dataesr/scientific-paragraphs-categorization huggingface dataset:
First, only 2 ETCs were included; second, a single laboratory generated all Cts; and third, patients who were treated with favipiravir and those who were not treated were managed at the same time. On the other hand, favipiravir was not evaluated in the controlled setting of a clinical trial. While we have made great efforts to reduce biases in the raw field data by using a set of criteria, the retrospective study design still has serious limitations, and data arising from the study have to be interpreted with care. A key limitation is the lack of randomization. Patients who were treated are clearly identifiable, while retrospectively defining an unbiased nontreatment (ie, control) group is difficult. The field database is likely to include nontreated patients who were too sick to provide consent, who were not able to take oral drugs, or who died before they could provide consent or before treatment could be commenced; these aspects are not recorded.
Frame systems and closed-world semantics Frame systems use the familiar closed-world semantics of databases, logic programming, etc. There is no standard for frame systems. The closest is the OKBC standard  [10, 60] , which is the basis for Protégé  [6] , and which we shall take as our prime example. In frames, as in all closed-world systems, the "world" or model consists all and only of the entities in the knowledge base. If an individual cannot be found, it is assumed not to exist. If a statement cannot be inferred from the individuals and statements explicitly present in the knowledge base, then it is considered false. Hence, the phrase: "negation as failure". To make the difference clearer, in a frame system, in order to say "John has a sister" we must add a statement identifying an individual in the system as John's sister, e.
Annotations and comments Finally, many tools and formalisms contain one or more mechanisms for providing comments and annotations that are otherwise outside the semantics of the formalism. Frequently these are a function of the editing environment and tools, rather than the formalisms per se. OWL provides annotation propertiesa feature of the OWL standard, rather than of the description logics that underpin it. Annotation properties have a similar syntax to other properties but are ignored by reasoners. However, they can be accessed by SPARQL and most other query languages. Protégé-Frames represents most annotations and comments as "class relations" ("class slots")relations that apply to classes rather than individuals but are otherwise indistinguishable from other relations, and in particular from relations used in higher order statements.
Database schema languages Database schema languages, such as UML class models or openEHR Archetypes  [83] , specify how information is to be storede. g. in an electronic health recordrather than representations of domain per se. The graphical representations of schemas and knowledge representations may look similar, but their function is fundamentally different. Statements about the domain are different from statements about how to store information about the domain. Most obviously, notions such as "missing data" or "mandatory field" only make sense with respect to data structures. There is a profound difference between the implications of a missing entry for "pulse rate" in a record and an "absent pulse rate" in a patient. "Pulse rate not recorded" makes no sense with reference to a patient, but only to data concerning that patient.
The question is often asked: "How do I convert OWL to UML class diagrams or openEHR Archetypes. Typically, OWL is used to represent only domain statements. Representations such as UML class diagrams or openEHR Archetypes are used to represent data structure specification statements. Similarly, other schema languages such as RDF(S)  [84]  and the more recent RDF schema and constraint languages ShEx  [85]  and SHACL  [86, 87]  are designed primarily to specify how data is to be represented in RDF graphs. 31 There can be no direct conversion between domain knowledge bases and data specifications because they are about different things. However, aspects of a schema may be selectively derived from or motivated by an OWL knowledge base, either staticallyas in the use of SNOMED CT as a source for value setsor dynamicallyas in the HOBO framework that was used to derive data structures for information on the staging of many different cancers based on an OWL KB  [88].
5 above, shows the results of an example query that links the ICD aggregation for "Hypertensive diseases" to the results of a query on the entities in the invariant hierarchy. In summary we recommend: 31 A more detailed discussion of the relation of RDF and related developments to databases and knowledge representation is beyond the scope of this paper. 32 Or if preferred, the slightly longer phrases, "Statements about the domain" vs "Statements about the representation" 33 In the historical language of 1. 2, "static" vs "dynamic" knowledge base. 34 Background statements are loosely analogous to database schemas; foreground statements, to data. 35 i. e. statements invariant under all operations that add to the KB. • OWL/Description logic for "invariants"/"indefeasible statements"represented by solid lines and arrows in Figs.
