dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, of the models in this study, only the IFS currently employs the global source dataset generally considered to be the highest quality (SRTM30). Yet recent as-yet unpublished work has revealed that the choice of source dataset has a significant effect on orographic drag but cannot explain the total spread seen among models. In other recent work, the degree of orographic smoothing employed has been found to have a considerable effect on the resultant orographic fields and consequently on modelled drag, yet the appropriate amount of smoothing, and precisely how and at what stage in the orography data processing this smoothing is applied, remains unclear. Addressing this uncertainty requires consideration of what scales are being resolved, and consequently which scales require parameterisation.
Acknowledgements: This work was funded by the  Met Office  and  NERC , the  Natural Environment Research Council , grant reference  NE/N009754/1  (the  Iceland Greenland Seas Project ). The authors would like to thank two anonymous reviewers for their constructive comments, which helped to improve the manuscript. We are grateful to  Anton Beljaars  for his ideas and feedback on the paper and for contributing the schematic from which Figure 1 is adapted. Keith Williams  and  Steve Derbyshire  are thanked for valuable discussions on orographic drag parameterisation, and  Georgie Bennett  for advising on matters of geology relevant to this study. Martin Koehler 's work in providing data is also acknowledged. Each model's GSO and SSO fields (the data used for Figures 2 to 6) are archived in UEA's Research Data Repository at https://people.
uea. ac. uk/a_elvidge/datasets. The meteorological data used to force the offline drag parameterization was derived from MetUM simulations carried out on the joint  Met Office/NERC  MONSooN computing system. This data requires a large tape storage facility, and has been archived through the Met Office mass storage system; accessible through the  STFC-CEDA platform JASMIN  (Lawrence et al. , 2013). The code used to generate the offline drag parameterization results (shown in Figures 7 and8) is the LM97 scheme as implemented in the MetUM (described in the Appendix and, in more detail, in Webster et al. , 2013). IFS simulations were carried out on the ECMWF Cray computing system, and the forecast data used to generate Figures 9 and 10 are available from UEA's Research Data Repository at https://people.
Assessment of Quality and Bias The assessment of risk of bias in the context of this systematic review is complex. There is no standard tool for assessment of risk of bias in guidelines-and by design, HTA guidelines represent the opinion and position of the health-policy framework that they form a part of. Similarly, the included peer-reviewed literature identified problems and made recommendations, which will inevitably be the opinion (however valid) of the authors. For this reason, risk of bias in these studies was assessed through identification of funding bodies and author disclosures. No formal scoring of these parameters was performed but the data are provided to give a perspective on the validity of the information provided by each study.
Outcomes and Measures Potentially eligible patients will be identified by their treating clinician and classified as either functioning or nonfunctioning based on their clinical diagnosis. At the time of consent, patients will complete a baseline questionnaire, and thereafter a 6-month follow-up questionnaire. Profiles of the psychosocial, QoL, and clinical characteristics of patients with functioning and nonfunctioning NETs will be developed based on patient-reported outcome data and clinical data extracted from medical records at baseline and 6-month follow-up. Those participants who consent to the opt-in interview will be contacted by the study team to complete a semistructured interview. The following measures form part of the patient questionnaires:.
Demographics and Clinical Variables A range of demographic and clinical information will be collected pertaining to the individual's characteristics (eg, age, gender, language, living arrangements, postcode, occupation, work status, and level of income). Clinical data collection will occur at baseline and 6 months post baseline and will include a medical record audit to identify clinical information such as date of diagnosis, presence or absence of functional syndrome, primary site, grade, list of and frequency of diagnostic imaging, primary treatment details, length and number of hospital stays, involvement with hospital services, or referrals at the time of recruitment. At follow-up, information pertaining to the participants' status-alive and disease free, alive with disease, lost to follow-up, or deceased (including date and cause of death and autopsy [if performed])-disease progression/recurrence (including date of detection) and progression-free survival; as well as any additional diagnostic imaging and additional treatments details will be collected.
Qualitative Semistructured Interview Patients will be asked to participate in an optional semistructured phone interview to discuss their experiences, care preferences, and information needs as a person with a NET. This interview will be conducted over the phone by a trained interviewer. On the basis of the aims, semistructured open-ended questions have been developed to allow participants to share their experiences and preferences. Given the semistructured format, additional questions and prompts will be used as necessary, as it is anticipated that themes/issues will be identified during the interview. This will allow the participant to share their individual experiences. It is anticipated that approximately 20 participant interviews will be completed for each NET group, however, to ensure that no more than a feasible amount of patients are recruited to the semistructured interview, the opt-in check box will be removed once enough participants have been recruited to achieve saturation of qualitative data themes.
