dataesr/scientific-paragraphs-categorization huggingface dataset:
To be specific, our findings suggest that COVID-19 disease may be associated with at least some of the AESIs and may consequently exert a confounding or intermediating effect in the observed association between the vaccines and the AESI. When conducting observational studies investigating the association between the vaccines and the AESI, it is important to control for COVID-19. A particular strength of this study is that it includes a large number of databases from around the world, covering a sizable study population with diversity in geographical location, underlying populations, pandemic status, health systems, and data types. Our analysis enabled a comprehensive and standardised assessment of incidence rates of AESIs among 'Patients with COVID-19' across multiple settings. This was possible due to the use of the OMOP CDM, which enabled use of the same study design and analytical code in all databases and to gather results from participating data partners rapidly and without transferring patient level data.
There was significant heterogeneity in COVID-19 test and disease ascertainment across databases. COVID-19 severity is an important factor that was outside the scope of this analysis but may have affected the risk of AESIs and probably varied by database. When defining the 'Pre-Pandemic Background Population', we used data from 2017 to 2019 of all people in each database with more than 365 days of observation indexed on 1 January. The impact of these design decisions, in particular the index date (anchoring effect) has been shown to influence rate estimates, however the effect of season has been shown to be moderate. 25 inally, there are some limitations related to participating databases. Information on hospital admissions was not available in all primary care databases used (Table  1 ) resulting in inpatient events not being captured.
EHR databases are subject to incomplete capture of medical events that may occur but are recorded outside the participating health system. The bias of incomplete information was partially mitigated by including only those patients who had at least one year of continuous observation but defining continuous observation can be challenging across disparate databases. Administrative claims databases offered a potentially complete data capture but lacked some important data elements such as laboratory test results. We mitigated some of these limitations by providing within database incidence rate comparison. Additionally, the repeated influxes of vast numbers of critically ill COVID-19 cases suddenly overwhelmed hospitals and we cannot exclude changes in the organisation and thus the quality of coding in code-based administrative database.
Finally, all our databases represented subsets of the population in which they originate, which poses a risk of selection bias. Our study assessed the descriptive epidemiology of the occurrence of AESIs in the 90-days after COVID-19 disease. These results found large variations in the rates of AESIs in 'Patients with COVID-19' across age groups and sex, showing the need for stratification. Considerable database heterogeneity was found across the AESIs, suggesting individual study estimates should be interpreted with caution. Comparing the 'Patients with COVID-19' to the 'Pre-Pandemic Background Population' showed a fairly consistent elevated rate in experiencing an AESI within 90-days after index. This elevated risk we see both consistently across the database stratified results as well as the meta-analysis.
Contributors Using the CRediT, Contributor Roles Taxonomy  38  here are how the co-authors participated in this work. For conceptualization, or formulation of the research goals, the following authors participated: EAV, AS1 (Shoaibi), TA, AGS, GR, DRM, TDS, KN, CR, EPM, NHS, JDP, MTGM, DB, HCJ, ADH, MPJ, PSB, NGB, RV, AJB, AP2 (Patel), LD, CH, FA, AO, FN, GH, MAS, DPA, PRR, PBR. For data curation, or those who worked with research data, the following authors participated: EAV, CB, SvS, TLC, SH, DRM, JMR, JMRA, MAM, MdW, LHJ, TDS, AP1 (Pistillo), RK, FM, SD, VP, MGK, KN, CR, AS2, EPM, NHS, JDP, MTGM, DB, HCJ, ADH, MPJ, NGB, SS, AYU, BE, LB2 (Belmans), GB, DJD, ZC, RV, AJB, AP2, LD, CH, GH, DPA, PRR, PBR. For formal analysis, or those who analysed or synthesised study data, the following authors participated: EAV, AS1, LYHL, CB, AGS, GR, CF, JMRA, MAM, LHJ, AP1, FM, MGK, EPM, SS, AYU, BE, LB2, GB, AO, FN, DPA, MJS, PBR.