• Fixed-depth mono-hierarchies using the JEPD principle with supplementary exclusions, inclusions, etc. for aggregations such as the ICD family of "classifications". • Query languages (e. g. SPARQL or the SNOMED Expression Constraint Language) to link OWL/Description Logic KBs to aggregations in JEPD mono-hierarchies corresponding to the "Linearizations" in the original ICD-11 architecture. (See Fig. 2 ) In addition, depending on the environment and application, there might be at least two other sorts of information not shown in Fig. 2:  • Comments external to the representation itself, e. g. in an associated discussion stream or in source files. Schema languages for data structure specifications that may be based in part on the information in the domain KBe. g. UML class diagrams or OpenEHR Archetypes.
In effect, such knowledge bases use OWL as a syntax for a frame system, but with no standardised semantics of either frames or OWL. Since OWL has become the primary language supported by Protégé  [50] , it is often used in this way disregarding OWL's semantics. • Tweaking the axioms so that the reasoner "gets the right answer" ignoring what else the axioms might entail: This can lead to the converse problem to that of the previous approach. For example, to list the symptoms of pneumonia, we might use statements such as: Rales SubClassOf ðis symptom of some PneumoniaÞ ð 20Þ The reasoner would then find Rales as a subclass of the expression: is symptom of some Pneumonia ð21Þ This is probably the result intended. However, there are other unintended consequences, e. g. : Rales and is symptom of some Congestive cardiac failure would also be classified by the reasoner under: is symptom of some Pneumonia ð23Þ since the axiom says all Rales are symptoms of Pneumonia.
g. Kattenstroth et al. [108]  and to deal with higher order reasoning, e. g. De Giacomo et al. [109]. There are more radical alternatives outside the description logic framework, which are discussed in various academic resources such as the Ontolog Forum  [110]  and its annual Ontology Summit, the annual conferences on Knowledge Representation  [111]  and Formal Ontology for Information Systems  [112]. Still further beyond the scope of this paper are topics such as non-monotonic logics and belief maintenance. However, none of the alternatives is yet in widespread use or has made an impact on standards in wide use in biomedicine. This paper confines itself to declarative knowledge. It should, perhaps, be mentioned that, for procedural knowledge, the SWRL rule language  [113]  is designed to work with OWL and is supported by the Protégé-OWL environment  [114, 115].
Acknowledgements The authors would like to thank the  World Health Organisation's Classification Group  and  Bedirhan Üstün  for originating the project that stimulated this paper and the  International Health Terminology Standards Organisation  (now  SNOMED International ) and particularly  Kent Spackman  for their participation in the project. Special thanks to the  International Conference on Biomedical Ontologies 2017  ( ICBO-2017 ) and its then chair  Philip Lord  for the invitation to give the keynote lecture that formed the basis for this paper and to all those who participated in the many discussions that followed. Thanks also to  Matthew Horridge  for comments and corrections on details of the OWL standards and to  Anthony Nowlan  for valuable comments. The ICD-11 project was funded in part by the  World Health Organisation  with contributions from IHTSDO (now  SNOMED International ).
However, my 2002 Master's memoire on aid workers' security in Afghanistan ('Quelle place pour la compréhension dans la trilogie: acceptabilité, protection et dissuasion?' 1 ) gave me the justification I needed to apply. I ultimately got an interview, after resubmitting my application and calling a few people I knew at MdM. When I started, my job description was as follows: monitor the risks and threats to project teams in countries where MdM was working; provide methodological and technical support for writing, finalising and setting up security-management documents at the missions; monitor the implementation of security analysis and management tools at international programmes and prepare a periodic report for the International Operations Directorate; advise the project teams on managing security incidents and update the securityincident database; support security-management briefings for field teams; and maintain the existing security network and take part in outside meetings.
