dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The responses were managed using SAS and XL and were analyzed, according to the purpose, grouping different categories of respondents: professionals in the quality teams, patients and parents. During restitutions to the centre teams, reports by centres were produced to share the results and discuss new improvement goals for the care process. To analyse the responses from the point of view of the patients/parents and the professionals, the analysis of the responses on all items of the questionnaire was made for the two groups of respondents: the patient/parent group (N = 12) and the professional group in the quality teams pooled for all teams and all disciplines (N = 64). We first identified the items that achieved a « strong Table  2  Engagement of the patients/parents on the quality team (QT) Information and activation of the patients/parents 1.
Study design This study is a cost-consequence and cost-effectiveness analysis of the ComPAS trial, a multi-country, clusterrandomised, controlled trial taking place between May 2017 and July 2018. We will estimate the total costs of both protocols from a societal perspective, including both service provider and household costs. Data collection for the costing study will take place at the midpoint of recruitment for the main trial, and accounting data will be accessed at the end of the study implementation. Time horizons for the costing study are within the trial period only. Ethical approval for the trial was obtained from the Kenya Medical Research Institute (KEMRI)  (  This protocol describes the methods specific to the economic study alongside the ComPAS randomised controlled trial, following the SPIRIT checklist format (see Additional file 1); further details of the protocol have been published elsewhere  [8].
Sample size There will be 12 health facilities (clusters) in South Sudan and 12 in Kenya; in each country, six health facilities will be randomly allocated as intervention and six as control, with 150 children per clinic. The sample size for the main trial outcome was calculated using an expected recovery rate of 85% based on the average programme statistics for each site, allowing for a 10% non-inferiority margin, with 80% power at the 5% level of significance. As the study is only statistically powered for determining effectiveness across both countries rather than within each country, the economic evaluation will be conducted using pooled costs and outcomes from both sites. Pooled and site-specific costs and outcomes will also be presented in the cost-consequence analysis. Cost data collection will be carried out at all 24 health facilities participating in the study.
For beneficiary costs, an exhaustive sample will be asked a simple survey about cost and time implications of participating in the programme as part of the main trial exit interview. In addition, more in-depth group interviews with beneficiary carers will take place with between four and six purposively selected participants at each clinic (i. e. ~72 participants for each country) (Table  1 ). Interview sample sizes will be determined by data saturation. Survey sample sizes will be dictated by the trial sample size; however, post-hoc sample size calculations for differences in cost and time data will be calculated where possible.
Measuring resources and costs The economic evaluation will be conducted from a societal perspective, measuring programme and household costs. A combination of activity-based costing and ingredients approach will be used to estimate programme costs. See Table  1  for list of activities and ingredients. The timing of data collection points can be seen in Fig. 2. Full start-up costs will not be collected as protocol development has been on-going across previous years and previous studies; however, simple start-up costs relevant for future use, such as stakeholder meetings, trainings, community sensitisation and translation of resources, will be estimated. Note that cost of inpatient treatment for SAM will not be collected as it is not part of the intervention.
Measuring household costs Basic travel time and costs will be measured using survey questions to all study participants during the main trial exit interview. In Kenya, survey data will be collected electronically and stored on a secure server. In South Sudan, survey data will be collected on paper and entered using the same electronic data collection system as in Kenya, which includes appropriate skip logic, value restrictions and data checks. Group interviews with a sub-sample of participants during their enrolment in the study will also be undertaken to consider any hidden or abstract costs to households as well as to calculate potential lost earnings due to time spent at the programme. Participants will be purposively selected to represent both SAM and MAM children as well as a range of travel times.
Analytical methods Incremental cost-effectiveness ratios will be calculated for the primary outcome (i. e. incremental cost per additional child recovered) and for secondary outcomes where a statistically significant difference is found. Potential secondary outcomes for inclusion are treatment coverage, defaulter rate, length of treatment, average weight gain and average MUAC gain. Univariate and multivariate sensitivity analyses will be performed in order to assess the potential best case and worse case variability in costs and outcomes. Areas of potential variability will be determined through exploration of the effectiveness data and the researcher's evaluation of cost data uncertainty following data collection. For example, there could be variability in costs or time-use information provided by staff and other key informants due to recall error.
e. food insecure settings). In addition, as treatment in one study site is government-led and in the other it is partner-led, results from each should be of interest to implementers with a variety of programme structures. One limitation of the study is that the main analysis will present an average of the two sites, therefore masking some of the heterogeneity between sites. Anonymised hardcopies of data will be stored in approved, secured locations in the respective countries; anonymised electronic data for the economic evaluation will be stored on a secure server in the UK, assessable only by the research team. No participant identifiable data will be recorded. Results will be reported following the consolidated health economic evaluation reporting standards (CHEERS) checklist  [25].