Quantitative Analysis of Patient-Reported Outcome Measures, Demographics, and Clinical Data Descriptive statistics will be used to summarize demographic and clinical characteristics and patient-reported outcomes by group at baseline and follow-up. Statistics will include counts and percentages for nominal valued variables and means and standard deviations or medians and interquartile ranges, as appropriate, for continuous valued variables. Dichotomous valued variables will be compared between groups using Fisher exact test. Nominal valued variables will be compared between groups using Pearson chi-squared test of independence. Responses to patient-reported outcome measures at baseline and follow-up will be compared between groups using independent samples t tests; between-groups differences on EORTC QLQ-C30 scales and items will be interpreted using evidence-based guidelines  [50].
In the absence of evidence-based guidelines for the SCNS and PROMIS measures, the Cohen d effect size will be calculated and interpreted using existing conventions  [51]. If a nonparametric method is required, differences in medians will be examined using the bootstrap percentile method  [52]. The number of bootstrap replications will be set at 10,000. All data will be entered in SPSS version 23 or higher (Chicago, IL), and SPSS will be used for scoring, descriptive analysis, and parametric tests. If a nonparametric method is required, data will be imported into R version 3. 3. 3 (or higher), and the R package pairwise CI will be used for this purpose  [53]. Alpha will be set at. 05 for all analyses, and all tests will be 2-tailed.
In addition, the Unicorn Foundation website offers a range of online resources such as updates on current NET research, access to support groups, and information on NETs. The accessibility to this service nationally makes it an optimal avenue for disseminating and implementing evidenced-based approaches to provide better quality of care for those accessing information. This study will expand upon this service and provide a more structured response for patients with information that is tailored to meet the needs of NET patients based on their clinical characteristics and from information that has been derived from both clinical and patient-reported outcome data collection.
Development Framework In line with the Schofield and Chambers framework, the design and development of the intervention will be guided by the 7 key features required to achieve effective translation into usual care and practice  [40] : 1. Targeting cancer type and stage: To gain an understanding of the problem, patients recruited to the study are classified as either functioning or nonfunctioning by their treating oncologist based on their clinical presentation of distinctive hormonal syndromes and symptoms associated with functioning NETs  [9, 11, [19] [20] [21]  versus the asymptomatic profile of nonfunctioning NETs  [23]. Intervention content will be developed based on quantitative and qualitative data on patient experiences and care preferences. The intervention will target the 2 subgroups and provide tailored supportive care around aspects of information on their disease, symptom burden, and self-care management.
Using this method of delivery adopts a low-intensity approach, improving efficiency by being a tool that can be easily integrated as an ongoing clinical service. 5. Ensuring evidence-based and theoretical grounding: The content of the intervention will be based on theory and available evidence. Content development will be based on analysis of both quantitative and qualitative data collected in the prospective cohort study that will describe the psychosocial, QoL, and clinical characteristics of patients with functioning and nonfunctioning NETs. Patient experiences and care preferences reported during semistructured interviews will also form part of the deliverable content of the intervention. The delivery mode is likely to incorporate media visual, textual, and diagrammatic resources and one-to-one telephone support.
Data Monitoring The study has received ethics approval from the Human Research Ethics Committee of Peter MacCallum Cancer Centre. No significant risks to participants are anticipated. Questionnaire responses will be scored within 3 working days of being received, so as to ensure that any high distress scores reported in questionnaires will be reported to the treating clinician and/or nurse coordinator in a timely manner. Participants may then be referred to receive treatment from a qualified psychologist; however, this procedure will be determined by, and is dependent upon, each site's standard of care with respect to referral procedures.
Results Currently, the project is progressing through phase 1 and has completed recruitment. A total of 138 participants have been recruited to the study. To date, we have patient-reported outcome data from 122 participants at baseline and 87 participants at 6-month follow-up. Of these, qualitative data have been collected from 35 participants who consented to a semistructured interview. Phase 2 and phase 3 of the project are yet to be completed.
In the past decade, efforts have been made to quantify the burden of disease attributable to specific dietary factors. 10-19 These efforts, although useful, had several important limitations, including insufficient geographically representative data on dietary consumption, inaccurate characterisation of population distribution of dietary intake, insufficient accounting for biases of different sources of dietary assessment, standardisation of the intake to 2000 kcal per day, and insufficient accounting for within-person variation of intake of dietary factors. To address these limitations, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we systematically collected geographically representative dietary data from multiple sources, characterised the population distribution of intake for 15 foods and nutrients among adults aged 25 years or older across 195 countries, estimated the effect of each individual dietary factor on NCD mortality, and quanti fied the overall impact of poor dietary habits on NCD mortality.