Task Five: Security-Incident Analysis The analysis of security incidents in the field also needed to improve. In 2012, it was impossible to know what type of incidents had the most impact on our field staff. Was it true, as everyone said, that road accidents were the leading cause of death among aid workers? While that was probably true in the 1990s, the logistical management of vehicle pools, driver training and respect for traffic rules (speed, seatbelts and car maintenance) had improved, as the 'Aid Worker Security Database' (Humanitarian Outcomes, n. d. ) demonstrated. Moreover, I found only a few car accidents in our archives, though it was likely that not all the incidents had been reported. In fact, one of the biggest obstacles to incident reporting was the fear of consequences and of headquarters interfering in the mission's affairs.
Recruitment and data collection Participant recruitment followed the Dillman Total Design Survey Method  [30]. A 4-week period was allotted for recruitment and data collection. Instructors in mandatory courses were recruited to optimize student participation. Instructors made in-class announcements informing students of the survey at two and one-week intervals prior to initiating the survey. Students were then emailed an invitation with the accompanying link to the survey. A final announcement and invitation were sent to all students 1 week before the survey was closed. Students at PU, CMCC, NWHSU and IFEC were given time to complete the questionnaire during class. The survey was developed and administered using the online SurveyMonkey platform (SurveyMonkey Inc. , San Mateo, California, USA.
Data analysis Demographic data were descriptively analyzed, using counts and percentages by category for categorical variables and means and standard deviations for continuous variables. General linear regression models were used to quantify the associations between overall PPOS scores and the independent variables and separately for the Sharing and Caring subscales. The primary independent variables of interest were the institution treated as a fixed effect and year/trimester of study nested within the institution to account for differences in program delivery structure across institutions. We approached modelling in a staged manner. First, the institution and year/trimester of study nested within the institution were included. Then the secondary independent variables age, gender, history of chiropractic care and having family members who are health professionals were added one at a time to examine their significance and whether their inclusion impacted the associations of the primary independent variables.
Results Overall, 1961 records were extracted from SurveyMonkey. Of these, we deleted 36 records due to blank or inappropriate responses to demographic questions and 67 records due to missing item responses. Thus, 1858 records across the seven chiropractic programs were used in the analysis (See Table  1 ). Response rates varied by program ranging from 25. 67 to 61. 14%, with an overall response rate of 48. 89%. Overall, 57% of respondents were female, 57% were 20 to 24 years of age inclusive, and almost 50% had a family member in healthcare (See Table  2 ). Internal consistency of the overall PPOS score, Sharing and Caring subscales from these data were Cronbach's α of 0. 67, 0. 63, and 0. 50 respectively which are similar to values reported by Krupat et al. [23]  and Lee et al. [18]. The average overall PPOS score was 4.
Acknowledgements We would like to thank the focus group and survey participants, as well as all the eUlar Pare liaisons who helped in the survey translation and dissemination. Contributors an, en, lg and FB participated to the study design, the data analysis, the task force meeting and the manuscript redaction. all the authors contributed to the data analysis, the task force meeting and the manuscript redaction. Funding this project was funded by eUlar (project number  cli102 ).
Le fichier national des causes médicales de décès (réseau français des registres de cancer). Les données essentielles au calcul de l'incidence et de la survie issues des registres généraux et spécialisés d'organes sont rassemblées dans une base de données commune. Les registres nationaux pédiatriques ont une base de données spécifique. Les registres fournissent les données d'incidence (nouveaux diagnostics de cancer) et les données de survie. Celles-ci rendent possible le calcul de la prévalence (nombre de personnes ayant eu un diagnostic de cancer et vivantes à une date déterminée). Il est géré par le Centre d'épidémiologie sur les causes médicales de décès de l'Inserm (CépiDc-Inserm) qui assure depuis 1968, la production des données de mortalité à partir de la base de données constituée.