Acknowledgements We acknowledge the important contributions of the study participants, the  Ministries of Health in Kenya and South Sudan , and the teams of dedicated staff who provide treatment to malnourished children across all the study health facilities. Specifically, we would like to thank  Bethany Marron ,  Eunice Musyoki ,  Imelda Awino  and  Pamela Onyoo , who have been integral in facilitating data collection across the facilities.
ACKNOWLEDGEMENTS Dr A. L. , Prof. A. V. and  Mr M. W. are the representatives of their respective institutions within the  HEPA Europe (European network  for the promotion of health-enhancing PA) which works for 'Better health and wellbeing in the WHO European region through more physical activity for all people'. All authors are grateful to the members of the  HEPA Europe working group  'HEPA promotion in health care settings' led by  Mr M. W. and  Ms Eszter Fu  ¨zeki. This working group aims to promote healthcare related HEPA activities across the  WHO European Region. Main activities include identifying common concerns, sharing information on development and research, promoting good practice, informing policy and strategy and advocating for evidence-based interventions. The opinions and the data communicated in this article are those from the authors and do not necessarily reflect the views of the  HEPA Europe network.
25, 26 Recent studies have also suggested an inverted U-shaped association between age and number of drugs, with a pronounced decline in the burden of medications after the age of 85 years. 27 However, only few of these studies had the possibility to account for the confounding effect of chronic multimorbidity (confounding by indication), thus most likely overestimating the role of sociodemographic risk factors. 9] [30] [31] [32]  This lack of evidence from longitudinal, prospective studies limits our understanding of the epidemiology of polypharmacy and most likely leads to underestimating the true burden of medication use among older adults. Using register data covering the entire population of older adults in Sweden, this study aimed to measure the prevalence of polypharmacy at baseline, to measure the incidence rate of polypharmacy over time, and finally to investigate the factors independently associated with both prevalent and incident polypharmacy.
Assessment of outcomes Although there is currently no consensual definition of polypharmacy,  33  previous studies have frequently relied on a cutoff point of ≥5 medications to measure its prevalence in the older population. 1, 34, 35 We define "polypharmacy" as the concurrent use of ≥5 medications and "excessive polypharmacy" as the concurrent use of ≥10 medications (ie, distinct substances according to the 5th level of Anatomical Therapeutic Chemical [ATC] classification system). Data on drug use were extracted from the Swedish Prescribed Drugs Register, which collects data about all prescription drugs delivered from pharmacies in Sweden since 2005. 36 s illustrated in Figure  S2 , periods of drug exposure were calculated on the basis of 1) the date of drug dispensing, 2) the total amount dispensed to the patient, and 3) the prescribed daily dose.
Measurement of individual characteristics at baseline Study participants' sex and age were derived from the Total Population Register. 39 Other characteristics were retrieved through record-linkage at the individual level between multiple registers with national coverage in Sweden. Highest level of education was categorized as "primary," "lower secondary," "upper secondary," and "tertiary" education in accordance with the International Standard for Classification of Education (ISCED97), and it was assessed through the Swedish Register of Education. 40 Living arrangement at baseline was defined as either "community-dwelling" or " living in nursing home," using data from the Swedish National Board of Health and Welfare's Social Services Register. This register comprises information about care and services provided by municipalities to older adults and persons with functional impairment across the entire Sweden.
Third, we only investigated the total number of prescribed drugs. Future studies should investigate how specific drug classes contribute to the development of incident polypharmacy over time. Fourth, it may be argued that positioning the baseline in November could lead to observing the effect of seasonality rather than the true incidence of polypharmacy. This hypothesis is however not supported by our data (Figure  S9 ). In fact, drug classes that are most affected by seasonality (eg, antibiotics and cough suppressants) have a low contribution to the total burden of prescription drugs, which explains that the prevalence of polypharmacy is stable across seasons. Fifth, the Swedish Prescribed Drugs Register collects only data about prescription drugs delivered from community pharmacies. This includes drugs prescribed by hospital physicians working in inpatient or specialized outpatient care facilities dispensed by community pharmacies.
Discussion The case presented in this report has demonstrated a link between improvement in hearing and improvement in balance in   a patient who underwent neurorehabilitation following stroke, subarachnoid hemorrhage (SAH), and shunt-treated hydrocephalus. A previous study, using the National Health and Nutrition Examination Survey Database  (2001) (2002) (2003) (2004) , showed that for each 10-decibel hearing level (dB-HL) of hearing loss, the risk of falls for an individual increased by 1. 4  [9]. Applying this association to the ³14. 68 dB-HL increased left hearing thresholds in the patient presented in this report, balance recovery might have been associated with regain in hearing of the affected ear. Considering the complex clinical state of the patient described in this report, some important points must be considered.
