dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient and public involvement statement Patient or public were not directly involved in the systematic review protocol. However, in the simulation area, patients have a wide place in training: to help the learners to experience a clinical situation within authentic condition; to take part in the feedback; until being 'patient instructor' (PI). Thus, patient feedback on simulation (both on student performance and on their experience of simulation) belongs to the data that will be included in the systematic review. For the randomised controlled trials (RCTs) included, we will note when patients themselves assess the burden of the intervention. We cannot disseminate results to all study participants included because we were not investigators. However, through publications in peer-reviewed journals, national and international conferences, we will try to make the results available for the maximum number of people.
Electronic search A comprehensive search of the following electronic databases will be conducted: MEDLINE, EMBASE, Scopus, CINAHL, PsychINFO, ERIC, the Cochrane Library (Cochrane database of systematic reviews and Cochrane central register of controlled trials (CENTRAL)) and the Web of Science (Science and Social Sciences Citation Index). An epidemiology instructor experienced in systematic reviews (from Cochrane France: AD) and an experienced research librarian (AC) helped to design a Medline search query. Search terms included both MeSH terms and free-text words referring to simulation techniques, psychiatric practice, and mental disorders or symptoms. The search query for MEDLINE is reported in online appendix 1. It will be adapted to the other databases.
Additional search We will search the tables of contents of the following journals for the last 10 years: Academic Psychiatry, Academic Medicine, International Journal of Medical Education, the Journal of Nursing Education, Nurse Education Today, Medical Education, BMJ STEL, Advances in Simulation, Simulation in Healthcare and Clinical Simulation in Nursing. We will search for registers through the International Clinical Trials Registry Platform and websites (such as http://www. aspeducators. org (for the association of standardised patient educators), https://www. tripdatabase. com and http://www. greylit. org/). Finally, we will screen all reference lists for further additional references. No time limits will be set. Databases will be searched from inception. We conducted our search in December 2017.
screening of identified studies The main author (M-AP) will search all databases. Two review authors (M-AP, GG) will independently screen the titles and abstracts retrieved by search. We will obtain full reports for all references that appear to meet the inclusion criteria. The two review authors will then independently screen the full-text reports. All disagreements will be solved by discussion with the help of a third reviewer if needed to reach a consensus. We will use Covidence software and seek additional information from study authors where necessary to resolve questions about eligibility. The review authors will not be blinded to the names of the journals, authors or institutions.
Meta-analysis We will compare the characteristics of the selected studies to determine the feasibility of performing meta-analyses. If meta-analysis is deemed possible, we will use both fixed-effects and random-effects models and compare their results. We will perform meta-analysis with RevMan V. 5. 3 software, generating ORs and 95% CIs for dichotomous data, and mean differences and SEs for continuous data. Forest plots will be produced. Otherwise, when we encounter substantial content or methodological heterogeneity across studies, we will not conduct a meta-analysis but instead will use a narrative approach for synthesising the data. A systematic narrative synthesis will be provided with information presented in the text and tables to summarise and explain the characteristics and findings of the included studies.
Open access moderate (further research is likely to have an important impact on the confidence in the effect and may change the estimate), low (further research is very likely to have an important impact on the confidence in the effect and is likely to change the estimate) and very low (any estimate of the effect is very uncertain). reporting of the protocol and the review This systematic review protocol will be reported according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines  33  and the results according to the PRISMA Statement. 25 he results of the screening process will be provided in detail with the PRISMA flow chart. Quantitative data will be summarised in tables of individual studies, summary tables and forest plots, and qualitative data will be reported in tables and text.
Background: Pneumonia is the leading cause of morbidity and mortality worldwide. Pneumococcal conjugate vaccines have reduced the burden of pneumonia, but data on the current burden of pneumonia and its impact on the healthcare system are needed to inform the development and use of new vaccines and other preventive measures. Methods: We retrospectively analyzed the frequency of pneumonia in the US during 2008-2014 using data from the MarketScan® Commercial Claims and Encounters database. Frequencies of healthcare utilization related to the index pneumonia episode were calculated using the annual number of enrolled person-years (PY) as the denominator and the number of individuals with pneumonia as the numerator. Pneumonia-associated costs were calculated as mean payment per episode during the 2 years from 2013 to 2014.
Study design This was a retrospective analysis of pneumonia-related healthcare utilization in the US. The analysis used data extracted from the Truven Health MarketScan® Commercial Claims and Encounters database  [19]. The co-primary objectives were (a) to determine the annual and monthly frequency of pneumonia-related healthcare utilization from 2008 to 2014 and (b) to determine pneumonia-associated costs in the two most recent years (2013-2014) due to hospitalization, outpatient visits, and emergency department (ED)/urgent care (UC) visits.