Ainsi, tout patient atteint de l'un de ces cancers rares pourra être pris en charge dans l'établissement de son choix, mais sera assuré d'un avis d'expert tant pour le diagnostic (double lecture des lames) qu'aux divers temps de sa maladie (discussion de son dossier en RCP de recours régionale ou nationale) et de son suivi. Des recommandations de bonnes pratiques seront élaborées, l'inclusion dans des essais cliniques favorisée, facilitant ainsi l'accès à des thérapeutiques innovantes, et l'analyse de la base de données colligée par chacun de ces centres permettra un observatoire de ces pathologies rares. Parallèlement, 3 réseaux nationaux de référence anatomopathologiques en lien avec ces cancers rares ont été labellisés :  réseau national de référence anatomopathologique des sarcomes des tissus mous et des viscères ;  réseau national de référence anatomopathologique des mésothéliomes malins de la plèvre et des tumeurs rares du péritoine ;  réseau national de référence anatomopathologique des tumeurs neuroendocrines malignes rares sporadiques et héréditaires.
Dans cet objectif, l'INCa a développé en 2010 une base de données permettant de centraliser et d'afficher sur son site internet des données standardisées relatives aux prélèvements tumoraux collectés dans les tumorothèques à des fins scientifiques. Cet outil national, intitulé Tumorothèque virtuelle nationale (TVN), a pour objectif de donner une visibilité et une accessibilité aux principales collections nationales d'échantillons constituées dans le cadre de réseaux et consortiums thématiques et de programmes de recherche nationaux ou internationaux.
Le réseau français des registres de cancer, réseau Francim, dispose d'une base de données commune qui rassemble les données des registres généraux et spécialisés d'organe. Cette base commune est administrée au sein du service de Biostatistiques pour la surveillance épidémiologique du cancer en France entre le réseau Francim, les HCL, l'Institut de veille sanitaire (InVS) et l'Institut National du Cancer (INCa). Cette base de données permet la réalisation d'études sur l'incidence, la survie et la prévalence des cancers. Les registres actuels couvrent environ 20 % de la population et le délai de mise à disposition des données est actuellement de 4 ans (les données observées jusqu'en 2005 sont disponibles mi-2009) 2  Le Registre national des hémopathies malignes de l'enfant (RNHE) et le Registre national des tumeurs solides de l'enfant (RNTSE) recueillent et produisent des données d'incidence et de survie sur les cancers de l'enfant de moins de 15 ans.
), les bases de données des mesures d'exposition en milieu professionnel (Colchic, Évalutif, etc. ), et le RNV3P (Réseau national de vigilance et de prévention des pathologies professionnelles) qui recueille les données concernant les pathologies professionnelles. Différents programmes (ESPACES, Spirale, Espri, etc. ) assurent, d'une part, le repérage de retraités ayant été exposés à des cancérogènes au cours de leur vie professionnelle (avec le bénéfice d'un suivi médical) et, d'autre part, un suivi épidémiologique des cohortes constituées(BEH, 2007). Afin d'améliorer la qualité des informations permettant d'évaluer le lien entre l'état de santé du travailleur et le(s) poste(s) et les conditions de travail actuelles et antérieures, la Haute autorité de santé (HAS) a élaboré, en 2009, des recommandations pour le dossier médical en médecine du travail22.
 Le réseau français des registres de cancer, réseau Francim, dispose d'une base de données commune qui rassemble les données des registres généraux et spécialisés d'organe. Cette base commune est administrée au sein du service de Biostatistiques pour la surveillance épidémiologique du cancer en France entre le réseau Francim, les HCL, l'Institut de veille sanitaire (InVS) et l'Institut National du Cancer (INCa). Cette base de données permet la réalisation d'études sur l'incidence, la survie et la prévalence des cancers. Les registres actuels couvrent environ 20 % de la population et le délai de mise à disposition des données est actuellement de 4 ans (les données observées jusqu'en 2005 sont disponibles mi-2009) 2  Le Registre national des hémopathies malignes de l'enfant (RNHE) et le Registre national des tumeurs solides de l'enfant (RNTSE) recueillent et produisent des données d'incidence et de survie sur les cancers de l'enfant de moins de 15 ans.
