dataesr/scientific-paragraphs-categorization huggingface dataset:
In the absence of well-designed and conducted RCTs to establish the comparative effectiveness of treatments, epidemiologists have proposed causal inference methods rooted in counterfactual theory applied to large observational databases, that can be viewed as an attempt to emulate a randomized experiment (the target trial) that would answer the question of interest  [28]. Target trial emulation is gaining traction as a two-step methodological approach, useful in situations where there is dearth of comparative evidence  [29, 30]. Whilst it has been applied in several settings already, it requires large observational datasets with rich data to provide unbiased answers to the question of interest. This latter requirement makes the approach currently prohibitive to many indications of cell and gene therapies, which are developed for rare or ultra-rare conditions.
The key question is how to collect reliable data on outcomes while still making the process manageable. Three approaches can be observed in the literature. First, data on outcomes can be obtained from clinical studies that are still ongoing at the time market access is granted. This is one of the approaches used to support the reimbursement of drugs on the Cancer Drugs Fund in the UK  [51]. Secondly, new data collection in clinical practice could be initiated by undertaking ad hoc studies or by establishing patient registries  [13]. This approach has been recently introduced in Germany, with the Law for More Safety in the Supply of Medicines (GSAV) in 2019  [52]. According to this law, routine clinical practice data collection can be required for orphan medicines, drugs with conditional approval, or those approved under exceptional circumstances.
Consideration of evidence post-launch Cell and gene therapies, and advanced therapy medicinal products in general, are often characterized by a high level of uncertainty at market launch over their (long-term) clinical and economic impact. Consequently, ad hoc post launch studies to produce realworld evidence (RWE) could be implemented, if they are needed and feasible. These studies can be linked with outcome-based managed entry agreements in the form of coverage with evidence development: initiating these agreements without an appropriate protocol and data collection may represent missed opportunities. If post-launch data collection through an ad hoc study is not feasible, a pragmatic approach to data collection is better, e. g. , the one adopted by England through Managed Access Agreements that rely on existing data collection programs managed by the industry, and existing NHS registries/administrative databases.
Acknowledgements This project was funded by an unrestricted sponsorship agreement between  F. Hoffmann-La Roche  and  Bocconi University. This support is gratefully acknowledged. We are grateful to  Thomas Kenny ,  Tina Taube ,  Entela Xoxi  and 5 other individuals representing industry, patient and payer perspectives, for expressing their views during the discussion of the results of the study, and for comments on an earlier draft of this paper. However, the authors are solely responsible for the views expressed. Preliminary results of this research we presented as a poster at the  ISPOR Annual Meeting, National Harbor (MD) ,  May 2022.
The range of ICER estimates show that Yescarta has plausible potential to be cost-effective and collecting further data on PFS, OS and IVIG use will reduce uncertainty in the evidence OS: median overall survival not been reached in pivotal trial (ZUMA-1) PFS and OS: timeline over which that the curves for progression-free survival and over- all survival may converge is not available Use of IVIG: need for IVIG in a real-world setting remains uncertain, even data from the follow-up of pivotal trial (ZUMA-1) demonstrated that IVIG was rarely used (8. 3% patients) OS -Long-term data coming from the follow-up of pivotal trial (ZUMA-1) (5-year data avail- able in Q1 2022 and confidentially reported to NICE) Timeline convergence of OS and PFS -Long-term data coming from the follow-up of pivotal trial (ZUMA-1) (5-year data avail- able in Q1 2022 and confidentially reported to NICE) IVIG -Long-term data coming from the follow-up of pivotal trial (JULIET-Final Clinical Report expected on Aug 2023) -Integrated dataset (MDSAS and NHS England's Blueteq) Most plausible ICER for Kymriah is higher than the threshold range and Kymriah does not meet the criteria to be a ELT OS: immaturity data that not fully support the curative nature of Kymriah Subsequent stem cell transplant rates Proportion of patients who would need treatment for B-cell aplasia with IVIG and duration of the treatment OS -Long-term data coming from the follow-up of pivotal trial (ELIANA-Final Clinical Report expected on Jun 2023) Stem cell transplant rates BMT Registry (stem cell transplant rates) (it is anticipated that there may be difficulties in linking these to national datasets, to identify patients treated with Kymiriah) IVIG Not specified, but the NHS England's Blueteq is mentioned Most plausible ICER for Kymriah compared to salvage chemotherapy is uncertain, but Kymriah has the plausible potential to satisfy the criteria for routine use if this uncertainty could be reduced OS: immature data that do not fully support the curative nature of Kymriah Proportion of patients who would need treatment for B-cell aplasia with IVIG and duration of the treatment OS -Long-term data coming from the follow-up of pivotal trial (JULIET-Final Clinical Report expected on Aug 2023) -Routine population-wide datasets (NHS SACT) IVIG -Long-term data coming from the follow-up of pivotal trial (JULIET-Final Clinical Report expected on Aug 2023) -Integrated dataset (MDSAS and NHS Eng- land's Blueteq) General concerns Uncertainties Data collection and sources for the post-HTA evidence.
