Data Definition Research Articles

Developing an Algorithm to Better Identify Pregnant Women with Inherited Bleeding Disorders within Electronic Health Records

Background: When using electronic health records (EHRs) to conduct population-based studies on inherited bleeding disorders (IBDs), multiple studies demonstrated that using diagnoses codes alone result in a high number of false positive identifications (J Blood Med. 2017 Aug 30;8:131-139). To improve data integrity, in-depth chart reviews are often required which typically can only be done from a local institutional level. This limits the potential to use national research infrastructure such as the National Patient-Centered Clinical Research Network (PCORnet) which draws its data from millions of EHRs across healthcare institutions in the United States. Objective: The objective of this study was to test the diagnostic accuracy of an algorithm that utilized common data definitions and multiple data elements in EHR to identify pregnant women with IBDs. Methods: We used data for pregnant women with IBDs who delivered at our institution from January 1, 2016 to December 31, 2020 to test the accuracy of the algorithm. Women with IBD included those with the following diagnoses: hemophilia and hemophilia carriers, von Willebrand disease and rarer bleeding disorders including factor II, V, VII, X, XI and XIII deficiencies. We did not include the following diagnoses: Platelet function disorder, fibrinogen disorder or hereditary hemorrhagic telangiectasia. We used EHR data elements from EPIC Clarity data model that conformed to those from the PCORnet Common Data Model (CDM). The PCORnet CDM can help ensure consistent data definitions and formats across multiple sites to facilitate large-scale patient-centered research. The original version of our algorithm included the following: (i) ICD-9/10 codes for an inherited bleeding disorder (Table 1) based on discharge diagnosis of any patient encounter visit types including inpatient, outpatient and emergency visits (criterion 1); (ii) Medications used for management of IBD (criterion 2); and (iii) Coagulation factor test and results (criterion 3). A validated diagnosis of IBD required fulfilling either criteria (1) and (2) or criteria (1) and (3). To develop our algorithm in an iterative fashion, we ran sequential queries (queries 1.0-1.1) with revisions to our initial criteria. The results of our retrospective queries were confirmed to have an IBD (true positive) using our local registry and by manual chart review. The local registry is a clinical database that is housed with EPIC and includes accurate demographic, phenotypic, and laboratory data of all persons with a confirmed diagnosis of IBD seen at our federally-funded adult hemophilia treatment center. Sensitivity and the positive predictive value of the algorithm to identify pregnant women with IBDs were calculated. Results: Using our original algorithm (query 1.0), there were 301 pregnant women who fulfilled criterion 1 and had at least one live birth or fetal death (>20 weeks gestation) at our institution during the study period. Of these, only 25 fulfilled criteria 2 and/or criteria 3. For the 276 cases that did not fulfill criteria 2 or 3, we verified the diagnosis through our local registry or through a manual review of the charts. We found that the following ICD-diagnosis codes for IBD - Z14.8; 286.9/D68.8/D68.9; 286.3/D68.2 - resulted in contamination and could not be used to diagnose carriers of IBD or the rarer bleeding disorders. These diagnoses codes were removed and we added a fourth criteria in our revised algorithm (Table 2). For cases that did not fulfill criteria 2 or 3, a case was considered as having a diagnosis of IBD if they had at least two identical ICD diagnosis code for an IBD in at least 2 separate patient encounter visit types (criterion 4). The revised algorithm (query 1.1) identified 35 pregnant women with IBD, of which 32 were confirmed to be true positive. The 3 women incorrectly identified (false positive) by our revised algorithm had seen a hematologist for work-up for a possible IBD which was eventually ruled out. From the original cohort of 301 pregnant women who had an ICD diagnosis code of an IBD, one patient who was a carrier for hemophilia A was missed (false negative) by our revised algorithm. The sensitivity of the revised algorithm was 97.0% and the PPV was 91.4% Conclusion: This study demonstrates the feasibility of an algorithm to accurately identify pregnant women with specific types of IBD within an EHR.

Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand

Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed.