Visualization, or creation of visualizations or data presentation, was performed by: EAV, LYHL, CB, FB, PBR. Writing of the original draft was performed by: EAV, AS1, LYHL, TA, TDS, ER, FM, SD, VP, MTGM, DB, HCJ, ADH, MPJ, PSB, NGB, SS, AYU, BE, DJD, ZC, AO, FN, PBR. Editing and reviewing the original writing was performed by: EAV, AS1, LYHL, CB, TA, RM, KH, AGS, GR, SvS, CF, LB1, TLC, DRM, JMR, JMRA, MAM, LHJ, TDS, ER, AP1, FM, SD, MGK, KN, CR, AS2, EPM, NHS, JDP, MTGM, DB, HCJ, ADH, MPJ, PSB, NGB, SS, AYU, BE, LB2, GB, MLNGM, DJD, ZC, RV, AJB, AP2, LD, CH, FB, FA, AO, FN, GH, MAS, DPA, PRR, MJS, PBR. All authors approved the final version, had access to the data, and accept responsibility to submit the final version for publication. No competing interests. All authors approved the final version, had access to the data, and accept responsibility to submit the final version for publication.
APHM = Health Data Warehouse of Assistance Publique -Hopitaux de Marseille, BE = Belgium, CPRD_AURUM = Clinical Practice Research Datalink AURUM, CU_AMC = University of Colorado Anschutz Medical Campus-Health Data Compass, CUIMC = Columbia University Irving Medical Center, DE = Germany, EE = Estonia, EHR = Electronic Health Record, ES = Spain, F = Female, FIIBAP = Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria COVID19, FR = France, GP = General Practitioner, HIC = Health Informatics Centre, IBM_CCAE = IBM® MarketScan® Commercial Claims and Encounters Database, IBM_MDCD = IBM® MarketScan® Multi-State Medicaid Database, IBM_MDCR = IBM® MarketScan® Medicare Supplemental and Coordination of Benefits Database, IMASIS = Parc de Salut Mar Barcelona Information System, IPCI = Integrated Primary Care Information, IQR = Interquartile Range, IQVIA_FRANCE_DA = IQVIA Disease Analyzer France, IQVIA_GERMAN_DA = IQVIA Disease Analyzer Germany, IQVIA_OPENCLAIMS = IQVIA LRxDX Open Claims, IQVIA_PHARMETRICS = IQVIA Pharmetrics, IU = Istanbul Faculty of Medicine, Istanbul University, JP = Japan, MHD = Medaman Hospital Data, N = No, NL = The Netherlands, OPTUM_EHR = Optum® de-identified Electronic Health Record Dataset, OPTUM_SES = Optum De-Identified Clinformatics® Data Mart Database -Socio-Economic Status, RS = Serbia, SC = Scotland, SIDIAP = The Information System for Research on Primary Care, STARR = STAnford medicine Research data Repository-OMOP, TR = Turkey, U_OF_TARTU = University of Tartu (U_OF_TARTU), UCCS = University Clinical Center of Serbia, UCHDW = University of California Health Data Warehouse, UK = United Kingdom, UK_BIOBANK = UK Biobank, US = United States, Y = Yes.
The administrative claims databases were: IBM ® MarketScan ® Commercial Claims and Encounters Database (IBM_CCAE);  IBM ® MarketScan ® Multi-State Medicaid Database (IBM_MDCD) ;  IBM ® Mar-ketScan ® Medicare Supplemental and Coordination of Benefits Database (IBM_MDCR) ;  IQVIA LRxDX Open Claims (IQVIA_OPENCLAIMS) ;  IQVIA PharMetrics Plus (IQVIA_PharMetrics) ;  JMDC 10  ;  Optum De-Identified Clinformatics  ® Data Mart Database-Socio-Economic Status (SES) (OPTUM_SES); and  University of Tartu (U_OF_TARTU). The EHR systems were: Health Data Warehouse of  Assistance Publique-Hopitaux de Marseille (APHM) ;  University of Colorado Anschutz Medical Campus-Health Data Compass (CU_AMC) ;  Columbia University Irving Medical Center (CUIMC) ;  Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria COVID-19 (FIIBAP) ;  Health Informatics Centre (HIC) ;  Parc de Salut Mar Barcelona Information System (IMASIS) ;  Istanbul Faculty of Medicine, Istanbul University (IU) ;  Medaman Hospital Data (MHD) ;  Optum ® de-identified Electronic Health Record Dataset (OPTUM_EHR) ;  STAnford medicine Research data Repository-OMOP (STARR)   11  ;  University Clinical Center of Serbia (UCCS) ; and the  University of California Health Data Warehouse (UCHDW).
