dataesr/scientific-paragraphs-categorization huggingface dataset:
We set up the ICIPEMIR project (Improving the completeness, interoperability and patients explanation of medical imaging reports) as an academic solution to optimize medical imaging reports production. Such a project requires two layers: one engineering layer to build the automation process, and a second medical layer to determine domain-specific data models for each type of report. We describe here the medical layer of this project. Methods. We designed a reproducible methodology to identify -for a given medical imaging exam-mandatory fields, and describe a corresponding simple data model using validated formats. The mandatory fields had to meet legal requirements, domain-specific guidelines, and results of a bibliographic review on clinical studies. An UML representation, a JSON Schema, and a YAML instance dataset were defined.
Based on this data model a form was created using Goupile, an open source eCRF script-based editor. In addition, a graphical display was designed and mapped with the data model, as well as a text template to automatically produce a free-text report. Finally, the YAML instance was encoded in a QR-Code to allow offline paper-based transmission of structured data. Results. We tested this methodology in a specific domain: computed tomography for urolithiasis. We successfully extracted 73 fields, and transformed them into a simple data model, with mapping to a simple graphical display, and textual report template. The offline QR-code transmission of a 2,615 characters YAML file was successful with simple smartphone QR-Code scanner. Conclusion. Although automated production of medical report requires domain-specific data model and mapping, these can be defined using a reproducible methodology.
Field definition: literature-based definition of relevant fields with expert validation We conducted a narrative bibliographic review using the Medline database (on http://pubmed. gov), Google, and Google Scholar, to identify guidelines on medical imaging reporting in general, and more specifically related to targeted exam and indication (here computed tomography for urolithiasis). We also included legal requirement on medical reporting, by screening the French official health agency and health regulation codes. We also searched the Medline database for scientific papers reporting imaging results of patients suffering from urolithiasis. The objective was to identify the pieces of information the authors had found relevant to report. The list of items to report was blindly reviewed by 4 independent experts from our university hospital (a radiologist, a general practitioner, a urologist, and a medical informatics specialist).
Data model: standardization of the data collection within the report The list of fields was transformed into a simple data model (Figure  2 ) and modelized using UML  [10]. We then defined a possible instance of the data model using the YAML syntax  [11]. We also defined the corresponding JSON schema  [12]  for schema validation. Finally, we used the web-based open source e-CRF tool Goupile  [13] , to output a shareable easy-to-use form for any imaging report in the defined domain. The form was intended to output the data following the YAML syntax defined above. The YAML file was finally encoded into an automatically generated QR Code with a 7% error. This format enables to embed up to 2,953 characters.
Results We tested this methodology on computed tomography for urolithiasis. The final model retained 73 fields (Figure  3 ), divided into 3 tables with different cardinality, that was handled by JSON and YAML syntax (e. g. the variable size of the lithiasis was likely to appear more than once in a single report, so we created a table for lithiasis related fields). A JavaScript file was also written to produce a form from Goupile. All result files can be found online on https://github. com/arthurldp/medical_imaging_report. The QR-Code containing a 2,615 character YAML file was successfully decoded by 4 out of 5 smartphone models, of which one model retrieved the data through the native camera application, and 3 through different non-native QR Code scanning apps.
We believe this concept can be adapted to the traditional paper-based transmission, and medical imaging report is an example of choice, because being highly domain-specific, it narrows size of the dataset file to be encoded. Also QR-Code scanning in clinical data warehouse might supposedly be more reliable than combining optic character recognition and natural language processing to detect structured data in clinical data warehouse. Further work on QR-Code storage of medical data will focus on compression and encryption methods, to lower the QR-Code size, and secure the access of these data. We are planning to develop a software for producing medical report based on these tailored data models. This solution is intended to take as input the data model and the mappings, and to produce as an output an enhanced medical imaging report.
In addition, the use of the data collected and analyzed for the study were exclusively extracted from the medical records of the patients (MUCODOMEOS, htt ps://www. vaincrelamuco. org/2019/05/09/mucodomeos-un-log iciel-adapte-aux-besoins-des-crcm-2684) after having obtained their informed consent. In this context and according to the French law in force, the approval of an ethics committee was not required.
Data Collected All children were followed prospectively at least every 3 months before and after the LUM/IVA initiation as part of their disease follow-up (Figure  1 ). Briefly, weight, height, BMI, and ppFEV1 were assessed the day of the LUM/IVA initiation (M0) and at 3, 6, 12, and 24 months (M3, M6, M12, and M24) after the treatment initiation during routine clinical visits of the patients. To assess the effect of the LUM/IVA treatment on the disease course, theses parameters were retrospectively collected 12 and 24 months (M-12 and M-24) prior to the initiation of LUM/IVA. Anthropometric data (weight, height, and BMI) were automatically transformed as Z-scores by the MUCODOMEOS software based on the World Health Organization standardized values  (19). The ppFEV1 was determined using the Global Lungs Initiative 2012 reference values.
