dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, randomization protocols could be built into the database systems of renal registries. When a new patient who fulfils prespecified inclusion criteria is reported to the registry, he would be randomized to an intervention arm and asked for consent. The intervention must carefully be selected and should be safe and inexpensive, for example, comparing two compositions of dialysate. As the inclusion criteria are usually less stringent than in conventional RCTs, the results may be more generalizable. As the follow-up data are obtained from registries, the cost is typically low. One successful example is a Swedish study on thrombus aspiration during myocardial infarction, which utilized a national registry to recruit and follow-up a large number of patients at low cost  [29]. Registry-based RCTs could be performed in some national renal registries in collaboration with the ERA-EDTA Registry with especially high-quality data, potentially providing very valuable information that could change treatment policies in nephrology.
In addition, many European paediatric nephrology centres participate in the global 'International Paediatric Dialysis Network'  [40]. The main causes of CKD in children are Congenital Anomalies of Kidney and Urinary Tract and hereditary nephropathies. Continuous advances in cell and molecular biology techniques have revolutionized the knowledge, diagnosis and sometimes even prognosis of many inherited kidney diseases. As the rarity of these diseases creates a barrier for clinical research, several European networks, registries, disease-specific databases and bio-repositories have been created to overcome this issue, with major clinical inputs in genetic counselling, phenotypic refinement or genotype-phenotype association, prognostic value and eventually mechanistic insights leading to therapeutic approaches  [41] [42] [43] [44].
Such indicators may overlap with existing quality of care measures, already captured by existing datasets. One key future goal may require inclusion of new indicators as part of prospectively collected datasets in different cohorts and particularly in the ERA-EDTA Registry. To this end, the QUality European STudies initiative  [53] taken over by the Registry almost 15 years ago-may be revitalized. For any such exercises to be informative, long-term involvement of experts in implementation science will be needed. Moreover, the promotion for a further and close collaboration between the two ERA-EDTA entities, the ERBP experts and the ERA-EDTA Registry staff, should become a key goal in the future.
It was felt that the ISN had successfully established a clinical trials infrastructure Advancing Clinical Trials (ACT), and those lessons could be learned from that while avoiding duplication. Big data. Traditionally, Scandinavian countries and those with nationalized healthcare systems provide an excellent opportunity to use routine health and social care records to describe associations between practices and outcomes in general, un-selected, populations. There are also, however, some potentially useful general population-level linked datasets in Spain, France and Switzerland. The new general data protection regulation makes the rules clearer and more consistent and makes it less difficult to establish a lawful basis for academics to process personal data for clinical research. There is also heterogeneity in clinical practice within Western Europe that provides an excellent opportunity to use observational data to explore the consistency of associations and even the causal nature of relationships.
It should also be recognized that big data research extends far beyond linking health and social care records for research; it can include data from scans, free text and wearable devices, genomics, proteomics, metabolomics and exposomics. Both types of big data research can involve the use of prior hypotheses and statistical models or agnostic approaches such as machine learning and artificial intelligence. There is certainly world-leading statistical expertise in Western Europe with considerable potential to apply novel methods of analysis to nephrology if interesting research questions and datasets can be presented to them. There is less of a track record of machine learning and artificial intelligence in nephrology in Western Europe, but the methodological expertise undoubtedly exists. It was felt that the ERA-EDTA could support the development of world-leading big data research in a number of ways: • establishing a network of researchers with awareness of and access to routine healthcare databases in different 1476 Z.
A. Massy et al. countries to facilitate international studies that validate findings and mine heterogeneity; • arranging standalone workshops that bring together nephrologists and methodologists to examine specific topics; • facilitating the training of the future big data researchers through this network and statistical/epidemiological training courses that take things beyond the introductory level; • encouraging the adoption of information standards as part of renal IT systems to improve data quality for clinical care and research; • educating the public, policy makers and funders on the importance of kidney disease and in particular the personal and societal burden of ESKD, which often gets overlooked due to its infrequent occurrence; • dedicating a session at the ERA-EDTA conference to innovations in big data analysis; • working out recommendations for which parameters would be essential to be included in kidney big databases; and • stimulating health-economically oriented kidney research (nephrology traditionally being not being keen to investe in this area, whereas nephrology is consuming proportionally large chunks of health care budgets).
