dataesr/scientific-paragraphs-categorization huggingface dataset:
Controls We used epidemiological data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. We compute the number of new COVID cases per 100,000 inhabitants for each state and day. 5. We also consider state-level social distancing policies from  Fullman et al. (2020). The dataset includes a wide set of policies, such as restrictions on gatherings, school closures, stay-at-home orders, and nonessential business closures. We create a dummy variable by state to indicate whether any of the previous policies were in place on a given date. We consider the date of policy enactment. We also create a variable to capture political polarization by considering the percentage of Trump votes by state during the 2016 election. Finally, we use environmental data from the National Centers for Environmental Information to control for daily rainfall and temperature in each state.
The use of the ordinary least squares method (OLS) regression would, in this case, produce negative predicted WTP values, which is incorrect from a theoretical perspective  (Mitchell & Carson, 1989). The data from the respondents were collected by posting an online web survey using the Qualtrics survey platform. The survey was run concomitantly in a number of countries, including the USA, the Netherlands, Chile, South Africa, Australia, France and Italy. The data were analysed separately for each country using a different methodology. The focus of this article is the analysis of the South African consumer preferences. Future research will also compare the data among the countries. The survey consisted of three sections. The first section asked respondents about their background and habits with wine consumption and purchasing behaviour.
Measures. The data were collected prospectively. Performance tests were conducted by trained nurses, and a comprehensive geriatric assessment was performed by a geriatrician. In the present study, we selected components from the comprehensive geriatric assessment's nutrition, mobility, physical activity, strength, endurance, balance, cognition, senses, mood, and social domains. Self-reported unintentional weight loss over the previous year (regardless of the amount lost) was recorded. Mobility was assessed via a daily pedometer count over a week (low level of physical activity: < 7500 steps per day)  23  , and gait speed over 10 m (slowness: < 1 m/s)  24. Muscle strength was estimated by the completion time in a five-time sit-to-stand test (≥ 11. 19 s)  25  , and maximum dominant-hand grip strength (kg) measured with a dynamometer (JAMAR, Sammons Preston, Bolingbrook, IL, USA).
A qualified reviewer should be able to link figures, graphs, conclusions, and other summary data to the raw data that was processed/analyzed; link the summary data to the corresponding experiment described in a lab notebook entry; and, likewise, link the lab notebook entry to the raw data (e. g. , when generated by an automated instrument). HOW: Depending on the organization, resources and workflows, this could be an electronic or paper-based lab notebook, a more complex Laboratory Information Management System (LIMS), or some other form of archiving as long as the information handling procedures are transparent and properly documented. Phase 2 -Defined by user-specific needs During this Phase, the primary focus is on challenges that are specific to the research unit where the EQIPD Quality System is being established.
Acknowledgements This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  777364. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The authors are very grateful to  Martin Heinrich  ( Abbvie, Ludwigshafen, Germany ) for the exceptional IT support and programming the  EQIPD Planning Tool  and the Creator Tool and to  Dr Shai Silberberg  ( NINDS, USA ),  Dr. Renza Roncarati  ( PAASP Italy ) and  Dr Judith Homberg  ( Radboud University, Nijmegen ) for highly stimulating contributions to the discussions and comments on earlier versions of this manuscript. We also wish to express our thanks to  Dr. Sara Sto ¨ber  ( concentris research management GmbH, Fu ¨rstenfeldbruck, Germany ) for excellent and continuous support of this project.
Acknowledgments We are indebted to  Joseph E. Stiglitz ,  Stephen G. Cecchetti ,  Sérafin Jaramillo ,  Dilyara Salakhova ,  Marco D'Errico ,  Guido Caldarelli , for valuable comments and discussions that helped to improve the paper. We also thank participants to the various conferences where earlier versions of this paper were presented. These include the  Second Conference on Network Models and Stress Testing for Financial Stability ,  Banco de México , September  26-27 ,  2017 , the  Conference on Complex System 2017  ( CCS 2017 ), in Cancún, September  17-22 ,  2017 , the first and second FINEXUS Conference, Zürich (January 2017, January 2018), the 22nd Workshop on Economic Science with Heterogeneous Interacting Agents (WEHIA 2017),  Catholic University of Milan , June  12-14 ,  2017 , and the 23rd Computing in Economics and Finance (CEF 2017),  Fordham University, New York City , June 28-30, 2017.
In Section 4 we demonstrate the algorithm's generality by applying it to other theories that feature non-convexities, a fat-tailed wealth distribution and multiple assets. Codes for these applications (and many more) are available from http:// www. princeton. edu/ ∼ moll/HACTproject. htm in Matlab as well as Python, Julia and C++. The first step of the algorithm is to solve the HJB equation for a given time path of prices. The second step is to solve the KF equation for the evolution of the joint distribution of income and wealth. Conveniently, after having solved the HJB equation, one obtains the time path of the distribution essentially "for free," i. e. with very few lines of code. This is because the KF equation is the "transpose problem" of the HJB equation. 10 The third step is to iterate and repeat the first two steps until an equilibrium fixed point for the time path of prices is found.
