dataesr/scientific-paragraphs-categorization huggingface dataset:
Example: The analysis included only case patients (and controls, for the case-control analysis) for whom vaccination records were available on the Australian Childhood Immunisation Register (ACIR). The combined data set included all 282 cases from the state-based admissions data plus another 38 captured by Paediatric Active Enhanced Disease Surveillance (PAEDS). In the eight cases of patients with incomplete records, the ACIR documented receipt of a second (or third) dose of rotavirus vaccine but data were missing for the earlier dose(s) (…) Analysis was performed using Stata 11. 2 software (StataCorp; 2009). (…) Results (obtained from a conditional Poisson regression model) are reported as relative incidence (RI), with 95% confidence intervals (CIs), for each exposure period compared with time outside this window.
Item 15: Model input parameters Definition: The model input parameters are all evidence combined into the model for the benefit-risk estimates. Recommendation: Authors should list (1) all input parameters used in the analyses, (2) their values, (3) their ranges and (4) their sources, with the appropriate references and (5) the criteria for selection. When probabilistic simulation models are used (see items 13a and 13b), (  6 ) the probability distribution used for each input parameter should also be described. A tabular representation summarising these elements is strongly recommended. Explanation: To conduct a qBRm, authors should identify and combine available data that allowed feeding the input parameters. Those data are rarely extractable from a single study and are usually collected from diverse sources, including clinical trials, observational studies, administrative databases, case-series, expert opinion, assumptions from authors and/or secondary analyses (e.
Different approaches have been proposed, and in some cases endorsed, by regulatory bodies. This is the case, for instance, of the Total-Product-Life-Cycle (TPLC) by the US Food and Drug Administration (FDA)  [17]  that combines data from various Center for Devices and Radiological Health (CDRH) databases to present an integrated record of premarket and post-market activity for MDs. The main disadvantage of the TPLC approach is that it encourages linearity on our thinking, in that it portrays a series of steps in product development, in a particular order, each requiring a particular type of clinical evidence. This approach works fairly well as a model for pharmaceutical product development, but the development of MDs is often more unpredictable. For example, it is possible for MDs to enter into regular clinical practice with relatively little clinical evidence, the evidence being predominantly gained through actual use of the device in clinical practice  [18] , as opposed to a research setting  [19, 20].
Generally speaking, robust clinical data generated through high-quality studies are probably valuable across countries and would likely form the 'minimal dataset' of clinical evidence widely applicable for the regulatory and market access across different jurisdictions  [47]. More complex and innovative variants of the simple two-group parallel RCT exist and include adaptive designs  [48]  which make use of data collected during the progress of a clinical study to allow modifications to the ongoing trial, such as dropping an ineffective arm or capturing incremental innovation throughout the evaluation process. Whilst the cost of a standard RCT may be too high, and even prohibitive, for many small and medium device manufacturers, the explosion of electronic health records (EHRs) and the extraordinary penetration of mobile technology are currently fostering the digital innovation paradigm in the clinical trial sector  [49].
Solutions are available to address various challenges from the study design to trial operations, data management, and report writing with a significant overall reduction in costs. In this regard, the Big Data for Better Outcomes (BD4BO) program by the Innovative Medicine Initiative as well as the European Database for Medical Devices (EUDAMED), to be launched simultaneously for both medical devices and in-vitro diagnostics together in May 2022, may be potentially interesting  [50,p. 4 ] and, although acceptance of EHRs by regulatory bodies and HTA agencies is not common yet  [51] , the trend is for an increased use of this source of evidence. There are several situations where an RCT is simply not possible or appropriate. These include 1) in small patient population where it is not possible to recruit sufficient numbers to demonstrate a statistically significant advantage over control, 2) when ethical issues discourage the use of a control or comparator arm (e.