The reference electricity price and demand data are taken from the Italian Power Exchange website 2  and from the Transparency Platform of the ENTSO-E for the Italian geographical zones and for foreign countries, respectively. We set the price elasticity equal to -0. 1 to indicate an inelastic demand in line with the literature. 3 Notice that the zonal reference demand used in these computations corresponds to the residual consumption related to gas-fired, wind and solar PV plants. In other words, zonal reference demand is netted out by the power production of all those technologies available in the zones but not included in our analysis. Similarly, the transfer capacity limits of the power flows exchanged between zones are re-calibrated starting from those provided by Terna. 4  This means that, taking as reference the real data provided by Terna, the network transfer limits between zones are reduced in such a way to be proportional to the balance between demand and power that can be generated with the available capacity in a zone.
Taking as reference this data availability and our assumptions on installed capacity, the stochastic costs of gas are constructed imposing that the zonal mean and variance values are defined as i,n = n and 2 i,n = 2 n , respectively and assuming that Φ -1 c c i,n ( ) ( ) is a quantile function of a standard normal random variable. The values of n and 2 n are reported in Table  4. Moreover, we consider different confidence levels i as indicated in Sect. 4. As already explained, the operating costs of wind and solar PV units are assumed to be equal to 0 €/MWh. The data used to compute the stochastic forecast of wind and solar PV production are taken from the Transparency Platform of the ENTSO-E. We consider as confidence levels i =1% and i =5%.
Acknowledgments The editorial team would like to acknowledge the following colleagues for their support in developing this Research Topic. From the  World Health Organization :  Marc McMonagle ,  Evis Kasapi ,  Sophie Amet ,  Pascale Abie ,  Denis Porignon ,  Gerard Schmets , and  Suraya Dalil ; and from the  International Association of National Public Health Institutes :  Anne-Catherine Viso ,  Quentin Sandifer ,  Jessica Borges , and  Neil Squires. The editorial team would also like to acknowledge the  Korea International Cooperation Agency (KOICA) , the  United States Agency for International Development (USAID) ,  Universal Health Coverage (UHC) Partnership , and  Department of Health of Ireland  for their support.
Predicting Importance Of Frailty Components In Heart Failure: Analysis Of T Measure Our research team analyzed the diagnostic importance of individual psychosocial and physical criteria in the diagnosis of FS in elderly patients suffering from HF  [37]. Based on the AI approach and the TFI questionnaire, including physical, psychological, and social components, machine learning models were constructed using decision tree, random decision forest, and AdaBoost classi er. These models were trained, validated and tested on three separated subsets of the full dataset. To nd the feature importance of the explanatory variables in machine learning classifying models, it was necessary to choose an appropriate method of evaluating these variables. The permutation method compares the accuracy of the model with its accuracy when we shu e the values of speci c variables.
Work supported by the  Finance for Energy Markets Research Initiative  and the  EcoREES   ANR-19-CE05-0042  project. † Center for Mathematical Economics (IMW) at Bielefeld University. Financial support from the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - SFB 1283/2 2021 -317210226 ,  DAAD  and BGTS is gratefully acknowledged. ‡ Ecole Polytechnique. This work benefits from the financial support of the  Chairs Financial Risk, and Finance and Sustainable Development.