Review: Toward sustainable management of groundwater in the deserts of Egypt

To achieve its ambitious plans to reclaim its deserts through mega projects, Egypt is heavily relying on fossil or little-recharged groundwater. This article revisits the results and methodologies of the studies conducted over the last two decades on groundwater management and uses in the Western Desert. Most previous studies aimed at simulating different groundwater abstraction scenarios by modeling local areas in aquifer systems, but with poor definitions of boundary conditions and limited historical data. Studies were constrained by the unavailability of data, access difficulties, and high collection costs in desert lands. Thus, to propose reliable sustainable groundwater resources development plans and recommendations for future protection strategies, an open-access monitoring network representing regional aquifers is needed. More investigations based on extensive field visits are essential to monitor environmental, economic, and social conditions, identify constraints, and learn lessons for reclaiming desert lands. Moreover, this review highlighted the need to frame a rational strategy for the long-term sustainable exploitation of non-renewable groundwater in the aquifer systems of Egypt and develop an appropriate exit strategy for desert communities in case of serious water resource depletion.

OGC P09 Benchmarking of OG cancer care across the United Kingdom: a collaboration of 2 national clinical audit systems

Abstract Background Clinical audit plays an important role in benchmarking cancer services to ensure the delivery of high-quality care. The UK has 2 separate clinical audits (NOGCA in England and Wales, and QPI data in Scotland) analysing the process and outcomes of care for oesophagogastric (OG) cancer patients. Both cover the care pathway from diagnosis to the end of primary treatment. Comparative analysis has not previously been undertaken between these 2 audits. We aim to demonstrate that the 2 systems can be interrogated to provide valuable population and benchmarking data, and present the results of a successful collaborative project. Methods Data was extracted from the 2 audits for all epithelial OG cancers diagnosed between 01/01/17 and 31/12/19 (n=34,584). Analysis was then undertaken, with a particular focus on: 1) disease stage at presentation; 2) proportion of patients undergoing CT staging 3) proportion of patients receiving curative/non-curative treatment; 4) Curative treatment patterns; and 5) short-term outcome measures (adjusted mortality, key pathology outcomes). Descriptive statistics were derived for each country and stratified by patient/tumour characteristics. Mortality rates and longitudinal margins were adjusted for case-mix using multiple logistic regression. Statistical tests were 2-sided; threshold of significance set at p<0.05. Results Baseline demographics were similar. Staging CTs were less commonly performed in England (83.1%; Scotland=98.5%; Wales=92.2%). Metastatic disease at presentation was highest in Scotland (40%-42%; England=30%-34%; Wales=36%-39%). England (40%) had highest proportion of MDT derived curative treatment plans (compared to Scotland=29% and Wales=34%). However, actual curative treatment rates (24-31%) were more similar and these differences narrowed after correction for population and staging differences. Curative treatment patterns are shown in Table 1. Mortality rates were similar in the 3 nations. Longitudinal margin positivity was significantly lower in Scotland (2.4%; England=5.4% and Wales=8.2%). Excision of >15 lymph nodes was highest in England. Conclusions The 2 audit systems used in the UK to analyse the care and outcomes for patients diagnosed with OG cancer share sufficient data definitions and overlap to allow meaningful comparison. The data presented here represents an important benchmark of current UK OG practice. Most results were similar although some important differences were highlighted which require further exploration.

Challenges of assessing the burden of sepsis.

Sepsis is one of the most frequent causes of death worldwide, but the recording of population-based epidemiology is challenging, which is why reliable data on sepsis incidence and mortality are only available in afew, mostly highly-resourced countries. The aim of this narrative review is to provide an overview of sepsis epidemiology worldwide and in Germany based on current literature, to identify challenges in this research area, and to give an outlook on future developments. Selective literature review. PubMed and Google Scholar were searched for current literature. The results were processed narratively. Based on modeling studies or meta-analyses of prospective studies, global annual sepsis incidence was found to be 276-678/100,000 persons. Case fatality ranged from 22.5 to 26.7%. However, current data sources have several limitations, as administrative data of selected individual countries-mostly with high income-were used as their basis. In these administrative data, sepsis is captured with limited validity. Prospective studies using clinical data often have limited comparability or lack population reference. There is alack of reliable data sources and definitions to monitor the epidemiology of sepsis and collect reliable global estimates. Increased policy efforts and new scientific approaches are needed to improve our understanding of sepsis epidemiology, identify vulnerable populations, and develop and target effective interventions.