Just to give a flavor, computing the stationary equilibrium of Huggett's economy with two income types and 1,000 grid points in the wealth dimension using Matlab takes around a quarter second on a Macbook Pro laptop computer. At the same time, the algorithm is simple. Implementing it requires only some basic knowledge of matrix algebra and access to a software package that can solve sparse linear systems (e. g. Matlab). Finally, the algorithm is portable. For example, it applies without change to problems that involve non-differentiabilities and non-convexities. These are difficult to handle with standard discrete-time methods. 11 In contrast, viscosity solutions and finite difference methods are designed to handle non-differentiable and nonconvex problems. To illustrate this, we use the same algorithm to compute equilibria of an economy in which the interplay of indivisible housing and mortgages with a down-payment constraint cause a non-convexity which can result in individual poverty traps and multiple stationary distributions (Section 4.
In contrast, the discrete-time condition (47) defines the optimal choice only implicitly. Typical solution methods therefore employ costly root-finding operations. Our continuous-time approach again sidesteps this difficulty. 40 ntuitively, discrete time distinguishes between "today" and "tomorrow" but in continuous time, "tomorrow" is "today. " 41 The third advantage of continuous time is a form of "sparsity. " To solve the HJB and KF equations (  7 ) and (8), we discretize these so that their solution boils down to solving systems of linear equations. The resulting matrices are typically extremely sparse, namely "tridiagonal" or at least "block-tridiagonal. " This sparsity generates considerable efficiency gains because there are well-developed routines for solving sparse linear systems, either implemented as part of commercial software packages like Matlab or open-source libraries like SuiteSparse.
The Method's Performance: Some Illustrative Results All of Figures 1, 6 and 8 for the Huggett economy in Section 1 earlier in the paper were computed using a Matlab implementation of the algorithm just laid out. Even though we work with a fine wealth grid with I = 1000 grid points, solving for a stationary equilibrium takes about 0. 25 seconds on a MacBook Pro laptop computer. Next, consider the corresponding transition dynamics. With I = 1000 wealth grid points, N = 400 time steps and the same hardware, computing (  12 ) and (  13 ) for a fixed time path r(t) takes about 2 seconds. Iterating on r(t) until an equilibrium transition is found takes about 4 minutes (even though market clearing conditions like (4) that implicitly define prices are notoriously difficult to impose during transitions).
This would not be true if we had relied on an pre-built ODE solver (say one that is part of Matlab) to solve the ODEs (7) and (8). Other income processes are possible as well. For instance,  Kaplan, Moll, and Violante (2016)  consider a "jump-drift process" with transitory and permanent components. As in (54) there is a continuum of types for each component; but rather than moving continuously over time as in (54), each component is subject to Poisson jumps. Income could also follow a jump-diffusion process. Figure  10  plots the stationary saving policy function and wealth distribution when income follows a diffusion. Both inherit all important properties of the saving policy function and wealth distribution from the baseline model with two income types from Sections 1 and 2. This is not just a numerical result.
Methods In this prospective study, we included participants from the Whitehall II cohort study who were assessed for cognitive impairment in 2007-09 and were followed up for clinically diagnosed dementia. Algorithms based on poor cognitive performance (defined using age-specific and sex-specific thresholds, and subsequently thresholds by education or occupation levels) and objective cognitive decline (using data from cognitive assessments in 1997-99, 2002-04, and 2007-09) were used to generate seven alternate definitions of cognitive impairment. We compared predictive accuracy using Royston's R², the Akaike information criterion (AIC), sensitivity, specificity, and Harrell's C-statistic. Findings 5687 participants, with a mean age of 65•7 years (SD 5•9) in 2007-09, were included and followed up for a median of 10•5 years (IQR 10•1-10•9).
Study population Data are from the ongoing Whitehall II cohort study, which was established in 1985 among 10 308 British civil servants (6895 men and 3413 women) aged 35-55 years. 12  cognitive test battery was introduced to the study in 1997-99 and repeated in 2002-04, 2007-09, 2012-13, and 2015-16. In addition to data collection within the study, data over the follow-up were available using linkage to electronic health records of the UK National Health Service (NHS) for all but ten of the 10 308 participants recruited to the study. There were no exclusion criteria. Participants gave informed written consent at each contact and the latest ethical approval was from the NHS London Harrow Research Ethics Committee (reference number 85/0938).