Since all of the above-mentioned difficulties have potential negative effects on various aspects (identification, prevention and treatment) of CKD, actions to be taken may or should target to improve each of these items. Some of these actions are more easily achievable (i. e. increasing public awareness, encouraging-supporting studies on clinical nephrology and epidemiology, improving nephrology training), whereas others are more difficult to achieve (i. e. limited funds and lower standards for healthcare and problematic cooperation with authorities). Suggestions by ERA-EDTA and/or EKHA may help contribute to convincing authorities to make changes, at least in some countries. To conduct collaborative research designed to uncover non-invasive diagnostic tests or new predictive markers for kidney disease complications, aimed to further improve the classification and prognosis of kidney diseases based on large-scale omics data using available European patient cohorts (ii) To review the feasibility and relevance of the development of CKD Stages 4-5 registries based on on-going experiences at national level and explore if and how these can be brought together for quality assurance and research at the European level (iii) To plan a successful transition process from paediatric to adult care of CKD by active collaboration between paediatric and adult nephrologists and cohorts/registries (iv) To reinforce the collaboration between the ERBP experts and ERA-EDTA Registry staff in order to extend the number of indicators to include the thresholds of the ERBP recommendations as part of prospectively collected datasets stemming from different national or regional cohorts and from the ERA-EDTA Registry and to adapt those as new recommendations are published (v) To continue supporting EKHA with its established links with the EU, allowing it to target key nephrological topics prioritized by patients and professionals, such as better quality of life in KRT patients and improving kidney transplantation and home treatment modalities, which also include regenerative and personalized medicine (vi) To support the development of world-leading big data research in a number of ways including the creation of data networks and the development of educational programmes (vii) To help the Eastern European nephrology community to optimize patient care and patient-oriented research in their countries by increasing public awareness, encouraging/supporting clinical nephrology and epidemiology studies and improving training in nephrology (viii) To create a European network of kidney units in order to extend our understanding of AKI progression and complications, including transition of AKI to CKD NPPC propositions to stimulate research Target population.
and the 'Antibiotic Prophylaxis and Renal Damage in Congenital Abnormalities of the Kidney and Urinary Tract' (PREDICT) trial [ClinicalTrials. gov. Antibiotic Prophylaxis and Renal Damage In Congenital Abnormalities of the Kidney and Urinary Tract (PREDICT). Accessed at: https://clinical trials. gov/ct2/show/NCT02021006]. The findings of these projects concerning the management of how to improve the outcome of CKD have often been translated into clinical practice guidelines and implemented in routine clinical settings. ESKD in children is a specific and extremely challenging condition that requires large-scale, prospective and cohort studies. In 2007, a new European registry of paediatric KRT, the European Society for Paediatric Nephrology (ESPN)/ ERA-EDTA Registry, was launched. Since then, there has been a progressive increase in the number of European countries providing individual patient data to the Registry, with 38 countries-the whole of Europe-currently participating, covering a general population of more than 100 million children 15 years old.