Acknowledgments The authors would like to thank the OPTIMOD investigators  Emma Allain-Launay ,  Véronique Baudouin ,  Audrey Coilly ,  Dominique Debray ,  Stéphane Decramer ,  Marc Fila ,  Romain Guillemain ,  Jérôme Harambat ,  Romain Kessler ,  Alain Lachaux ,  Jérôme Le Pavec ,  Anne Maisin ,  Elodie Merieau ,  Clément Picard , and  Martine Reynaud-Gaubert. The authors would also like to acknowledge  Valérie Sementa  and  Valentine Vincenot  (ICTA PM). Daniella T Draper, PhD ,  CMPP  and  Amy MacLucas, PhD  from  Cello Health MedErgy (Europe)  provided editorial support. Editorial support was funded by  Astellas Pharma Global Development.
Statistical Analyses All of the skin lesion areas were analyzed. The data obtained for each lesion area changes (assessed by image analysis) and presented as area (cm 2 ) and as percentage (%) of reduction of the area of lesion were then statistically analyzed. A one-sample Student's t-test was utilized, and the results were analyzed with the statistics software GraphPad Prism Version 6. 0a for Macintosh (GraphPad Software, San Diego, CA, USA).
M. A. C. A. center of the A. O. R. N. Antonio Cardarelli. Thus, in the first phase of the work, three batches prepared into the hospital were characterized in terms of viscoelastic behavior, showing no significant differences with gel previously prepared  [19]. Gels were prepared and stored in hermetically sealed containers, to avoid following growth of microbiological contamination. Microbiological tests carried out at the Laboratorio Chimico Merceologico of Naples confirmed that all the three pilot batches prepared in this study were sterile and suitable to be administrated on damaged skin (data not shown). The clinical protocol was designed according to the 2014 EUPAP/NPUAP guidelines. In particular, following the cleansing of the wound, CHI gel was spread on the skin lesions, then covered with a secondary dressing.
Background: Despite several studies that have estimated the economic impact of Respiratory Syncytial Virus (RSV) in infants, limited data are available on healthcare resource use and costs attributable to RSV across age groups. The aim of this study was to quantify age-specific RSV-related healthcare resource use and costs on the US healthcare system. Methods: This retrospective case-control study identified patients aged ≥1 year with an RSV event in the Truven Health Marketscan® Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits databases between August 31, 2012 and August 1, 2013. RSV patients were matched 1:1 with non-RSV controls for age, gender, region, healthcare plan and index date (n = 11,432 in each group). Stratified analyses for healthcare resource use and costs were conducted by age groups.
Study design and data source This retrospective, case-control study used patient data from the Truven Health Marketscan® Commercial Claims and Encounters, and Medicare Supplemental and Coordination of Benefits databases (Truven Health Analytics, Michigan, USA). The Truven Health Marketscan® databases include longitudinal records of patient demographics, inpatient and outpatient services, long-term care accessed, and prescription drug claims covered under a variety of health benefit plans; it is considered representative of the US health population with regards to health coverage  [28]. The payments reported in the MarketScan® database represent the amount eligible for payment to providers after applying pricing guidelines such as fee schedules and discounts, and before applying deductibles, copayments, and coordination of benefits.
Statistical analysis Descriptive analyses are presented for the RSV cases and the matched controls. For each continuous variable the mean and standard deviation (SD) were calculated and for each categorical variable the frequency and percentage were calculated. Stratified analyses were conducted by age group (1 year, 2-4 years, 5-17 years, 18-49 years, 50-64 years, 65-74 years, 75-84 years, and ≥ 85 years). The total length of stay in hospital, the number of visits/ prescriptions, and the costs for the different resource categories were calculated as a mean per patient taking into account patients without consumption in the calculation. A matched design was used to identify RSV cases and controls and so statistical analyses took into consideration the dependency in the data introduced by matching.
Patient characteristics Of 63,702,072 individuals enrolled in the databases between August 31, 2012 and August 1, 2013, a total of 11,432 patients with RSV met the inclusion criteria and were matched 1:1 with controls without RSV (Fig. 1 ). The two matched populations (RSV cases and controls) had a mean age of 15. 8 years (63. 2% aged < 5 years), 51. 3% were male, and the vast majority were identified from the commercial claims and encounters database (92. 8%) (Table  1 ). In the majority of cases, the index RSV episode occurred during the RSV season (96. 5%), with diagnosis in the outpatient setting (80. 9%). Significantly more RSV patients had a history of RSV during the baseline period compared to controls (9. 2% vs. 1. 3%; p < 0. 0001), and there was a significantly higher proportion of premature patients compared to controls among children aged < 5 years (12.