Data source and extraction The MarketScan database contains information on US individuals insured commercially (i. e. privately) or through the Medicare program  [19]. The database collects information on paid claims from health plans, employers, and state-level Medicaid agencies using a nationwide convenience sample. It covers all census regions of the US, includes an average of 48,982,662 individuals per year, and has complete longitudinal records of patient demographics, outpatient services, inpatient services, long-term care, and prescription drug claims. The database is considered representative of the US population with employer-provided health insurance  [1, 20]  and is used extensively to understand the burden and healthcare utilization for different illnesses in the country. Records within the database are de-identified and fully compliant with US patient confidentiality requirements, including the Health Insurance Portability and Accountability Act of 1996.
For this reason, ethical approval was not required for this study. In this study, data were extracted from January 1, 2008 to December 31, 2014. Only data from individuals in the enrollment tables were included. Pneumonia episodes were identified as a consultation with a principal diagnosis of pneumonia or with a principal diagnosis of meningitis, septicemia, or empyema in addition to a diagnosis of pneumonia in another diagnostic field  [21]. For outpatient visits, the principal diagnosis was considered based on International Classification of Diseases, 9th revision, Clinical Modification codes in the primary or the secondary position (see Additional file 1: Table  S1 ). In addition, the first consultation had to be more than 28 days after any previous consultation that had the same diagnosis code.
Statistical analysis The annual frequency of pneumonia is given per 1000 person-years (PY) and was calculated as 1000 × [annual number of patients with a pneumonia episode] ÷ [annual number of total enrolled PY in the MarketScan databases]. Monthly frequencies for each year were calculated per 1000 person-months. Proportions were calculated as 100% × [number of index visits for each setting] ÷ [number of total index visits]. Costs related to the index episode were calculated as the mean payment for each episode during the 2 years from 2013 to 2014. The 95% confidence intervals (CIs) for frequencies of pneumonia and costs were calculated using a normal approximation. The significance in the difference in frequencies of pneumonia between 2008 and 2009 and 2014 was assessed by a log-linked Poisson regression with the log of the number of PYs as an offset.
We were unable to find published reports on changes in overall rates of pneumonia for the same period as the current study  (2008) (2009) (2010) (2011) (2012) (2013) (2014) , although data from the National (Nationwide) Inpatient Sample indicated that the age-adjusted in incidence of hospitalized cases of pneumonia decreased in the US by roughly one-quarter between 2001 and 2014  [23]. Also, Tennessee hospital discharge data from 1998 to 2012 indicated that hospitalization for pneumonia in children aged < 2 years decreased by 72% from 1998 (pre-PCV period) and 2012 (2 years after PCV13 was introduced) and by 27% from 2000 to 2010 (PCV7 period) and 2012  [24]. Another study of hospital discharge data found a decrease of 17-21% in children aged < 5 years after PCV13 was introduced  [25]. Data from the Medical Expenditure Panel Survey database indicated a 16% decrease in the incidence of pneumonia from pre-PCV13 period  (2007) (2008) (2009)  to the post-PCV13 period (2011), although the difference was not statistically significant  [26].
However, their estimates reflect all pneumonia-related healthcare consumption within a year, not only the index episode as in our analysis. This current study was strengthened by the large size and representativeness of the dataset, the use of real-world outpatient and inpatient settings, and the multiple years covered. Although we expect that our estimates are reliable, their accuracy could have been affected by misclassifications or measurement errors, for example, due to miscoding, misdiagnosis, or patients not using their insurance. Also, care should be taken when generalizing these results to other pneumonia patient populations with other types of health insurance coverage (e. g. Medicaid) or without insurance coverage.
The interviewers entered the data using tablets equipped with Census and Survey Processing System software (CSPro). A random sample of 10% of questionnaires was taken from Stata 14. 2 and checked for quality purposes by re-interviewing participants by telephone. We observed high consistency between the answers to the face-to-face interview and the telephone interview. The ethics board of the School of Nursing at University of São Paulo approved the study. The 20-minute questionnaire included questions on women's socio-demographic characteristics, sexual and reproductive histories, contraception use at last intercourse, and pregnancy history. Variables considered were age, paid work, education, social status (based on the Brazilian Economic Classification Criteria, which uses a methodology to classify households based on purchasing power of household appliances, householder's education, and family income)  [13] , marital status, private health insurance, number of births, future pregnancy intention, contraceptive use at last sexual intercourse (none or less effective, including barrier/ natural methods and more effective, including hormonal and intrauterine methods), switched to more effective method since Zika outbreak, and used emergency contraception since Zika outbreak.