Research in context Evidence before this study We did a comprehensive search of studies on progression from cognitive impairment to dementia using PubMed, including all studies from database inception until Jan 20, 2021. We used the following search terms: "dementia", "cognitive impairment", "cognitive decline", "poor cognitive performance", "progression", and "prediction". Our search showed that most studies used a single cognitive assessment to ascertain cognitive impairment and then examined rate of conversion to dementia, often using a short follow-up. While this method suggests associations between cognitive impairment and dementia, it does not allow conclusions to be drawn on the validity of cognitive impairment in predicting dementia.
Outcomes For our primary outcome of dementia, dementia ascertainment was undertaken using linkage to Hospital Episode Statistics (HES), the Mental Health Services Data Set, and the British national mortality register using the International Classification of Diseases, Tenth Revision codes F00-F03, F05•1, G30, and G31. Record linkage for dementia was available until March 31, 2019. The HES contains clinical diagnoses from inpatient, outpatient, and emergency departments and has a sensitivity of 78•0% and specificity of 92•0%. 17 The Mental Health Services Data Set contains dementia diagnoses from inpatient, outpatient, and community mental health services, including memory services. The British national mortality register collects information about cause-specific mortality. Date of dementia was set at the first record of dementia diagnosis in any of these three databases.
First, we used a three-category variable (no cognitive impairment, only poor cognitive performance, or poor cognitive performance and cognitive decline) to assess the importance of cognitive decline in cognitive impairment when both poor cognitive performance and cognitive decline are included in the definition (definitions 3, 5, and 7), using age, sex, and then education and occupation thresholds as described in the main analyses. Second, to address the effect of selection biases due to missing data, we repeated the main analysis using inverse probability weighting to reflect the study population at the start of the cohort study (ie, the year 1985). Of 9362 participants who were alive in 2007-09 (start of follow-up in these analyses) and did not have a dementia diagnosis, we first calculated the probability of being included in the present study using data from the start of the cohort study on sociodemographic factors, health behaviours, cardiometabolic risk factors, and mental health and data on chronic conditions over the follow-up period (1985-2019).
23 In our study, with assessment of cognitive decline over 10 years and a subsequent 10-year follow-up, the cumulative conversion ranged from 9% to 18% for the seven definitions of cognitive impairment. Conversion to dementia might not be meaningful, because an annual conversion rate of 10% from MCI to dementia reported in a review  24  seems implausible because it implies that nearly two thirds of the study population will have converted to dementia after 10 years. Furthermore, the review also reported considerable heterogeneity in conversion rates between studies, limiting the usefulness of MCI for identifying risk of dementia. Two recent studies used data from the Alzheimer's Disease Neuroimaging Initiative study, based only on people with a diagnosis of MCI, to examine conversion to dementia.
34 he primary strength of this study is the use of a largescale, longitudinal study spanning over 20 years that allows assessment of the predictive value of cognitive impairment for dementia. Cognitive decline spanning 10 years could be used to determine cognitive impairment status, following recommendations in this domain. 4, 6, 7 As such data are unlikely to be available in clinical practice, we also used measures of cognitive performance alone in definitions 1, 4, and 6. Other advantages were the use of an algorithm to guide diagnostic classification of cognitive impairment, which reduces random variability and biases due to variability in clinical judgment. Finally, the use of predictive statistics in addition to conversion rates and HRs to show strength of associations allows assessment of the utility of cognitive impairment.
Acknowledgments We thank all of the participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants, and data entry staff, who make the study possible. The  Whitehall II  study.
26 After communicating with the district health office in each district, we obtained lists of registered public and private health facilities within the area. At the time of data collection, there were a total of 202 outlets registered in the 6 districts selected: 6 (2. 9%) outlets were of the public sector (2 provincial hospitals, 4 district hospitals), and 196 (97. 0%) outlets were of the private pharmacies. Figure  2  presents the flowchart of the sampled outlets. Data on availability and price of ASM were collected from all provincial hospitals, and district hospitals presented in both urban and rural districts. Random sampling was performed to select 50% of private pharmacies (I, II, III) in the urban district. In the rural district, all private pharmacies were included. For data collection, availability and affordability endpoints, and data analysis were adapted from those used in another project performed in Lao PDR and published in 2020.
| Data collection Anti-seizure medication availability, strength, cost, and country of manufacture were obtained from the head of the pharmacy service in the public sector. In the private sector, investigators gathered information by acting as a patient, showing a handwritten prescription of ASM with the International Non-proprietary Name (never the brand name), and then asking questions such as "I would like to buy these drugs -Are these drugs available? If so, what strength? How much does it cost? And what is the country of manufacture?".
