 morbidity and mortality in the previous 14 days), and (iii) change in recent cases (COVID-19 morbidity/mortality cases recorded in the 14-28 days prior to data collection subtracted from morbidity/mortality recorded in prior 0-14 days). These indicators were assumed to be predictors of handwashing adherence, which was the outcome variable of interest.
Study design and procedure The design of the study was correlational. Data for the 6 indicators of the trajectory of the COVID-19 pandemic were extracted from Coronavirus Disease Situation Reports  [17]  and matched with self-reported participants' data using the date and country of data collection. This preregistered observational study (see Clinical Trials. Gov, #NCT04367337) was conducted in 14 countries: Australia, Canada, China, France, Gambia, Germany, Israel, Italy, Malaysia, Poland, Portugal, Romania, Singapore, and Switzerland. The countries were recruited until the following criteria were met: (i) representing at least 5 continents; (ii) representing different trajectories of the COVID-19 pandemic (e. g. , low vs high numbers of total cases during the data collection period, as reported by the WHO Coronavirus Disease Situation Reports  [17] ); (iii) at least one country with moderate to high values (.