Ces estimations ainsi qu'une synthèse des principaux résultats de ce travail sont présentées dans le présent rapport. 2. 1. 4. Méthodes d'estimation de la prévalence en France Les données de prévalence du cancer sont mal connues en raison de l'absence d'enregistrement exhaustif des données concernant le suivi des patients atteints de cancer. Il est donc nécessaire de recourir à des estimations. Ces estimations s'appuient sur les données d'incidence fournies par les registres ainsi que les données de survie qu'il est possible de recueillir à partir du suivi des patients. Une étude à partir de la base de données commune des registres du réseau Francim a été réalisée en 2007 par Francim afin de fournir des estimations de la prévalence partielle à 5 ans du cancer, en France, au 31 décembre 2002 (Colonna M, 2007) et celles de la prévalence réelle pour 4 localisations cancéreuses : sein, colorectal, prostate, rein (rapport d'étude INCa 2007).
incidents diagnostiqués entre 1998 et 2002. Cet indicateur permet d'approcher plus précisément le nombre de patients nécessitant des soins. La prévalence partielle constitue un sous-ensemble de la prévalence totale qui dénombre les personnes en vie à un moment donné et ayant eu un diagnostic de cancer, quel que soit leur état de santé. L'estimation pour 2012 est une simple application des taux observés en 2002 à la population de 2012.  2. 1. 5. Source des données internationales Les données d'incidence et de mortalité au niveau européen et international présentées dans ce rapport sont issues de la base de données « GLOBOCAN 2008 » (Ferlay J, 2010) gérée par le Centre international de recherche sur le cancer (Circ) pour comparer l'incidence et la mortalité par cancer à partir des dernières estimations réalisées par le Circ, pour l'année 2008.
), les bases de données des mesures d'exposition en milieu professionnel (Colchic, Évalutif, etc. ), et le RNV3P (Réseau national de vigilance et de prévention des pathologies professionnelles) qui recueille les données concernant les pathologies professionnelles. Différents programmes (ESPACES, Spirale, Espri, etc. ) assurent, d'une part, le repérage de retraités ayant été exposés à des cancérogènes au cours de leur vie professionnelle (avec le bénéfice d'un suivi médical) et, d'autre part, un suivi épidémiologique des cohortes constituées(BEH, 2007). Afin d'améliorer la qualité des informations permettant d'évaluer le lien entre l'état de santé du travailleur et le(s) poste(s) et les conditions de travail actuelles et antérieures, la Haute autorité de santé (HAS) a élaboré, en 2009, des recommandations pour le dossier médical en médecine du travail22  renforcer l'information ; La réduction des inégalités sociales de santé, spécifiquement dans le domaine des cancers professionnels, constitue désormais un des axes structurants de divers plans et programmes nationaux de santé publique.
The study participants were then selected randomly from the listing of the two kebeles of Hururta. Ethical considerations. Ethical approval was obtained from the Ethics Review Committee of the College of Natural and Computational Sciences of Addis Ababa University (CNSDO/100/09/2016) and the Oromia Regional Health Bureau (AHIQFTFWO/408). Informed consent was obtained after a detailed explanation was given about the objectives and principles of the study. Informed consent/ascent was obtained from parents and/ or legal guardians for those under 18 years of age and from adolescents only when they were above 18. All experiments were performed in accordance with relevant guidelines and regulations. Socio-demographic and anthropometric measurements. Data on socio-demographic characteristics was collected through interviews administered using a pre-tested questionnaire that included questions about the educational status of the family, household composition, available facilities in the household, and access to healthcare.
Main analyses Given the multi-level nature of our data (individuals within study centres), we conducted survival analysis, applying a mixed effects Cox regression model with menopause as the survival object (event), age as the time indicator, residential surrounding greenspace as the timevarying fixed effect predictor, and centre as random effect. For nonmenopausal women, the age at the third survey was included as censored observation. The model was adjusted for an a priori selected set of covariates: smoking habit at the third survey (lifelong non-smoker (reference); ex-smoker; current smoker), body mass index (BMI) at baseline, parity (continuous), age at menarche (continuous), ever-use of oral contraception (yes/no), and age at completed full-time education (< 17 years (reference); 17-20 years; > 20 years) as a proxy for socioeconomic status.