| Data analysis and statistics Statistical analyses were performed using SPSS version 23. 0. Proportion, means, standard deviations, and 95% confidence intervals (CIs) were used for the descriptive analysis. Chisquare and Fisher's exact tests were performed for comparative analysis. Multinomial logistic regression was performed to identify the association between the independent variables (storage conditions including presence or absence of airconditioning, exposure to light or wind; presence or absence of packaging; healthcare sector; study area; local or imported ASM) and the dependent variable (quality of ASM). Only variables with a P-value <. 25 after univariate analysis were entered into a multivariate logistic regression model using the backward stepwise exclusion method.
| Sampling Of the 125 randomized outlets, six were inaccessible due to flooding in Kampong champ province on the day of data collection. Nineteen private pharmacies within a kilometer radius of provincial/district hospitals were added, due to their proximity to health centers able to diagnose epilepsy. Finally, 138 outlets were included in this study corresponding to a proportion of 68. 3% of the total number of outlets present in the study area (2 provincial hospitals, 3 district hospitals, and 132 private pharmacies).
The main limitation is that we did not investigate the procurement price in each healthcare facility. The availability of ASM at the time of data collection may not be the same throughout the year. Besides, our measure of affordability did not consider other diagnostic or consultation costs and other indirect costs such as transportation cost and loss of day work. The main strength of this study is its multicentric design, providing a picture of socio-economically different areas. Availability and price were assessed in real-life situations by acting as patients for data collection, and quality analysis was consistent with standard international methodologies. Increasing and assuring domestic production of ASM is an integral part of the strategy to lower drug prices and increase availability.
The profusion of policies and the generation of disparate data sets increases the complexity and costs associated with the design and conduct of clinical trials; neglects an opportunity to streamline the oversight process; and prevents the development of international databases that might be used to improve real-world outcomes and identify areas of clinical practice that may be amenable to cost reductions. To facilitate the development of an evidence base for migraine that clarifies the effects of new technologies for acute and preventive treatments for regulators and payers who rely on HTAs for decision making, the Clinical Trials Subcommittee of the International Headache Society presents the first Health Technology Assessment for the Acute Treatment of Migraine Attacks and Prevention of Migraine: A Position Statement of the International Headache Society.
org/instruments/migraine-specific-qual ity-of-life-questionnaire). Migraine Disability Assessment questionnaire. The Migraine Disability Assessment (MIDAS) questionnaire  (36)  was originally validated using a 3-month recall period, but 4-week recall forms have been developed and used in clinical trials  (37). MIDAS can provide estimates of absenteeism and presenteeism  (38). MIDAS score data can be reported as time lost due to migraine, which is calculated as absenteeism plus 50% of presenteeism. Health-related quality of life and generic. Validated, disease-specific health-related quality of life (HRQOL), productivity, and generic instruments are recommended as secondary endpoints. For some of the instruments listed in this section, the betweengroup minimal important difference (MID) has been defined in migraine and used in clinical trials of treatments for episodic migraine  (34, 39).
The advantage of reweighting is that it only requires observational baseline patient characteristic data, which are more likely than observational outcome data to be available at the time of market access and reimbursement regulatory decisions. The supplementation with real-world evidence is possible using different sources, including patient surveys, patient registries, healthcare databases (including electronic health records), pharmacy and health insurance databases, social media and patient-powered research networks. All of these, however, suffer from selection biases that can affect valid inference. Using these sources, real-world evidence is usually provided by observational studies. However, real-world evidence is difficult to implement because it is rarely available for a new drug or treatment at the time of market access and reimbursement regulatory decisions.
Second, we chose to study colorectal cancer, cancer whose incidence is not influenced by socioeconomic status in France. 34 A different result could be observed for other cancers. Third, since we chose to include only patients with mCRC, for who neither oral nor intravenous was preferred by French guidelines, we were only able to include a limited number of patients. Our study also presents some strengths. EDI is a pragmatic, widely used and reliable ecologic score to be used as a proxy for measuring individual socioeconomic status. Its validity as a very good approximation of individual social status has been demonstrated  21, 34, 35  and it has the advantage of being available for all inpatients since the patient's address is systematically collected in the medical records. Second, we performed a thorough search through hospital databases in order to identify our target population.
METHODS The World Health Survey (WHS) was a cross-sectional survey carried out in  2002-2004. Survey details are available elsewhere (http://www. who. int/healthinfo/survey/en/). Briefly, a single stage random sampling approach was employed in 10 countries, while a stratified multi-stage random cluster sampling method was used in 60 countries. Individuals with a valid home address aged ≥18 years were eligible to participate. Kish tables were used so that all household members had an equal chance of being selected. A standardized questionnaire to collect data for the WHS was developed and a consistent translation procedure was employed to ensure cross-country comparability. Information was obtained through face-toface interviews conducted by trained interviewers who received training before going to the field and who followed standard protocols.