Acknowledgements The authors would like to thank  Lee Evans  for his support of the environment that allowed sharing of results. The authors thank the  Center for Data-driven Insights and Innovation at UC Health  (CDI2; https://www. ucop. edu/uc-health/functions/center-for-data-driven-insights-and-innovations-cdi2. html), for its analytical and technical support related to use of the UC Health Data Warehouse and related data assets, including the  UC COVID Research Data Set (CORDS).
Objectives: To describe management, and to assess factors associated with antithrombotic prescription thereafter in patients who had epistaxis referred to emergency department (ED). Design: Prospective cohort study. From EDs, clinical, biological and hospital data were collected. The clinical database was linked to the French Health Insurance Database where we retrieved antithrombotic drug deliveries in a 3-month period before and after referral. Setting: Multicentric population-based cohort study within five well-defined areas. Participants: We considered 306 patients referred for epistaxis with a stable oral antithrombotic regimen before referral. Main Outcome Measures: We considered management, hospital outcome and case fatality. Antithrombotic prescription in a 3-month follow-up period was categorised into three classes: no change, class change, or discontinuation.
| Participants For this analysis, we considered only patients (i) referred for epistaxis without any other concurrent bleeding during the study period, retaining the first occurrence as index epistaxis, (ii) with a stable oral AT regimen before referral (i. e. , without any change in therapeutic class of AT agents, within 3 months prior to epistaxis). Any other concurrent bleeding was excluded. From EDs, demographic data, clinical data (co-morbid conditions, AT class with presumed indication and time since initiation, systolic blood pressure), as well as biological data (haemoglobin and creatinine level), therapeutic management such as red blood transfusion, platelet transfusion, plasma transfusion, vitamin K, protamin sulphate, prothrombin complex concentrate (PCC) and FEIBA ® (anti-inhibitor coagulant complex), and hospital data (endoscopic or surgical procedures, ward type, intensive care unit (ICU) stay, length of stay [LOS] and case.
16 In each ED, the local referent medical doctor validated the inclusion, and specifically, the bleeding severity defined as having at least one of the following criteria: unstable haemodynamic (systolic arterial pressure < 90 mmHg or mean arterial pressure < 65 mmHg) or haemorrhagic shock, need for transfusion or for emergency haemostatic procedure. The clinical database was linked to the French Health Insurance Database (SNIIRAM) using common key variables (date of birth, gender, date of hospital entry and discharge, type of AT therapy, and care facility involved). 14 The SNIIRAM contains anonymous individual data on all reimbursements for health expenditure, including drugs (of note, the database does not provide the medical indication for each drug prescription), hospital discharge diagnoses (ICD-10 code) as well as details on medical acts.
| Strengths and limitations The study has some strengths. All nosebleed events were medically validated, drug exposure and medical history were assessed through clinical records as well as on Health Insurance Database (SNIIRAM), the two data sources being linked allowing cross-checking. Health Insurance Database (SNIIRAM) allowed to identify patients with stable AT therapy for 3 months before epistaxis, and then epistaxis recurrence (referred to ED) without attrition bias. The study has also some limitations mostly stemming from our inability to comprehensively measure some dimension of individual risk assessment such as presence of a third party at home to help with the preparation and taking of medication, disability and cognitive status, social context and life habits. There was also a risk of misclassification related to coding errors at the time of hospital admissions.
In this article, production process databases originating from environmental sciences, more specifically from Life Cycle Inventory (LCI), are considered as bipartite directed random networks. To model the observed directed hierarchical connection patterns, we turn to recent development concerning trophic coherence. Extending the scope to include bipartite networks, we compare several LCI networks to networks from other fields, and show empirically that they have high coherence and belong to the loopless regime, or close to its boundary. ‡ 'the structure of the network is highly asymmetric. There are processes (transportation, electricity) that deliver to many other processes but that do not require inputs from these other processes. Conversely, there are processes (such as infrastructure-related processes) that have few customers, but that have many suppliers ' [15].