Registries are likely to be more useful to payers if they include several devices of the same type or group, so that comparative clinical data can be collected. The outcomes measured in registries should also include those of interest to patients and payers, such as use of key resources and, where possible, assessments of patients' quality of life. It is also important to include information on relevant covariates that can be used to adjust for known confounders in observational studies. Finally, it may be of interest to payers to enroll patients from different countries and settings, in order to explore the generalizability of clinical outcomes from one setting to another. Some payers have entered into various types of performance-based risk-sharing arrangements, such as 'coverage with evidence development' with device manufacturers.
SONG-PD Consensus Workshop Overview and Context The SONG-PD consensus workshop was convened at the 17th Congress of the International Society for Peritoneal Dialysis (May 7, 2018) in Vancouver, Canada, to elicit stakeholder perspectives on the potential core outcome set for PD. The potential core outcomes were based on data from an international 3-round Delphi survey that was completed online by patients, caregivers, and health professionals with experience or expertise in PD. The Delphi survey included outcome domains reported in trials in PD, as well as from previous studies with patients receiving PD. 7  The SONG-PD process for establishing core outcome domains is shown in Figure  1.
Several studies have reported the benefit of lipoprotein apheresis in high-risk patients with elevated Lp(a)  [21]. At the same time, therapeutic innovation in lipid-lowering treatments has brought new insights, suggesting that a decrease in Lp(a) concentration by new drugs may produce significant risk reduction mediated through Lp(a) reduction, reinforcing the interest in Lp(a) and its reduction. New approaches targeting atherogenic lipoproteins include inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) to decrease circulating LDL cholesterol. PCSK9 inhibitors have also been shown to significantly decrease Lp(a) concentrations  [22]. New pharmacotherapeutic agents blocking Lp(a) synthesis with antisense oligonucleotide therapy are being developed, with ongoing phase III outcomes trials planned (ClinicalTrials.
The magnitude of cardiovascular benefits directly linked to a decrease in Lp(a) remains a matter of debate. The ANITSCHKOW study  [27] , investigating the anti-PCSK9 antibody evolocumab, showed a modest 14% reduction in Lp(a) in a cohort with a baseline Lp(a) concentration of 75 mg/dL. No decrease in arterial wall inflammation measured by positron emission tomography/computed tomography was observed (results as reported on ClinicalTrials. gov) [27]. These findings highlight the need for more efficient and specific agents to decrease the adverse proinflammatory state associated with Lp(a) elevation. This controversy may be resolved by the ongoing dedicated cardiovascular outcome trials investigating Lp(a) specific inhibitors that reduce Lp(a) by a sufficient magnitude.
The spectrum of patients is large, including those who have had a myocardial infarction or ischaemic stroke 3 months to 10 years before, and patients with asymptomatic peripheral artery disease. The primary endpoint is a composite of cardiovascular death, non-fatal myocardial infarction, non-fatal stroke and urgent coronary revascularization requiring hospitalization in patients with Lp(a) ≥ 70 mg/dL at baseline; the same primary endpoint will be reported in the subgroup of patients with Lp(a) ≥ 90 mg/dL at baseline. Other "usual" efficacy and safety endpoints have also been prespecified in this large randomized controlled trial to validate this new lipid-lowering therapy. In this therapeutic class, a phase 1 study with a single dose of another oligonucleotide antisense named AMG 890 has been initiated, mainly to evaluate the safety of this new entity (ClinicalTrials.
These promising findings have led to an ongoing phase III outcomes trial investigating cardiovascular benefits in high-risk patients with elevated Lp(a) concentrations. This study, named "Assessing the Impact of Lipoprotein (a) Lowering With TQJ230 on Major Cardiovascular Events in Patients With CVD (Lp(a) HORIZON)" (ClinicalTrials. gov Identifier: NCT04023552), will randomize 7680 patients in secondary prevention to AKCEA-APO(a)-LRx (TQJ230) or placebo. Key inclusion criteria combine the need for secondary prevention with an Lp(a) concentration ≥ 70 mg/dL at.