Survey We designed a cross-sectional survey targeted at clinicians involved in the care of IBD worldwide. From January to February 2023, participants were invited to take a survey hosted on an online platform. The link was sent via email through the mailing lists of IBD-scope, a webinar platform designed for healthcare professionals interested in IBD, and by personal invitation of physicians with a focus on IBD. Screening questions were included at the beginning of the survey to filter respondents out of the target population and email registration was used to prevent double participation. Responses were collected anonymously and permission for data collection was asked upon survey start. The survey and the invitation email were in English, and all questions were multiple-choice. The questionnaire comprised 46 questions classified into 5 sections (Table  1 ): the first recorded demographics, specialty, and level of experience of participants (6 questions); the second focused on practices and attitudes toward biosimilar use in clinical practice (22 questions); the third was about interchangeability (reverse/multiple switches) (9 questions); the fourth addressed the nocebo effect and non-medical switch  (5) ; and the last one included current and future perspectives (4 questions).
M Calvo  has participated as a speaker, trainer, or consultant in projects funded by  MSD Spain ,  AbbVie ,  Takeda ,  Janssen ,  Pfizer ,  Dr. Falk ,  Faes Farma ,  Ferring , and  Tillotts. PG Kotze  has received honoraria from  AbbVie ,  Ferring ,  Janssen ,  Pfizer  and  Takeda  as a speaker and member of advisory boards; he also received scientific research grants from  Pfizer  and  Takeda. S Ghosh  declares consulting fees from  Pfizer ,  Janssen ,  AbbVie ,  Takeda ,  Bristol-Myers Squibb ,  Receptos ,  Celgene ,  Gilead ,  Eli Lilly  and  Boehringer Ingelheim  and speaker fees from  AbbVie ,  Janssen ,  Takeda ,  Ferring ,  Pfizer ,  Shield , and  Falk Pharma ; outside of the submitted work. L Peyrin-Biroulet  has served as a speaker, consultant and advisory board member for  Merck ,  Abbvie ,  Janssen ,  Genentech ,  Mitsubishi ,  Ferring ,  Norgine ,  Tillots ,  Vifor ,  Hospira/Pfizer ,  Celltrion ,  Takeda ,  Biogaran ,  Boerhinger-Ingelheim ,  Lilly ,  HAC Pharma ,  Index Pharmaceuticals ,  Amgen ,  Sandoz ,  Forward Pharma GmbH ,  Celgene ,  Biogen ,  Lycera ,  Samsung Bioepis ,  Theravance.
Identification and measurement of heterogeneous low-carbon lifestyle types To measure low-carbon lifestyle heterogeneity the LIFE model draws on a cluster analysis of large-scale social survey data in four countries (UK, China, USA and Australia)  [52] [53] [54] [55]. This distinguishes four lifestyle types heterogeneous across low-carbon behaviours and low-carbon cognitions. Low-carbon behaviours are captured 'universally' i. e. distinctive domain related behaviour is measured within three key domains: mobility; food; homes and aggregated. Cognitions are measured within four distinctive but complimentary pathways which measure perceived behavioural control, values, health and wellbeing, and environmental beliefs, and are similarly aggregated. Context is exogenous. It captures material and social constraints, and enablers to low-carbon lifestyle.
Extrapolation of lifestyle heterogeneity to countries globally Lifestyle groups are heterogeneous within a country, and lifestyle changes develop differently in each group. Empirical evidence for this heterogeneity is only available in some countries based on data analysed to date, but global IAMs require global coverage. We extrapolate the key characteristics of each lifestyle type to other global datasets. The main output of this process is a set of parameters that measure lifestyle heterogeneity globally, regionally, and within or across three domains (mobility, food, homes). For further details, see supplementary information A-Measurement and Extrapolation of Lifestyle Heterogeneity.
See also supplementary information C-Additional Tables and Results. Step 6. The revised low-carbon behavioural propensities are fed back to the IAM. Step 7. Modified IAM equations re-estimate behavioural outcomes. Return to step (1) for the next timestep. Note that both LIFE and MESSAGEix-Buildings run over a default five-year timestep, so are aligned over time periods of change in the empirical data (LIFE) and future simulations (MESSAGEix-Buildings). In this implementation, the coupling is unidirectional, with the MESSAGEix-Buildings model receiving energy price signals from the MESSAGEix-GLOBIOM IAM. See supplementary information A-The LIFE Model Schematic (1b) for full details of the LIFE model development (Step 1) and application to MESSAGEix-Buildings (Steps 4-5) (figure  2 ).