Introduction Production processes are central to organized societies and have thus been extensively studied in complex systems and complex networks literature from various viewpoints: interfirm supply-chain  [1, 2, 3] , trade  [4, 5, 6] , material flow analysis  [7]. Recent works use block models to reconstruct an interfirm network  [8, 9, 10, 11]  from partial knowledge, or model manufacturing process below the factory level  [12]. While they give deep insight into the structure of production processes, those works leave aside the technical details of production: inputs and outputs are usually aggregated, and detailed production processes are not considered. Furthermore, they neglect the ordering intrinsic to production processes and tend to focus on sectoral layers or communities  [13]. In the field of Life-cycle Inventory (LCI), at the crossroads of engineering, environment sciences and economy, large and detailed databases of production processes have been built up for decades, and offer a more precise description.
Similarly, several authors have analyzed IO datasets with network tools  [16]  or with some interest in topology  [17] , but those nets are highly aggregated, which leads to dense weighted networks where each sector seems connected to every other. Conversely, little attention was paid to LCI databases in the complex networks community. While complex networks methods are not likely to improve performance of classical problems in LCI represented algebraically (see sec 2. 2), they can help tackle problems more naturally expressed in a network way, and that so far remain unaddressed, for example: identifying central processes, probing the robustness to attacks of a given supply chain to avoid cascading failures, or examining dynamical phenomena such as propagation on the production network. In the present article, we thus look at LCI databases from a network science standpoint and describe the properties of production networks at the process level, to propose new explanations for unexplained observations.
Firstly, the observed networks are directed: each process takes inputs and generates outputs. Then, in LCI databases, process nodes are connected to product (or "flow") nodes, but are not directly connected, making the network bipartite or "two-mode". (As explained in sec. 2. 2 some LCI databases are built in a way such that they can be projected to monopartite networks, preserving some empirical properties). Being directed networks, production processes share some of their properties. It has been remarked already in the literature  [18, 19]  that real directed networks were "underlooped", that is they have less directed cycles than randomized versions of the network. Examples provided included a power grid, food webs, a metabolic network, the worldwide web, a neural network and a genetic transcription network.
In  [20]  the notion of inherent directionality is put forward, relying on a measure of hierarchical order inspired by trophic level  [21]. A model is proposed and compared to empirical networks, and confirms the underlooped nature of empirical networks. As will be shown in sec. 3 it turns out that this property is also present in production processes depicted by LCI databases, which has not been reported so far, up to our knowledge. The notion of hierarchical order was used in  [2] , and also in the random directed acyclic graphs (DAG) literature, which deals with random directed graphs without cycles. As explained in  [22] : "it is the ordering of the vertices and not their acyclic structure that is the definitive property of the network. The acyclic structure is merely a corollary of the ordering".
Recent works explain low abundance of loops in directed networks by the notion of trophic coherence, which quantifies to what extent a network can be ordered into discrete levels  [25, 26, 27, 28]. In these works, random graph ensembles are defined not from block-like assumptions but supposing that trophic coherence is constant, set to the empirically observed value. Some important properties of those random ensembles can be computed approximately and compared to empirical networks, for example the largest eigenvalue or the branching factor. Comparisons are made with life sciences (ecological food webs, genetic and metabolic), economic (international trade, inputoutput, supply-chains)  [29, 30] , linguistic and technical networks. Nevertheless, trophic coherence theory concerns general unipartite directed networks, which disregards the particularities of bipartite networks encountered in LCI datasets.
Conversely economic complexity measures such as country fitness and product complexity in  [5]  are adapted to bipartite undirected networks. We show below that some of the results of the trophic coherence theory can be extended to handle the bipartite case, providing the necessary correction to the bias induced by bipartivity. Then we analyze LCI datasets considered as networks, with a trophic coherence point of view and compare them to networks studied in other domains. Section 2 introduces some elements of the theory as well as the datasets. Section 3 discusses our results and applications, sec. 4 concludes.