Acknowledgements Kausik K. Ray  acknowledges the support of the  National Institute for Health Research (NIHR) Imperial Biomedical Research Centre. Imperial College London  is grateful for support from the  North West London NIHR Applied Research Collaboration. The views expressed in this publication are those of the authors and not necessarily those of the  NIHR  or the  Department of Health and Social Care. We thank  Mrs Elena Angoulvant  for drawing the original figure depicting Lp(a) structure and  Marinos Fysekidis, MD, PhD , for his comments. D. A. Consulting fees, lecture fees or funding for conference travel from the companies  Amgen, AstraZeneca ,  Bayer ,  Eli Lilly ,  MSD ,  Novartis ,  Pfizer. Sanofi and Servier. K. K. R. Personal  fees from the companies Abbvie,  Akcea, Aegerion, Algorithm, Amgen, AstraZeneca ,  Bayer ,  Boehringer Ingelheim ,  Cerenis ,  Cipla ,  Daiichi Sankyo ,  Dr Reddys ,  Esperion ,  Kowa ,  Lilly ,  MSD ,  Novartis ,  Pfizer ,  Resverlogix ,  Sanofi/Regeneron ,  Silence Therapeutics ,  Takeda ,  The Medicines Company and Zuelling Pharma.
Statistical analysis All statistical tests were performed using GraphPad Prism V. 8. 3. 1. for Windows (GraphPad Software, La Jolla, California, USA). Non-parametric Spearman's correlation coefficient (r) was calculated to measure the strength of the association between the peak surface area of captopril and that of captopril disulfide. Mann-Whitney tests were used to compare the median between drugs with lower and those with higher protection from humidity packaging blisters. Open access.
Results The model is implemented in scala and integrated into the OpenMOLE software  (Reuillon et al. , 2013)  for numerical experiments. OpenMOLE allows embedding models in any language as black boxes, provides a transparent access to high performance computing infrastructures, and model exploration and validation methods such as sensitivity analysis, design of experiments, and calibration methods. Experiments are designed through workflows using a Domain Specific Language  (Passerat-Palmbach et al. , 2017). Source code of the model and analysis, and results are available on the open git repository of the project at https://github. com/ JusteRaimbault/UrbanEvolution. Simulation data files are available on the dataverse repository at https://doi. org/10. 7910/DVN/Q5GKZ0.
Model optimization The last experiment we perform is the application of a multiobjective optimization procedure to the model. More particularly, one could want to optimize simultaneously the global utility but also the diversity to ensure a certain resilience in the urban system. We apply thus a bi-objective genetic algorithm on the model, trying to maximize simultaneously utility and diversity. The algorithm is the NSGA2 algorithm  (Deb et al. , 2002) , which we run for 10,000 generations with a population of 200 individuals on a Island distribution scheme. The algorithm is integrated into the OpenMOLE software, and the stochastic aspect is internally tackled by using an embedding strategy, i. e. by adding the number of repetitions as an additional objective to find compromise points between the number of run needed and their statistical accuracy.
Methods We used the PROMOTION Registry  19  to identify all cancer RCTs published between January 2004 and February 2019 conducted in the most common cancer types  20  , i. e. breast, lung, colorectal, genitourinary, and gynaecological cancers. The PROMOTION Registry is a large database containing all RCTs published from 2004 across a wide range of cancer populations that included at least one PRO, either as a primary or secondary or exploratory endpoint. The trials were systematically identified, mainly through PubMed/Medline, and using ad-hoc key searching strategies for each cancer disease site. We included RCTs that enrolled a minimum of 50 patients and that reported at least one PRO endpoint. Trials comparing screening programs, complementary/alternative medicines and psychosocial interventions were excluded.
Details on the search strategy and selection process were documented according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines (PRISMA)  21. For each trial selected, two trained reviewers independently extracted information. When disagreement about data extraction occurred, the two reviewers revisited the paper to reconcile any differences and, if necessary, a third reviewer was consulted to resolve any outstanding discrepancy and reach consensus. Each reviewer had a personal password to access the study website (REDCap  22  ) and complete a pre-defined electronic data extraction form. Data extracted from each RCT included: (1) basic trial information (e. g. study location, sample size, disease stage, type of treatments being compared); (2) quality of PRO reporting, based on the International Society for Quality of Life Research (ISOQOL) recommended standards  23  ; and (3) PRO instruments used and type of PRO outcomes that yielded statistically significant differences between trial arms.