The means of these evaluations were kept for further analyses. The %HAV was automatically measured by using the Pulmo3D Syngo software (Siemens, Erlangen, Germany). To assess treatment tolerance, plasma liver enzymes were monitored prior to the LUM/IVA treatment initiation and at each visit during the first year and annually thereafter. Eye examinations were performed prior to the start of treatment and monitored at 1 year. At each visit, we also collected clinical data regarding side effects and recorded adherence to treatment in order to avoid a lack of response due to poor adherence treatment.
Statistics Analyses were performed only on available data using R software R (version 4. 0). Figures were done using Prism software R (version 5. 1). Data from the entire population were analyzed and comparison between subgroups according to the presence or absence of impaired lung function (ppFEV1 < 80%) or according to the Z-scores BMI (Z-scores < 0 vs. Z-scores ≥ 0) at the LUM/IVA initiation) were performed. Normality was assessed using Q-Q plots. Parametric variables were compared using Mann-Whitney test or ANOVA, and results were expressed as mean ± standard deviation (SD). Non-parametric variables were compared with Mann-Whitney test for unpaired values or Wilcoxon test for paired values and with t-test for parametric variables. Results were expressed as the mean ± standard deviation or as the median and interquartile range [median (IQR 25 ; IQR 75 )].
Interestingly, we observed psychological side effects such as increased anxiety and depression in one patient leading to an early cessation of treatment as previously described in real life  (33)  but not in the industry-sponsored studies  (8, 15, 17). Our study had several limitations. First, the number of patients included is low compared with other real-life studies and there is no control-group. However, these patients accounted for 84% of our patients eligible for the LUM/IVA treatment in our centers. Second, spirometric tests performed 2 years and 1 year before and after T0 were selected to match the best ppFEV1 obtained at ±1 month from the chosen time point. We use this method to avoid the transient effect of exacerbation on ppFEV1. Third, pre-treatment data were assessed retrospectively.
However, such data included standard items, which were uniformly and prospectively recorded during routine follow-up using the same software in the entire South-West network. This data is also channeled to the national registry, decreasing the risk of bias due to missing data. Fourth, only exacerbations requiring antibiotics have been considered, in contrast to less severe pediatric exacerbations due in most instances to viral infections. However, compared with previous clinical trials we assessed exacerbation requiring IV and/or oral antibiotics and not only those requiring IV courses which represent only a subset of antibiotic-treated exacerbations especially in adolescents. Fifth, the adherence to treatment was assessed at each visit but was based on the reports of children or their parents, and this represents an important limit of real-life studies.
MATERIALS AND METHODS The study draws primarily on cross-sectional data collected in the sixth round of a longitudinal online survey (COCONEL, COronavirus et CONfinement: Enquête Longitudinale). This round of the survey was designed and conducted by the Institut national d'études démographiques (INED), focusing on housing and living conditions during the first wave of the pandemic. A sample of 2,003 adults living in metropolitan France were questioned online between 30 April and 3 May 2020, using a quota sampling method covering age, gender, education, occupation, and category of municipality. Data collected included socio-demographic characteristics, household composition, a detailed description of housing conditions, employment characteristics, and perceptions of well-being. The COCONEL survey has three advantages compared to other national surveys.
Working from Home: Gendered Access to Personal Space The home became not only a place for leisure pursuits and family life, but also a place for paid work activities. While telework was not a widespread practice in France before the pandemics, it spread considerably during the first lockdown, but in a very unequal way according to the type of work and the level of education  (OECD, 2021). The analysis of the COCONEL survey data also shows that conditions for teleworking are highly gendered. COCONEL was one of few surveys in France to provide information about the conditions of teleworking at home during the pandemic. By May 2020, 29% of the population in employment before lockdown worked from home. This was the case for 86% of those in higher-level occupations. Telework was presented in the public debate as an advantage for well-qualified workers  (Leclerc, 2020).
Highly educated women were less affected by firm closures than women with a low level of education, since they were more often able to work from home. These results are in line with findings from other literature about the gendered impact of COVID-19 on the labour market  (Adams-Prassl et al. , 2020). Data on living conditions and social well-being were included in the analysis as potential factors exacerbating gender inequalities. The authors measured the gendered access to personal spaces for women and men working from home, gendered access to personal leisure time and activities, and the legitimacy of this differential access, and gendered and class-based access to family support networks during lockdown. The study found that men left the home more frequently during the day than women during lockdown, and that these differences increased when children were present.