Acknowledgment B V R, C W, and T l G acknowledge funding support from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 821124  ( NAVIGATE ); HP and A M under Grant Agreement No. 101081604  (PRISMA). C W and M A were also funded by the  E R C  under Grant Agreement No. 101003083  (iDODDLE).
This should be of interest to policymakers using complexity metrics for inclusive green development and reinforce the idea that metrics of economic complexity go beyond measures of trade sophistication  33, 34, 60, 62, 63. Yet, this approach is not without limitations. First, patent application and research publication data also have limitations. For instance, since patent applications and research documents are usually written in English, these datasets can favor both English-speaking countries (e. g. , USA, Australia) and countries with high proficiency in English (e. g. , Netherlands, Sweden). Moreover, patent applications may differ from granted patents and could potentially be used to game patent-based indicators by actors willing to submit spurious patents to increase their reported output in certain technologies.
The use of patent applications, however, is common in the geography of innovation literature, and hence, our use of it makes our work comparable to previous research  40, 62. Second, there are plenty of activities that are not captured in either trade, patent, or research publication data-such as services, digital products, and cultural activities. These may capture additional aspects of the complexity of economies that would need to be included in a more comprehensive multidimensional framework  16, 64, 65. Unfortunately, the current state of the art does not include internationally comparable fine-grained datasets for these additional activities (e. g. , service trade data are too aggregate to approximate the productive structure of an economy, see ref. 66  and Supplementary Note 9). Third, our research is also limited by differences in the granularity of the three datasets: trade data are the most granular, with about 1200 unique products, while research publication data involves only about 300 subject categories.
This may be one of the reasons why we do not see strong effects from research complexity in economic growth and income inequality and one of the reasons why combining these datasets into a unified matrix (e. g. , by concatenating or multiplying these matrices) is nontrivial. Fourth, these results cannot be readily generalized to other geographic scales, such as states and provinces. For instance, while future economic growth has been shown to correlate with the complexity of countries  13, 15, 17, 20, 23  and regions  64  , the relationship between complexity and inequality is known to reverse at the regional scale  21, [65] [66] [67] [68]. Thus, this approach cannot tell us much about regional effects, which could be different from those observed on the international scale  21, [68] [69] [70] [71].
In regression analyses, we further normalize the values of ECI c to be nonnegative using a max-min technique (i. e. , they are between 0 and 1). We build our results using the standard definition of ECI  13, 15  because of multiple reasons. First, because this is a widely used definition, it makes our results more readily comparable with previous research. Second, because it is a definition designed for data on the geography of economic activities and focused on where activities come from instead of where they are consumed, we can apply it directly to our three datasets (without the need for special adaptations). Nevertheless, throughout the remainder of the paper, we also compare our results with two alternative definitions of economic complexity, the fitness index  46, 47  , and the innovation-adjusted ECI  40  (i-ECI).
Acknowledgements We thank  Eva Coll ,  Ron Boschma ,  Koen Frenken ,  Eduardo Hernández , the attendees of the  2022 Economic Geography and Networks PhD school in Utrecht , and the members of the  Center for Collective Learning  for valuable feedback. We thank  Pierre-Alexandre Balland  for facilitating patent data. This project was supported by:  ANR-19-PI3A-0004.
Participants All types of HCWs practicing in Africa (doctors, nurses, midwives, laboratory workers, and public health practitioners) were eligible for the study, irrespective of their experience of caring for people infected with HBV. We sent the invitation via email to HCWs listed in an existing database of stakeholders involved in previous hepatitis B projects. We also sought the collaboration of the Coalition for Global Hepatitis Elimination (CGHE) and Hepatitis B in Africa Collaborative Network (HEPSANET) to pilot the questionnaire and to help disseminate the survey across the continent. We used the chain-referral sampling technique. Study participation was entirely anonymous and no informed consent was required. The Institutional Review Board at the Institut Pasteur formally exempted the protocol from a full review (Reference: IRB2021-I-Exempt).