Selection of dietary risk factors We selected 15 dietary risk factors (table) that met GBD selection criteria for risk factors. 10-13 These criteria include the importance of the risk factor to either disease burden or policy; the availability of sufficient data to estimate risk factor exposure; the strength of the epidemiological evidence supporting a causal relationship between risk factor exposure and disease endpoints, and availability of data to quantify the magnitude of this relationship per unit of change in the exposure; and evidence supporting the generalisability of the effects to all populations. The process of evaluation of the strength of epidemiological evidence for the causal relationship of each diet-disease pair is described elsewhere 10-13 and summarised in the appendix.
Dietary intake at the population level We did a systematic review of the scientific literature to identify nationally or subnationally representative nutrition surveys providing data on consumption of each dietary factor (appendix). We also searched the Global Health Data Exchange website for nationally or subnationally representative nutrition surveys and household budget surveys. Additionally, for food groups, we used national sales data from Euromonitor and national availability data from United Nations Food and Agriculture Organization food balance sheets. For nutrients, we used data on their national availability from the Global Nutrient Database. 20 For sodium, we collected data on 24 h urinary sodium, where available. For trans fat, we used sales data from Euromonitor on hydrogenated vegetable oil.
The list of all dietary data sources used in GBD 2017 is publicly available at the Global Health Data Exchange website. For each dietary factor, we computed a data repre sentativeness index as the fraction of countries for which we identified any data on the risk factor exposure (table). Our dietary data were from multiple sources and were affected by different types of biases. We considered 24 h diet recall as the gold standard method for assessing mean intake at the population level and adjusted dietary data from other sources accordingly (appendix). Some types of dietary data (ie, availability, sales, and household data) were only available for all-age groups and both sexes. To split these data into standard age-specific and sex-specific groups, we first estimated the global age and sex patterns of intake using data from nutrition surveys and then used those patterns to split the availability, sales, and household data.
Effect size of dietary risks on disease endpoints For each diet-disease pair, we used data from published meta-analyses of prospective observational studies to estimate the relative risk of mortality and morbidity. 21 For diet-disease pairs for which evidence was only available on morbidity, we assumed that the estimated relative risks were also applied to mortality (appendix). Considering the relationship of diet and metabolic risk factors and the well established age trend of the relative risks of metabolic risks for cardiovascular disease and type 2 diabetes, we used the age trend of the relative risks of metabolic risk factors 22 to estimate the agespecific relative risk of dietary risks for cardiovascular disease and type 2 diabetes (appendix). To estimate the impact of sodium on outcomes, we first estimated the relationship between urinary sodium and change.
Research in context Evidence before this study We systematically searched MEDLINE and the Global Health Data Exchange (GHDx) to identify studies providing nationally or subnationally representative estimates of consumption of 15 foods and nutrients. We included only studies reporting data collected between Jan 1, 1980, and Dec 31, 2016, in one of the 195 countries included in this analysis. Studies were excluded if done with non-random samples or among specific subpopulations. We estimated the potential health effects of each dietary risk by use of the Global Burden of Diseases, Injuries, and Risk Factors Study comparative risk assessment approach.
Many countries do not have local food composition tables and rely on data from food composition tables from other countries (eg, US Department of Agriculture food composition tables). Additionally, the recipes of mixed dishes as well as formulation of the food products, particularly their content of fat, sugar, and sodium, varies across countries and over time, which makes estimation of the true intake of nutrient more challenging. Our systematic evaluation of epidemiological evidence shows several important limitations in existing dietary relative risks. The effect sizes of the dietary risk factors on disease endpoints were mostly obtained from metaanalyses of prospective observational studies. Although many of these dietary relative risks have been adjusted for the major confounders (eg, age, sex, smoking, and physical activity), the possibility of residual confounding cannot be excluded.
Other potential limitations should also be considered in interpreting and using the findings of our study. We did not evaluate the effect of other forms of malnutrition (ie, undernutrition and obesity). The epidemiological evidence supporting a causal relationship between dietary risks and disease endpoints were mostly from observational studies, and the strength of evidence was generally weaker than the strength of evidence sup porting a causal relationship between other estab lished risks factors (eg, tobacco use and high systolic blood pressure) and chronic diseases. Additionally, the strength of evidence varied across foods and nutrients. Dietary data were from mixed sources and were not available for all countries. These factors increase the statistical uncertainty of our estimates for exposure to dietary risks.