Most (91%) used text entry and 33% used digital images to help identify foods. Only 65% had integrated databases for estimating energy or nutrients. Fewer than 50% contained any features of customization and about half generated automatic reports. Most tools reported on usability or reported validity compared with another assessment method (77%). A set of Best Practice Guidelines was developed for reporting dietary assessment tools using new technology. Conclusions: Dietary assessment methods that utilize technology offer many advantages for research and are often preferable to consumers over more traditional methods. In order to meet general quality standards, new technology tools require detailed publications describing tool development, food identification and quantification, customization, outputs, food composition tables used, and usability/validity testing.
Introduction The opportunities provided by the internet to link large scale food and nutrient databases with automated dietary recording has led to growth in the number of online dietary assessment tools  [1]. New technologies for measuring diet can be categorized according to the type of technology being used, such as Web-based or online tools, mobile systems (apps), camera-based tools, and other developing technologies, such as consumer purchase data and wearable sensors. Traditional methods relied heavily on self-reporting of foods consumed either using food frequency questionnaires (FFQ) or with paper-based recalls or diaries. All of the traditional methods lacked accuracy as a result of problems including the ability to recall food consumed, difficulties with portion size estimations or limited food composition tables  [2].
Study participants can be invited to take part in research electronically via email or text  [8]. Users of new technology tools can more easily identify foods consumed through interactive searchable databases  [9]. They can provide real-time results and feedback  [1]  and can include enhanced options for portion size description, such as using digital images  [10] , and more relevant lists of branded food items  [9]. It is often not clear how relevant a particular dietary assessment tool is for research as a result of limited information provided on the development process and lack of validation. An evaluation of new technologies to assess diet may help understanding of their potential to replace, improve, or complement traditional methods. Due to the rapid development of new technologies, existing reviews of the area quickly become out of date, including obsolete technologies such as personal digital assistants or PDAs  [11].
Inclusion Criteria and Search Strategy Comprehensive literature searches were conducted to identify articles pertaining to new technologies for dietary intake assessment using key word searches with the following inclusion criteria: (1) publications were in English, (2) articles were published from January 2011 to September 2017, and (3) sufficient information was available to evaluate tool features, functions, and uses. Various search terms were used related to dietary or nutrition surveys, nutrition assessment, and the use of technologies, including mobile apps, Web-based tools, online or Internet tools, and software. PubMed, PLOS, BioMED, Science Direct and Ovid databases were used, each with slightly different search terms (Supplemental Table  S1 ). The searches were limited to articles published after 1 January 2011 because the field of technology development for dietary intake assessment is advancing rapidly, and tools developed prior to 2011 have been previously evaluated  [12].
Evaluation Criteria and Data Extraction The Expert Group, comprised of the authors of this manuscript, identified 25 attributes related to data entry, identification and quantification of foods, customization, output, usability and validity, which were used to evaluate each dietary assessment tool (Supplemental Table  S2 ). Under the heading of Data Entry, we assessed whether the tools relied on text entry, digital images and/or bar-code scanners, and whether they also collected information about health characteristics or physical activity. For the Identification and Quantification of Foods, we assessed whether the foods or beverages were automatically identified from an image or required manual identification, the source of food composition data used, and how the intake amounts were quantified, either by weights or household measures, or estimated from digital images.
In the Customization section, we assessed whether the tool allowed the user to add missing foods, custom recipes or dietary supplements, and whether the program used machine learning to adapt the list of foods to user preferences. Under Output, we considered whether the tool provided data on energy, macro-and micro-nutrient intakes, food groups consumed, time of intake and meal name, and whether the tool generated automated reports. Finally, we assessed Usability and Validity by checking whether there were any reports of user feedback, time to complete the assessment, and whether any validation studies had been conducted. The features of each dietary assessment tool were assessed independently by two members of the Expert Group from details provided in the publications, and any discrepancies were discussed at the Expert Group level.
Search Results The PRISMA diagram showing the search flow and inclusion/exclusion of studies appears in Figure  1. A total of 4695 articles were initially identified. Duplicates were removed and the remaining articles screened (title and abstract) to eliminate those that were not relevant to meet the project objectives, yielding a total of 800 publications related to dietary intake databases, applications, and tools. The goal of this review was to identify unique technology-based tools for dietary intake assessment, including smartphone applications, those that captured digital images of foods and beverages for the purpose of dietary intake assessment, and dietary assessment tools available from the Web or that were accessed from a personal computer (PC). From the 800 articles that mentioned dietary assessment in the title or abstract, 151 were related to new technologies for dietary intake assessment, and of these, 66 were additional references for tools already identified.