This paper advances a research agenda to interpret the unequal and geographies of ownership markets. Specifically, we ask to what extent contemporary social inequality is shaped by one's relationship to spatially stratified housing markets? We hypothesise that the flow of real estate investments and residential housing production are predicated upon intensifying the financial stratification of urban inequalities at the neighbourhood level. This complex socio-spatial layering is what we refer to as a housing finance regime (HFR), or a system of national and local incentives for accessing credit associated with the spatial variability of income and housing prices. Using disaggregated datasets on property transactions, household surveys, and homeowner insights on residential mobility, we propose to analyse the economic spatial and financial trajectories of households in relation to housing price dynamics among three French cities (Paris, Lyon, Avignon).
Identifying these factors helps to clarify the ways that socio-spatial stratification is produced, as initially proposed by  Harvey (1974). Geocomputational advancements now make the visualisation of large, disparate datasets possible to apprehend the factors affecting urban inequality  (Brown & Chung 2008; Le Goix et al. 2019; Migozzi 2019 ) and economic mobility across geographical contexts  (Hochstenbach & Musterd 2018). Thus far, however, the methodological integration of these data remains underdeveloped.
Interviews with the main stakeholders aim to deepen our understanding of local housing policies and their implementation. Specific attention is paid to the relationships between stakeholders and how they structure local conditions in which urban development, land availability, and fiscal policies operate, as well as their impacts on home values and wider patterns of residential market segmentation. The unequal spatial structure of affordability -To inform the local spatial structure of affordability we integrate different spatial datasets and surveys which have so far been studied separately (Table  1 ). There are plenty of institutional (census records), private (realestate agents and websites) and national or local datasets (cadastral records, tax rolls). However, few methods have been developed to harmonise this spatial data as a means to study housing market trends  (Julliard & Gusarova 2019)   1 ).
A key issue with these datasets is that they differ substantially in their structure, exhaustiveness and content, with variables that do not always compare  (Casanova et al. 2017). Combining these different elements nevertheless allows us to control for inconsistencies across transaction data, while also enhancing our ability to analyse new relationships between disaggregated data © 2020 Royal Dutch Geographical Society / Koninklijk Nederlands Aardrijkskundig (between sellers and buyers, for instance). In particular, our focus centres on the local geographies of the market (200 m grid, 1k grid and municipalities) to insure the robustness of our aggregation techniques, and to draw conclusions about spatial stratification at a finer scale of analysis  (Boulay 2012; Le Goix et al. 2019). To approach the problem empirically, we observe and measure the feedback loop at four key junctures (Figure  1 ).
Tax rolls also allow us to examine investment/divestment trajectories of different property owners relative to the tenure status of the assets, over time. Additionally, we use disaggregated data from the French family benefits agency (Caisse d'allocations familiales, with access to individual data for 2. 3 millions households), which provides crucial information describing the benefits history of households, and allows to analyse several important dimensions: the upward/downward mobility of households, changes in the life-course (coupling, having children, etc. ), or the loss or gain of income over a period of time. Such a dataset helps to explain the wider trends in financial gains and losses across households. This matters because rising house prices do not always affect housing affordability for households.
2019; Hochstenbach & Arundel 2020) , and further work will focus on fine grain data analysis. Data presented in this section does not include relevant information for outer suburbs municipalities, mostly because of statistical secret and sampling issues (number of records not significant to aggregate). Price-to-income ratio -First, we characterise affordability for median households as an entry point to assess socio-economic inequality in the feedback loop, starting with price-toincome ratio (PIR) metric  (Friggit 2017). Household purchasing power is estimated as the ratio between local (municipal) and national median income. The question we pose is: to what extent would a 'median income household' anywhere in France have the purchasing power to access a given neighbourhood? We measure affordability as the monthly income required to buy 1 sq.