For sodium, we did not include data from spot urine sample, which resulted in a lower data representativeness index for sodium than that of other dietary risks. In estimation of the NCD burden of diet, we assumed that the distribution of dietary factors is independent within each unit of analysis (ie, country, age, and sex group), which might have resulted in underestimation or overestimation of the combined effect of dietary factors. To quantify the effect of correlation of dietary factors, we used individual-level data from the US National Health and Nutrition Examination Survey and estimated the overall burden of dietary risks (ie, joint population attributable fractions) with and without taking into account their correlation. The absolute difference in the joint population attributable fractions, on average, was less than 2%.
Acknowledgments The work of HB was financially supported by  Mazandaran University of Medical Sciences, Sari, Iran. AB is supported by the  Public Health Agency of Canada. JCF  acknowledges funding support from  Fundação para a Ciência e a Tecnologia , through project  UID/Multi/50016/2013. TL is supported by the  Academy of Finland  (grants number  287488  and number  319200 ). The work of SL is funded by the  German Federal Ministry of Education and Research  (nutriCARD; grant agreement number  01EA1411A ). TM acknowledges additional institutional support from the  Competence Cluster for Nutrition and Cardiovascular Health  (nutriCARD), in  Jena, Halle, and Leipzig, Germany. AES is supported by the  South African Medical Research Council  and the South African Research Chair Initiative by the  National Research Foundation.
MATERIALS AND METHODS Objectives of RA BIODAM. The primary objective of RA BIODAM was to generate data and study resources that would allow the conduct of an analysis to determine the independent predictive validity of soluble biomarkers considered to be high priority candidates for predicting structural damage in RA according to the criteria and protocol developed by the working group. It was agreed that secondary objectives would establish which clinical and laboratory predictors used in routine practice, individually and in combination, have the strongest and the most consistent association with change in radiographic damage. Tertiary objectives aimed to test the effect of treatment on biomarkers, to test statistical models to determine which may be optimal for describing the independent association between the biomarker and radiographic progression, and to establish sample size requirements for future studies of candidate biomarkers.
Study design of RA BIODAM. This was a multicenter, multinational, prospective observational study of patients with RA and fulfilling the 2010 Rheumatoid Arthritis Classification Criteria  30. They were recruited consecutively from rheumatologist outpatient clinics and offices in Canada (n = 9), the United States (n = 5), Israel (n = 1), Denmark (n = 1), France (n = 6), Germany (n = 4), Ireland (n = 1), Italy (n = 6), the Netherlands (n = 4), and Norway (n = 1); trial registration: Assess Structural Damage in Rheumatoid Arthritis Using Biomarkers and Radiography: Clinicaltrials. gov: NCT01476956, clinicaltrials. gov/ct2/show/NCT01476956. The first patient was recruited on October 30, 2011, and the last patient visit was May 17, 2017. Principal study design features were focused on recording change in biomarker following change/institution of conventional synthetic disease-modifying antirheumatic drugs (csDMARD) therapy [methotrexate (MTX), sulfasalazine, hydroxychloroquine, chloroquine, leflunomide] and/or following addition of tumor necrosis factor inhibitor (TNFi) therapy [adalimumab (ADA), etanercept, infliximab (IFX), certolizumab pegol, golimumab] as patients were observed every 3 months over a 2-year time frame.
RESULTS Baseline demographics and disease status. Complete baseline data was available on 571 patients who were recruited from Oct. 30, 2011, and last patient visit was on May 17, 2017. Each center (n = 38) recruited between 1 and 60 patients (median 10. 5) and 439 (76. 9%) had complete 2-year followup. Reasons for discontinuation were subject withdrew consent (52), subject lost to followup (25), major protocol violation(s) with study noncompliance (14), subject noncompliant with protocol (13), serious adverse event (10), other/unspecified (10), worsening of intercurrent medical condition (5), and investigator judgment (3). Baseline patient and disease characteristics comparing completers and noncompleters are shown in Table  1. Overall, the patient population comprised a demographically typical cohort of patients with RA, the majority being female (76%) and with mean age of 55.
DISCUSSION This is the first report of the data from RA BIODAM, which was an investigator-initiated 6-year international collaborative effort to compile a unique resource of clinical and imaging data with biosamples acquired according to an international consensus for the conduct of a prognostic study and standard operating procedures for the handling, transportation, and storage of biosamples. In particular, the procedures developed for RA BIODAM ensured that biosamples from 90% of patients from 10 countries were maintained at -70°C from the point of blood draw and serum extraction to the point of creating aliquots for storage in the central biorepository in Canada. Moreover, the level of patient retention, data collection, and acquisition of radiographs over 2 years was at least comparable to many clinical trials that included on-site study oversight and source data verification  39, 40.