Let Ψ = [ψ 111 ,. , ψ juw ,. , ψ JU J W JU J ] T be the numbers of newly arrived patients, and the post-action state for {s, a} is denoted by H(s, a) = [h sa 111 ,. , h sa juw ,. , h sa JU J W JU J ] T where h sa ju,w+1 = n juw -m juw for any j, u and w < W ju , then we have s = Ψ + H(s, a). Further, the approximate greedy action â(s, Θ) minimizes the approximate Q-value for state-action pair {s, a}: (n juw + 1) (13) As the number of patients in the waiting list is not bounded, A(s) can be extremely large. Therefore, enumerating all the feasible actions in A(s) for each visited state will consume enormous amount of CPU time and computer resources. Considering that commercial optimization solvers (e. g. , CPLEX, GUROBI) can efficiently solve MP problems, we formulate the process of searching for â(s, Θ) as the following MIP problem: o 2 J j=1  l j Uj u=1 Wju w=1 m juw   -R (18) 0 m juw ∈ Z, ∀j, ∀u, ∀w (17) 0 o j 1 , o 2 ∈ R, ∀j (19) The objective function (  14 ) is derived from (  3 ) and (  12 ) by removing the terms that are independent of a.
Questionnaires Online questionnaires were developed by the investigators using C# (ASP. net) framework (version 2. 0; Microsoft Corporation, WA, USA) together with Visual Studio software 2008 (Microsoft Corporation, Redmond, WA, USA). Data storage was externally managed using SQL server software (version 2008 R2; Microsoft Corporation, Redmond, WA, USA). Both questionnaires were structured and consisted of multiple choice, yes/no, and open-ended questions (103 for physicians and 44 for parents) grouped into predefined sets. Physician questionnaires included (Additional file 1): birth and current infant characteristics; parents' allergy history, as defined by Kjellman  [17] ; functional gastrointestinal disorders (FGID) according to the Rome III criteria  [18] ; infant feeding practices since birth and currently, based on a 24 h recall; dietary advice given in relation to any breastfeeding, use of infant formula, the introduction of solid foods, or a possible FGID; and the time spent providing this advice.
Data analysis Results were given as the number, percentage, and median with the interquartile range (Q1-Q3). The Stat Process of SAS 9. 2 (SAS Institute, Cary, NC, USA) was used for statistical analyses. Pearson's chi-squared or Fisher's exact tests with the Monte Carlo algorithm were used for the qualitative categorical and binary data, the Kruskal-Wallis test was used as a non-parametric test for the quantitative data. To compare doctor and parent's perception on the same item McNemar's test was used for the paired nominal data, and Cohen's kappa coefficient (κ) measured inter-rater agreement for the qualitative (categorical) items. A significance level of 0. 05 was used, with a 95% confidence interval.
Statistical Analysis The statistical data was collected in a spreadsheet and analyzed using IBM SPSS software version 18. 0 (Statistical Package for Social Services, Chicago, IL, USA). Descriptive statistics were performed with at sorting: frequencies for the qualitative variables, means and standard deviations for the quantitative variables. The effect of different variables on the presence of a gingival increase was studied. Some variables were "binarized" when the numbers were too small. The plaque index and the gingival index were dichotomized in "visible plaque": yes / no and in "gingival bleeding": yes / no. The increase was considered "present" if it concerned at least 4 papillae  [15]. It was analyzed thanks to cross-tabulations and Chi-square test for qualitative variables. For quantitative variables the t-test was used.
Discovered in 1898 by Pierre and Marie Curie, 210 Po is a natural radionuclide occurring in the environment, with its precursors, i. e. radon-222 and lead-210, being the penultimate daughter of natural uranium-238. Of the 33 polonium isotopes, 210 Po is one of the most abundant. Due to its very high specific activity (1. 66 10 14 Bq. g -1 ), 210 Po is a substance extremely toxic to humans. Just a few micrograms are sufficient to cause death. This metallic substance is some one million times more toxic than sodium or potassium cyanide  (Al-Masri et al. , 2004; Ansoborlo et al. , 2012). The issue of its bioavailability is directly correlated with the species' chemical form, which depends on environmental characteristics  (pH, Eh, organic carbon content, etc. ). This correlation between speciation and bioavailability remains puzzling given the current state of knowledge on polonium behavior (notably as regards the limited thermodynamic databases describing surface complexation reactions).