Acknowledgements We thank the  leadership and communication team at the Coalition for Global Hepatitis Elimination (CGHE)  and colleagues of the  Hepatitis in Sub-Saharan Africa network  (HEPSANET; https:// www. hepsa net. org), the  Conference on Liver Disease in Africa (COLDA) ,  Médecins Sans Frontières (MSF) , and  Médecins du Monde (MDM)  who helped in the wider distribution of the study questionnaire as well as all healthcare workers who participated. We also thank all participants who graciously agreed to pre-test the early versions of the questionnaire.
Study design The rationale, objectives, and methodology used in SANTORINI have been described in detail previously. 11 riefly, this prospective, observational, non-interventional study (NCT04271280) enrolled patients with high and very high CV risk across Europe. The patient recruitment period lasted from 17th March 2020 to 11th February 2021. The primary objective of this report of the baseline characteristics was to assess how physicians assessed risk, how they then approached lipid lowering regimens and to what extent the current approaches would result in attainment of the latest 2019 ESC/EAS lipid goals. Baseline data were collected from the patient charts from all dyslipidaemia-related visits at which the patient has been seen by the physician, starting from date of diagnosis. Electronic data capture was used for the recording of the information.
Patient cohort and data There were no specific exclusion criteria. Patients were eligible for enrolment if they were ≥18 years of age with high or very high risk of ASCVD, for whom LLT would likely be beneficial and had an anticipated life expectancy of more than one year. These patients were recruited from different care settings (primary and secondary care, and across different specialities); there was no specific criterion regarding physician selection. 11 he CV risk was assigned by the physician at enrolment, and the basis for risk classification was documented. 3] [14] [15]  When inconsistencies were found between the CV risk as assessed by the physician and the CV risk category recalculated centrally, a medical query was raised and the physicians were given the possibility to confirm their original classification.
Acknowledgements Kausik Ray  acknowledges support from the  National Institute of Health Research Imperial Biomedical Research Centre  and from the  National Institute for Health and Care Research Applied Research Collaboration Northwest London. Alberico L. Catapano  acknowledges support from the  Italian Ministry of Health-Ricerca Corrente-IRCCS MultiMedica. The authors would like to thank  Florence Mercier  for her statistical support and input. Medical writing support was provided by  Martina Klinger Sikora  and  Stefan Courtney of inScience Communications, Springer Healthcare Ltd, UK , and was funded by  Daiichi Sankyo Europe GmbH, Munich, Germany  in accordance with Good Publication Practice (GPP3) guidelines (http://www. ismpp. org/gpp3). Funding information: This study is funded by  Daiichi Sankyo Europe GmbH, Munich, Germany.
Patients were either below high risk (based on available data and SCORE) or potentially high-risk patients (max 48) but without the evidence in the dataset to support this classification. ASCVD, atherosclerotic cardiovascular disease; CV, cardiovascular; DBP, diastolic blood pressure; HeFH, heterozygous familial hypercholesterolaemia; eGFR, estimated glomerular filtration rate; ESC/EAS, European Society of Cardiology/European Atherosclerosis Society; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; SD, standard deviation; SCORE, Systematic COronary Risk Evaluation; TC, total cholesterol. a Total missing risk, n = 6. b Missing risk, n = 110.
999} has been calculated numerically. This was done using the R function uniroot in order to find the solution of (6. 1), where the function cubintegrate from the R package cubature has been used beforehand in order to calculate the distorted Tail-Value-at-Risk. In each replication we estimate this risk measure with the estimator introduced in Section 5, where the intermediate level k = k n is allowed to vary between n/50 and 2n/3 (corresponding respectively to 2% and 66. 7% of the total sample size). This produces estimates x e( j) τ n (k), j = 1, 2,. , N, which are used to calculate the Monte-Carlo approximation to the relative Mean Squared Error (relative MSE) of the estimator x e τ n , that is, rMSE(k) = 1 N N j=1 x e( j) τ n (k) x e τ n -1 2. These errors are represented as a function of k in Figs.