Acknowledgements The authors are grateful to the  COCONEL Consortium  for supporting the research:  VITROME IHU Méditerranée Infection, Centre d'investigation clinique en vaccinologie Cochin-Pasteur, École des hautes études en santé publique, Observatoire régional de santé, Agence nationale de la recherche (ANR ),  Institut de recherche pour le développement (IRD) , and  Institut  français d'opinion publique (IFOP).
Funding The COCONEL survey was funded by the  Agence nationale de la recherche (ANR) , grant no. ANR-20-COVI-0035-01 , the  Institut de recherche pour le développement (IRD)  and the  Fondation de France. Funding support was provided for the sixth wave of the COCONEL survey by the  Institut  national d'études démographiques on 1 April 2020.
Systematic Review: Importance of fibers for allergy prevention and treatment For the purposes of this review, we focused on studies published during the last 5 years. In our search, we included observational epidemiological studies and clinical trials/intervention studies with application of dietary fiber and/or prebiotics to prevent or treat allergic diseases. Search terms are given in supplement 1. Based on these, 542 papers (235 from Pubmed and 307 from EMBASE) were retrieved. After removal of duplicates, 512 papers remained. Finally, after abstract and full text screening, we identified 16 studies that involved either dietary prebiotic (n=7) or fiber (n=9) intake and measured allergy-relevant outcomes. Exposures, interventions, and outcomes were deemed to be too heterogeneous with respect to prebiotic/fiber type and assessment of the outcome to attempt to pool the data for meta-analysis, so results are summarised as a narrative systematic review only.
Introduction Chronic kidney disease is a serious and a frequent disease associated with a high risk of morbimortality. Although several risk factors have already been well addressed, mostly diabetes and hypertension, many remain underappreciated, such as chronic exposure to air pollution. Methods and analysis We will search EMBASE, PubMed, Web of Science, Cochrane Library and CINAHL database, from inception to 31 March 2020, for relevant records using a combination of keywords related to the type of exposure (ozone, carbon monoxide, nitrogen oxides and dioxide, sulfur dioxide, PM 2. 5 , PM coarse and PM 10 ) and to the type of outcome (chronic kidney disease, endstage renal/kidney disease, kidney failure, proteinuria/ albuminuria, renal function, renal transplant, kidney graft, kidney transplant failure, nephrotic syndrome and kidney cancer).
METHODS AND ANALYSIS This systematic review and meta-analysis will be reported in conformity with the guidelines of the Meta-analysis Of Observational Studies in Epidemiology. 27 The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) was used to report this protocol. 28 he PRISMA-P checklist is attached as online supplemental file 1. Criteria for considering studies for the review ► Population: all studies in human beings which present original data and are published in full text or meeting abstract will be eligible for inclusion, with no restrictions on study design, publication date, language or ethnicity. We will exclude animal studies, ex vivo and toxicological studies, commentaries and editorials, case reports, and studies with no original data. If a citation lacks enough quantitative data and these essential data cannot be obtained from the corresponding author, the study will be excluded.
Bibliographic database searches Relevant records will be identified by searching EMBASE, PubMed, Web of Science, Cochrane Library and CINAHL database, from inception to 31 March 2020. We will use a combination of keywords related to the type of exposure ('air pollution', 'air pollutants', 'ozone', 'carbon monoxide', 'sulfur dioxide', 'nitrogen dioxide', 'particulate matter', 'PM 2. 5 ', 'PM coarse ' and 'PM 10 ') and to the type of outcome ('chronic kidney disease', 'end-stage renal/kidney disease', 'kidney failure', 'renal function', 'proteinuria', 'albuminuria', 'renal transplant', 'kidney graft', 'kidney graft failure', 'nephrotic syndrome' and 'kidney cancer'). Online supplemental file 3 shows the full search strategy for EMBASE that will be adapted to fit with other databases. No language restriction will be applied.
Open access Selection of for inclusion in the review All references identified after implementation of the search strategy will be imported to the Zotero software. All records obtained from various databases will be combined in a single Zotero library and duplicates will be removed. Two reviewers (AH and AC) will independently evaluate the studies obtained from the searches using an assessment form to ensure that the selection criteria are reliably applied. These reviewers will screen the titles and abstracts of the papers obtained, after which the full texts of potentially eligible papers will be retrieved by one reviewer (AH). The two reviewers will independently review the full text of each potentially eligible study, compare their results and resolve any discrepancy by discussion. For duplicates, or studies published in more than one report, the one reporting the largest sample size will be considered.
Assessment of methodological quality and reporting of data 4] [35]  Two independent reviewers will assess each study for potential biases on four major components, namely exposure assessment bias, detection bias, selection bias and adjustment for confounders. For exposure assessment bias, we will deem studies using less than three fixed-site monitors to assign participant air pollutant exposure levels as having a high risk of exposure assessment bias, studies using three or more as having moderate risk, and studies using personal exposure or atmospheric modelling as having low risk. We will also regard studies as having a high risk of exposure assessment bias if they were done before 1980 because insufficient technological and methodological precision in measuring and assigning particulate matter exposure was available during that period.