As mentioned in the methodological framework section, many such datasets remain incomplete as few countries make data on individual-level property transactions widely available. This poses major challenges for comparison. Our approach seeks that not only should these data be integrated for stronger, more comprehensive analysis across geographical scales, but also how to go about doing it, conceptually and methodologically. As in Table  1 , we bridge local data aggregates with qualitative information on the households actually involved in market transactions while moving in and out (recent buyers identified by new addresses declared at the family benefits agencies and at the postal services). We link these trends to wider geographical attributes and socioeconomic characterisations among the properties and households.
Acknowledgements This paper was prepared under the  ANR WIsDHoM project ,  Wealth inequalities and the dynamics of housing markets. ( ANR-18-CE41-0004 ). The transactions BIEN proprietary database was made available by Paris Notaire Service, on the behalf of the Chamber of the Notaries, under an agreement contracted by the  LabEx DynamiTe  ( ANR-11-LABX-0046 ) consortium and the  Univ. Paris 1 Pantheon-Sorbonne. And infinite thanks to the Menthonnex 2018 and 2019 #AcWri team: "We have opened the space for writing. Let's make it hospitable". We also wish to thank the three anonymous referees for valuable comments and criticisms on the earlier version, and therefore their contribution. The usual disclaimers apply.
However, it is of critical importance as such volatility spillovers could re ect the potential existence of new crisis transmission channels driven by the interactions between these two sectors. To conduct our analysis, we use intraday data (1-minute spot prices) of stock market indexes of the four major US banks (Bank of America, Citigroup, JP Morgan Chase, and Wells Fargo) and the West Texas Intermediate (WTI) oil price from January 2006 to June 2016. These high-frequency data allow us to build a daily realised volatility measure and to obtain an accurate estimate of the integrated variance. To assess volatility spillovers, we then use Vector AutoRegressive Fractionally Integrated Moving Average (VARFIMA) models introduced by  Chiriac and Voev (2011). These models provide a suitable framework for capturing long memory dynamics of stock and oil price volatilities and identifying the interrelationship between them.
To assess the banking sector's nancial health, we considered the stock price index of four major US banks on the S&P 500 sourced from QuantQuote: Bank of America, Citigroup, JP Morgan Chase, and Wells Fargo. Two criteria have guided this choice: (i) the four selected banks have been the most exposed to O&G sectors over the recent period; 14 (ii) they are recurrently identi ed as global systemically important banks by the Financial Stability Board, 15 and, they are thus likely to destabilise the whole nancial system in case of bankruptcy. 13. Our sample period begins in 2006 since the US has started to extract shale oil on a large scale during this year. It ends in 2016 due to a lack of available data. Indeed, the dataset on the historical series of intraday stocks used in this paper has a restricted policy and is not available free of charge.
Source: O cial US. Energy Information Administration (EIA) estimates derived from state administrative data collected by Enverus. Source: Companies' annual reports. Notes: The data used to produce this chart were extracted from companies' annual reports from 2006 to 2017. Vertical axis: Calculated as the amount of credit exposure to O&G industries divided by total loans multiplied by 100. Credit exposure is net of risk participation and excludes the bene t of credit derivative hedges and collateral held against derivative receivables or loans.
Procedures The questionnaire was published online in English and 21 other languages corresponding to the participating countries on SurveyMonkey1 (www. surveymonkey. com, Palo Alto, CA, USA). Content validity of the translations where checked by the national coordinators who were all fluent in English. In Ukraine, where web access was limited, a paper version was administered. The first set of survey questions determined GP -specific characteristics (sex, years of experience as a GP, estimation of prevalence of oldest-old patients and location of their practice). Then, GPs were asked if they used hypertension guidelines to decide how to treat the oldest-old, and which guidelines they used. We defined the first guideline they mentioned as the most important. We analyzed all the documented guidelines and we categorized them.