Of the 10 tools designed for consumer use, n = 7 (70%) were for adults exclusively, while n = 3 (30%) were designed for all ages. Among all the tools designed for research purposes, n = 17 (52%) collected dietary intake over the previous 24h using dietary recalls; n = 11 (33%) collected food records, while the rest collected intakes via food frequency questionnaires (n = 3; 9%) or imaging systems (n = 2; 6%). Of the 10 tools designed for consumer use, most of them collected food records (n = 8; 80%), while n = 2 (20%) collected food frequency questionnaires. Although all of these tools used technology for dietary intake data collection, not all of the tools automatically coded the intake information to generate energy and nutrients (Table  1 ). Of the tools assessed here, 15 of the 43 (35%) were used for data capture only and required a dietitian or a coder to enter the items and portions in another tool later to estimate energy and nutrient intakes.
These are identified as "not integrated into the tool" in Table  1. Another large difference in the tools was the source of food composition data and the number of items available. Tools designed to assess food consumption frequency (Evident II, Food4Me, GraFFS, IDQC, Oxford WebQ, and WebFFQ) included 135-200 individual line items (individual foods or aggregated food categories). Those designed for children varied, with SNAP and WebCaaFE including a limited list (49 and 32 foods and beverages, respectively), while WebFR and WebDASC included a more extensive list of 550 and 1300 items, respectively. Tools that relied on national food composition tables ranged from about 1000 items to more than 45,000 if branded foods were also included (e. g. , myfood24), and were largely complete with respect to nutrients.
The source of food composition was reported in all but one case, but the number of foods included in the database was missing for six of the tools. The daily time to complete each tool was reported in 18 of the 43 studies. The times ranged from an average low of 14 min to as much as 45-60 min, but most tools were completed within 15-35 min. The use of images also differed considerably among tools. TADA, Snap-N-Eat, and DietCam automatically coded foods and beverages from digital images  [14] [15] [16] , and RFPM used semi-automatic coding of images to facilitate data entry. GoCARB automatically coded carbohydrate content of food categories identified from images. Chest-worn cameras, like eButton or Microsoft SenseCam, captured digital images throughout the day but required subsequent coding by nutritionists for nutrient intake estimates.
Several tools, CHAT, FoodNow, NANA, NuDAM, and TECH, used digital images to enhance reporting of food intakes, along with text or voice recordings. FoodLog used images as a visual diary of food intakes for patients with diabetes, and Microsoft SenseCam used images as a memory aid for food records. FNDDS is the US Department of Agriculture's Food and Nutrient Database for Dietary Studies; FNDDS provides the nutrient values for foods and beverages reported in the dietary intake component of the National Health and Nutrition Examination Survey (NHANES). 5 Australian Food, Supplement and Nutrient Database (AUSNUT). 6 European Prospective Investigation into Cancer and Nutrition (EPIC). 7 WISP (Tinuviel Software) is nutritional analysis software for the UK and Ireland (http://www. tinuvielsoftware.
With regard to the category 'Customization,' research and surveillance tools had proportionally more options to add missing items, customize recipes, and report use of dietary supplements. Research and surveillance tools more frequently provide detailed information on dietary intake in the 'Output' category, particularly for the features 'Food groups', 'Time of intake', and 'Meal name', but fewer of the research tools contained integrated food databases, so lacked the ability to estimate energy or nutrient intakes automatically. In contrast, all consumer access tools we identified generated automatic reports, but only 39% (13 tools) of research and surveillance tools did so. In the 'Usability and validity' category, a higher proportion in tools used for research or surveillance (91%; 30 tools) have conducted validation studies, compared to 30% (n = 3) consumer access tools.
Results from a study using DES were compared with results from a national survey in the same population  [28] , and DAP compared FFQs with 24-h recalls collected using the same tool  [26]. VNP was evaluated by comparing the coding of 24-h recalls with DietPro 5i, a different dietary intake coding software  [77]. Lastly, GoCARB was compared with self-estimates of carbohydrates and carbohydrate intakes calculated from weighed food samples  [100]. Macronutrient intake comparisons were available for 22 of the 25 validation comparisons (Supplemental Table  2 ). Protein intake estimates were the closest between traditional and new technology tools, with 18 comparisons within 5 g of the reference (average 2. 1 g). Three of the protein comparisons were between 5-9 g different from the reference and only one was >10 g.
In the meantime, a number of other reviews have been published. While we deliberately chose to focus on new technologies identified from the published academic literature, other reviews have used app-store downloads as the criteria for selection  [110, 111]. Few of the app-store tools (4%) provided details about the sources of food composition data, and only 14% provided micronutrient estimates  [111]. In contrast, half of the consumer apps in our review used a comprehensive food composition table, and 40% reported on micronutrient intakes. It is clear from the two approaches that apps with publications are more likely to include comprehensive food composition databases and, therefore, can report on a full complement of nutrients, compared to the most popular consumer apps. Image capture can increase accuracy and ease reporting of foods and beverages consumed  [14, 50].