Gamma Cartography Gamma cartography was conducted at the study sites in order to identify areas of high activity. Two gamma ray maps of the sites were derived from a gamma ray sensor with a Colibri device (Canberra, Inc. ). The data were acquired automatically every five seconds. These measurements were recorded in May 2017 for the 'Granges du Colonel' site and in November 2017 for 'Rophin'. The gamma mapping images and photographs were superimposed using the QGIS (Quantum Geographic Information System) software.
Subjects who chose a cochlear implant are thus randomised between two arms: in the first one ("CI" arm), a cochlear implantation is performed within a month and a new evaluation on quality of life, binaural hearing performances, and tinnitus severity is planned 6 months after CI activation. Subjects of the second arm ("observation" arm) simply undergo the new evaluation 6 months after randomisation, without any intercurrent treatment for SSD/AHL. For obvious ethical reasons, subjects of the second arm benefit from a cochlear implantation after this 6-month evaluation. Data from all the subjects included will be anonymised, collected and analysed. If a serious adverse event does not allow the end of the protocol to be reached and/or if a subject withdraws his/her consent, the data which were collected until this event will also be taken into account in the final analysis.
The EQ-5D-3 L is translated and validated into the French language and it has a utility function calculated on the basis of known preferences of the French population  [38]. The utility-preference approach of the EQ-5D-3 L provides a cardinal measuring instrument available to calculate the cost-utility ratios. Healthcare costs will be assessed from the French health insurance perspective  [39, 40]. Direct medical and non-medical costs will be included in this study. Costs related to productivity loss will also be analysed. Direct medical costs correspond to hospitalisation costs, outpatient costs (i. e. visits and medical acts, paramedical acts), medication and medical device costs, especially the cost of the CI. Non-medical costs include transportation costs. Data linked to the number of days the patient has missed work will also be gathered from the French health insurance databases.
Costs will be estimated by multiplying the number of units used for each resource with the corresponding unit cost. Consumption of healthcare resources will be retrospectively gathered from the French Social Health Insurance databases, using a bottom-up approach. Administrative data corresponding to the name, surname, date of birth, place of living and gender were recorded for patients included in the study. Inpatient stays will be valued using the French Disease Related Groups (DRGs). Outpatient care, which includes visits, medical and paramedical acts, will be valued using the tariffs reimbursed by the French health insurance. Visits and paramedical acts will be valued using the French General Nomenclature of Professional Acts. Medical acts will be valued using the French Common Classification of Medical Acts, except for laboratory tests for which valuation will be based on the Nomenclature of Biological Acts.
Speech understanding in noise Speech recognition in competing noise is measured using the French Matrix test  [44]  in sound field (IAC 120A-1 sound booth). The French Matrix test is a closed-set sentence test that uses 50 well-known words in French. Each sentence has the same syntactic structure: name -verb -number -objectcolour, for example: "Felix draws six blue bikes". The number of combinations of five words is large enough to eliminate memory-based responses. Speech and noise signals are generated from an IBM PC running the OMA software (www. hoertech. de) and presented via loudspeakers and amplifier (Studio Lab, SLB sat 200). Speech signal is presented at a fixed level of 65 dB SPL and the level of competing noise is adjusted using the adaptive procedure described by Jansen et al. to obtain the signal-to-noise ratio in dB for 50% correct word recognition (SNR50).
Statistical methods, data reporting and analysis Once the validity of the database is double-checked, descriptive analyses will be carried out for each treatment option (observation, CROS hearing aids, bone conduction device, cochlear implant). Quantitative variables will be described as means, standard deviations, and percentile distribution. The number and proportion of subjects will be used to describe categorical data. Regarding the randomised controlled trial phase comparing CI versus observation, the analysis will be conducted on all subjects who were randomised on an intention-to-treat basis. For each outcome (quality of life, hearing performances indicators), the value measured at 6 months will be compared between the two groups using bivariate analyses (Student's T test or Wilcoxon rank sum tests) and linear regressions adjusted for the baseline value.