We were also interested in healthcare utilization and costs in infants aged < 1 year, due to the documented burden of RSV in this population, and particularly wished to explore outcomes by age subgroups (< 3 months, 3-5 months, and 6-11 months). However, among the 31,013 infants aged < 1 year with a RSV diagnosis during the 2012-2013 season, the results were not coherent and did not follow the global trend of healthcare costs. We were not able to identify the potential bias or confounding factors for these unexpected results, and so they were not included in the final analyses. One source of the potential bias may have been related to the process used to retrieve the birth date, as date of birth is not available in the MarketScan® databases; the date of the first claim with an ICD-9-CM code for birth was used as a proxy for birth date (Appendix 2), if this was not available then the first enrolment date was used when year of enrolment and birth year were the same.
Our data on the economic impact of RSV across age groups can be used to re-evaluate cost effectiveness of new vaccines or monoclonal antibodies, especially in the elderly age groups. This was a large observational study of over 11,000 patients ≥1 year of age with an RSV event in the US, and with follow-up data for 12 months. This analysis, covering a wide range of ages, allowed assessment of RSV-related costs in children, adults and the elderly; utilizing age ranges chosen to focus on those age groups with the highest risk. The use of matched non-RSV controls allowed for comparison and estimates of the RSV-attributable resource use and costs, which is lacking in many other RSV costs studies. There were, however, several limitations to this analysis. ICD-9-CM codes were used to identify RSV cases and healthcare resource use and costs in the 12 month follow-up period; miscoding or misclassification or missed opportunities for RSV testing may have occurred leading to a misdiagnosis or incorrect utilization or cost to be attributed.
Similarly, where a patient did not make an insurance claim the associated healthcare resource use or cost would not have been captured in the database. Some outcomes that may be relevant to this analysis, such as mortality or the severity of disease were not available. The completeness of the data on prematurity in those aged < 5 years in the databases is uncertain; prematurity was identified using ICD-9CM and additional diagnosis-related group codes (Appendix 3), however, the proportion of premature child who are not identified by these codes is unknown. The severity of prematurity was also not assessable for almost 40% of premature children in this study. Palivizumab use during hospitalization was not captured in the databases, so not all patients with palivizumab would have been excluded from the analysis.
About the Paper Selection Two electronic databases were searched, PubMed/Medline and Web of Science®. Searches were performed in October and December 2017 to identify peer-reviewed journal articles published in 2017, in the English language, related to HFOI research in medical informatics. In addition to the search of electronic databases, manual searches of key themes were performed in seven major biomedical journals (Journal of the American Medical Informatics Association, Methods of information in medicine, Journal of Medical Internet Research, International Journal of Medical Informatics, Journal of Biomedical Informatics, BMC Medical Informatics and Decision Making, Informatics for health and social care). Keywords used in the searches included both free-text and coded keywords. Consequently, two queries were built: one based on MeSH terms used as major topic in the Pubmed/Medline database, the second one based on free-text keywords searched in titles or abstracts through Pubmed/ Medline and Web of Science ® databases.
Pubmed was queried to test keywords in an iterative process. One of the two section editors performed the literature search. Database searches yielded a total of 695 references. The two section editors independently undertook the initial screening of titles and abstracts to identify papers relevant to the field of interest. Papers were considered according to their originality, innovativeness, scientific and/or practical impact, and scientific quality. Both section editors classified the papers into three categories: accepted, rejected, or pending. They then reviewed in detail the accepted and pending fulltext articles to reach two lists of 18 and 20 candidate papers, respectively. The two lists were then pooled. Eight articles were iden- tical and were kept for further evaluation. The remaining 22 papers were reviewed in detail to get to a consensual list of seven candidate papers, and then a final list of 15 candidate best papers.
Dufendach KR, Koch S, Unertl KM, Lehmann CU A randomized trial comparing classical participatory design to VandAID, an interactive crowdsourcing platform to facilitate user-centered design Methods Inf Med 2017 Oct 26;56(5):344-9 Early involvement of stakeholders in the design of medical software is particularly important due to the need to incorporate complex knowledge and actions associated with clinical work. Standard user-centered design (UCD) methods may limit user involvement to a small number of individuals due to the significant time investment from designers and end users. VandAID, a new web-based crowdsourcing platform, was tested in a randomized trial. The results show that VandAID can simultaneously involve multiple users in UCD and provides means of obtaining design feedback remotely.
Instrumentation and particle analysis The egress of the IV line was connected to a Qicpic® dynamic image analysis device (Sympatec GmbH Inc. , Clausthal-Zellerfeld, Germany) linked to a Lixell module (Sympatec GmbH). The Qicpic® particle analyser with Windows 5. 0 software also determines particle sizes of between 1 μm and 30 mm and provides dynamic imaging analysis  [13]. The pH of each different drug solution and the pH of the solution at the egress of the Qicpic® analyser were measured three times with a pH meter (SB70P Symphony®, VWR International, Singapore).