LCI, datasets and methods In this section we discuss basic definitions and problems in the field of Life Cycle Inventory. Performing the LCI of a unit process consists in "the compilation and quantification of [its] inputs and outputs"  [33]. Inputs and outputs are usually called flows and each unit process is associated to a reference flow, i. e. its main output. A product system is a collection of such processes and flows. For example, production of electricity can be modeled as a unit process: in a simplified way, to produce 10 kWh of electricity it uses 2 litres of fuel, and outputs 1 kg of carbon dioxide CO 2 and 0. 1 kg of sulphur dioxide SO 2  [34]. There are several ways to represent unit processes and product systems  [15]. Firstly, a unit process can be represented as a process vector, for example p 1 = (-2, 10, 1, 0.
This constitutes Life Cycle Analysis (LCA), that is the assessment of the environmental impacts of a product system meeting a certain demand. Performing LCI and LCA, requires adequate datasets, prepared by experts and made available to process engineers. To put it simply, such datasets are large P matrices, with additional domain-specific metadata. fuel production of electricity Then, we present briefly the common characteristics of LCI datasets and preprocessing steps required to build bipartite and monopartite graphs from them. Databases are selected because they're free to download, and have the following description taken from nexus. openlca. org: CO 2 SO 2 elec. w 1 w 3 w 4 w 2 • Agribalyse: "the French LCI database for the agriculture and food sector (. ) comprises LCIs for 2 500 agricultural and food products produced and/or consumed in France".
• ELCD: "(European reference Life Cycle Database) comprises Life Cycle Inventory (LCI) data from EU-level business associations and other sources for key materials, energy carriers, transport, and waste management". • Worldsteel: "This study contains global and regional LCI data for 16 steel products, from hot rolled coil to plate, rebar, sections, and coated steels". • Bioenergiedat: "Processes for bioenergy supply chains, with German background". • Ozlci: "The database inventory groups cover 958 [Australasian regional] supply chains". It includes building products, chemicals, electric products, fabrics, farm and forest products, metal, minerals, as well as "utilities comprising use of freight, fuel, water and power by energy source and state grid". Some databases have a wide span, for example Agribalyse, which starts from the farm and goes to the distribution.
Others have a narrower scope, for example Worldsteel. In several databases, there is a convention that processes have a single product output, plus elementary flows. As in  [15] , we filter elementary flows since they do not connect vertices, which can result in processes having out-degree equal to one. We do not perform monopartite projection because it loses information and can modify dramatically the networks' properties  [35]. In the case where process out-degree is equal to one, some properties (e. g. in/out degree) are conserved, but this is not the general case for other properties. Additional preprocessing steps may be required, for example when modeling conventions result in spurious loops. This is documented for ELCD database in the case of transport , and will be dealt with below by selective node removal.
Other datasets Because of bipartivity, we can't directly compare our results to those in  [27]. For comparison purposes, a list of repositories of empirical networks was explored (see detailed list in Appendix D). Among all examined empirical networks tagged as directed and bipartite (2-mode), most have a trivial trophic structure. This is mostly because in those cases all edges from mode A to mode B have the same direction (for example in a plant/pollinator foodweb, all edges go from plants to pollinators). Those networks were thus excluded from our comparison basis. On the contrary, metabolic networks from the BiGG database  [37]  that represent chemical reactions on one hand and metabolites on the other, are bipartite and directed and don't have a trivial trophic structure. Instead of considering the whole set of reactions and metabolites which would not correspond to a physiological phenomenon, we follow the standard practice in flux balance analysis (FBA) which consists in maximizing an objective function (for example biomass production), which can be provided with the BiGG dataset.
Right nodes receive a total of L-L B 2 + L B = L+L B 2 edges. Noting sR the average trophic level of right nodes, and sL b (resp. sL nb ) the average trophic level of left basal (resp. non-basal) nodes, we get from the definition in eq. (  2 ): sL b = 1 (13) sR = 1 + 2L B L + L B + L -L B L + L B sL nb ( 14 ) Similarly, non-basal left nodes receive edges from right nodes only and we get: sL nb = 1 + sR ( 15 ) This yields: sR = L L B + 1 ( 16 ) sL nb = L L B + 2 (17) Left Right basal non-basal We remark that sR has the same value as snb in the monopartite case  [27] , which explains why qb is to leading order close to L L B when L is much larger than L B. This is treated in detail in Appendix B, and compared to numerical simulations. Lastly, we explored the possibility that the numbers of edges leaving non-basal left layer and leaving the right layer are unbalanced, to reflect what is observed empirically with the LCI dataset.