For each intervention, we will assess the associations between changes in hearing performances and changes in quality of life. Data will be presented according to the Consolidated Standards of Reporting Trials (CONSORT) Statement  [47, 48]. In order to test the robustness of the ICUR, deterministic and probabilistic sensitivity analyses will be conducted. As part of the sensitivity analysis, we will determine the robustness of the results by testing the impact on the outcome of the variation of different cost and utility parameters  [40]. Probabilistic sensitivity analysis, performed using the non-parametric bootstrap method, identified the uncertainty around the ICUR by estimating its confidence interval  [49]. Moreover, a Cost-Utility Acceptability Curve (CUAC) will be built to summarise the impact of uncertainty on the ICUR in relation to possible values of the cost-effectiveness threshold  [50].
Data We analyze data from the Performance Monitoring Accountability 2020 (PMA2020) fourth round survey collected in Burkina Faso. A network of locally trained female resident enumerators collects the nationally representative data. The resident enumerators administer face-to-face interviews, recording data on cell phones to track key family planning indicators under the Family Planning 2020 initiative, which aimed to enable 120 additional million women to use contraceptives by the year 2020  [23]. Since 2013, PMA2020 surveys have been conducted every 6 to 12 months in 11 countries (http://www. pma2020. org). The surveys in Burkina Faso use a two-stage stratified cluster design, starting with a selection of geographical sample clusters based on probability proportional to size in each of the urban and rural strata followed by a random selection of households within each cluster.
Detailed sampling methods and procedures are available elsewhere  [24]. The surveys include household and female interviews. Household interviews collect basic demographic data on all household members and household characteristics. Within each sampled household, all women 15 to 49 years old are eligible for the interview after their informed consent. The female survey collects information on women's background characteristics, contraceptive use and reproductive health. Regarding contraception, women are asked if they have ever heard of each available contraceptive method in the country. Then, women are asked if they (or their partner) are currently using anything to prevent or delay pregnancy. Women who report using a method are further asked to specify the type of methods currently used.
In Burkina Faso, PMA2020 has collected five rounds of nationally representative data since 2014. The fourth survey was conducted between November 2016 and January 2017. A total of 2751 households and 3215 eligible women were interviewed. The response rates were 95. 4% for the household interview and 95. 4% for the female interview  [25]. Ethical approval was obtained from the Johns Hopkins Bloomberg School of Public Health's Institutional Review Board as well as the Comité d'éthique pour la recherche en santé in Ouagadougou, Burkina Faso. Datasets are available to the public for research purposes  [24, 26].
A study in Nigeria assessed the relationship between maternal health service utilization, including contraceptive use, and cell phone ownershipbut focused on women who had children under 2 years of age, who tended to be younger than overall women of reproductive age, in five states in the country. Nonetheless, their results were consistent with our findingswomen who did not own a phone had half the odds of using modern contraception compared to women who owned a phone  [29]. Of note, we measured women's ownership of cell phones instead of household ownership. Cell phone ownership has typically been collected at the household level by national surveys. Studies examining cell phone ownership or access among females in SSA are relatively rare. The scant literature reveals a substantial ownership difference by gender: cell phone ownership data from Niger indicates a gender gap by 45% points  [30].
Although the data were collected in November 2016, changes in the profile of phone owners are likely to occur even in a short time frame. The value of owning a cell phone is well understood by women  [30] ; thus, we expect increases in levels of ownership among women, albeit growth will be slowest among poorest women. Finally, while differential contraceptive practices by cell phone ownership identified potential limitations of mHealth programs in reaching women in general, we did not assess its implications in reaching women with unmet need for family planning. With still relatively low modern contraceptive prevalence in Burkina Faso, demand for family planning may correlate strongly with socioeconomic characteristics and, thus, cell phone ownership.