Data extraction and management A data extraction form will be used to collect information on the surname of the first author, year of publication, country where the study was conducted, study design, study area (rural, urban), research period, sampling method, timing of data collection, population setting (general population, hospitalised patients), nature and definition of studied outcomes, assessment method of air pollutant exposure, mean or median age, proportion of men, ethnicity, other specific characteristics of the study population (such as proportions of active smoking, diabetes, obesity or cardiovascular comorbidities), sample size, crude and adjusted estimates (OR, HR and beta-coefficients) for the association between each air pollutant and the prespecified outcomes of interest, as well as adjustment factors included in multivariate analyses.
All pooled estimates will be pooled by pollutant and renal outcome and reported with 95% CI. Given the potential study design heterogeneity, the results will be stratified by the nature of included studies: on the one hand the results from crosssectional studies and on the other hand those from longitudinal studies. Regarding CKD outcome, for example, the results will be presented according to whether these are prevalence (summarised as OR (95% CI)) or incidence data (summarised as HR (95% CI)). One study estimate will be included per city for the same study period to ensure results will not be biased by multiple inclusions of one city data. Where duplicate cities will be presented for the same study period, we will select one estimate for meta-analysis by prioritising multicity designs because of their standardised and often higher quality methodologies, and then if duplicates are still present selecting the study with the lower risk of exposure assessment bias.
Chemical composition of the concentrated aerosol Size-resolved particle chemical composition (in µg/m 3 ), determined by ICP-MS and ICP-OES, is presented in Fig. 3  and Table  1  (mean results for both sampling days are presented for booth #3). The elemental composition was also determined by XFR for quality-control purposes. The inter-comparison between both analytical methods provided good results for the majority of the elements analysed, with especially high intra-method correlations for Ca, Fe, Ti, Cr, Sr, Co, Ni, W, Zn and Pb (R 2 > 0. 98; Fig. S1  in Supporting Information), which include the main tracers of the feedstock materials sprayed (Table  S1  in Supporting Information). Based on this quality control, for the following analyses the data obtained by ICP-MS and ICP-OES were used.
Acknowledgements This study was supported by  Institut National de la Santé et de la Recherche Médicale  ( INSERM, Paris, France ),  Fondation de France  ( Program Cancer  and Environment 2016),  AVIESAN (Plan Cancer  Program: Cancer and Environment 2015), and by  Intramural Research Program of the National Cancer Institute, Division of Cancer Epidemiology and Genetics, NIH (Bethesda, MD, USA). The authors are grateful to  Dr. N. Gentner  and  Ms S. Cueto-Habersack  for their efforts to locate reports of  Australia and New Zealand  to  United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Special thanks are to  Miss O. Drozdovitch  for assistance with preparation of the figures. The authors are also grateful to  Dr. Frederic Poirrier ,  Head  of the  Département de Suivi des Centres d'Expérimentations Nucléaires (DSCEN)  for providing access to de-classified reports of  French Ministry of Defence  and  Dr.
Machine Learning and Big Data Analytics One of the major contributions that AI-assisted technologies has had in recent years in NCD management is through machine learning and big data analytics. A systematic review of literature on AI highlighted that machine learning is currently the most commonly used AI technology in healthcare  (28). However, this field is still in its infancy; there are currently <100 FDA-approved AI/machine learning-based medical devices and algorithms  (19) , which are constantly updated on an online database  (41). Although many relate to computer-assisted diagnosis and clinical decision support systems, there are several interesting examples in the scientific literature of how machine learning has been used to measure, predict, and increase medication adherence in NCD patients.
Machine learning methods can also be extended to examine the effect of medication adherence on clinical outcomes. For example, in a study using data from more than 30,000 patients with type 2 diabetes mellites, machine learning was used to examine how adherence to oral hypoglycemic medications was associated with reduced hospitalization  (49). In another study, an AI framework that was designed to learn from clinical data was able to improve patient outcomes by 30-35% compared with treatment-as-usual  (50). Big database analysis techniques have also been utilized for measuring the effectiveness of interventions, for example, investigating adherence as a clinical outcome  (51). Importantly, the benefit of machine learning is that combining a magnitude of data sources into a comprehensive AI dataset can help to optimize treatment and adherence  (52).
Kardas et al. (76)  highlighted that the use of big data to study patient adherence is hindered by three major barriers in terms of standardizing data for analysis. They recommended that there should be a standard format for the data collected in big data databases, sound metrics for processing of the raw data, and commonly agreed standards of presentation of adherence measures being assessed within big data. Importantly, according to a recently proposed framework  (66) , future AI research in healthcare should consider issues of transparency, reproducibility, ethics, and effectiveness.