The global economic burden of health anxiety/hypochondriasis- a systematic review

BackgroundRecent studies have shown a lifetime prevalence of 5.7% for health anxiety/hypochondriasis resulting in increased healthcare service utilisation and disability as consequences. To the best of our knowledge, there has been no systematic review examining the global costs of hypochondriasis, encompassing both direct and indirect costs. Our objective was to synthesize the available evidence on the economic burden of health anxiety and hypochondriasis to identify research gaps and provide guidance and insights for policymakers and future research.MethodsA systematic literature search was conducted using PubMed, Web of Science, PsycInfo, EconLit, IBSS and Google Scholar without any time limit, up until April 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in this search and the following article selection process. The included studies were systematically analysed and summarized using a predefined data extraction sheet.ResultsOf the 3044 articles identified; 10 publications met our inclusion criteria. The results displayed significant variance in the overall costs listed among the studies. The reported economic burden of hypochondriasis ranged from 857.19 to 21137.55 US$ per capita per year. Most of the investigated costs were direct costs, whereas the assessment of indirect costs was strongly underrepresented.ConclusionThis systematic review suggests that existing studies underestimate the costs of hypochondriasis due to missing information on indirect costs. Furthermore, there is no uniform data collection of the costs and definition of the disease, so that the few existing data are not comparable and difficult to evaluate. There is a need for standardised data collection and definition of hypochondriasis in future studies to identify major cost drivers as potential target point for interventions.

FAIRness of research data in humanities

Research data are considered the primary result and output of scientific research, and sharing and reusing data are key aspects of the transition to open science on a European level. However, there are many unanswered questions regarding research data management, such as understanding which data might be shared and under what conditions, who might share them, or even what research data is (Borgman, 2012). In the humanities, research data are among the most diverse of all scientific disciplines because nearly any data on human activity can be considered research data, such as newspapers or photographs. Thus, the boundary between data and publication in this context is often quite vague (Borgman, 2008; Thoegersen, 2018).This paper aims to examine and answer several research questions related to datasets in the field of humanities. The first research question will analyse the types of research data represented in humanities (1). In literature, a consensus has yet to be reached on the definition, and often, research data in the humanities are used as an umbrella term that includes different types of sources for research. DARIAH-DE defines research data in humanities as all sources/materials and results collected, written, described, and/or evaluated in the context of a research and research questions in the field of human and cultural sciences, presented in machine-readable form for archiving, citation and further use. This definition aims to account for the particular characteristics of human-scientific research and the resulting heterogeneity of the underlying data (DARIAH- DE, 2021). For the purpose of this research, the authors have chosen to adopt the DARIAH- DE definition of research data which will be the base for determining types of data represented in humanities. The research was conducted for humanities fields, including philosophy, theology, philology, history, art history, art science, archaeology, ethnology and anthropology, religious studies, and interdisciplinary humanities science. An analysis of datasets published in institutional and thematic repositories in the field of humanities in Croatia and Europe was conducted using platforms such as Zenodo, Digital Academic Archives and Repositories (DABAR), CLARIN, CROSSDA, and DARIAH. The research aimed to identify the most prevalent data types in the humanities found in repositories. In a study on the storage of research data from the humanities in repositories, Buddenbohm et al. (2016) note that a culture of sharing and reusing research data has not yet been established; although research data are, to some extent, stored in repositories, they are difficult to find. In light of this, the second research question will examine the extent to which datasets in the field of humanities are represented in repositories in open access and under which licences (2). The third research question will examine to what extent research data align with FAIR principles (3). The evaluation of dataset FAIRness will be conducted using the principles outlined in the Wilkinson et al. (2016) FAIR guidelines and Routledge Open Research data guidelines, as mentioned in Grant (2022) for FAIR humanities data. In addition, based on FAIR guidelines, authors will provide a practical framework for managing research data in humanities, which can be used to assess the quality of research datasets. The paper will present the types of research data in humanities that are represented in repositories, assess their level of openness, licensing, and alignment with FAIR data principles.

Safety assessment of tunnel construction based on counterintuitivity detection using multi-profile multi-model ensemble learning

Motivated to accurately predict the building tilt rate (BTR) in tunnel construction, a new multi-profile multi-model ensemble learning approach is proposed in this study by distinguishing between counterintuitive data and intuitive data, and handling each type appropriately. The proposed approach consists of three phases, namely the definition, identification, and handling of counterintuitive data, which are the major theoretical contributions of this study. Firstly, counterintuitive data is defined based on the input–output causal relation of safety-related data, introducing a novel concept in this research. Secondly, counterintuitive data is initially identified using a multi-profile ensemble learning approach and subsequently validated through multi-model ensemble learning. Finally, the identified and confirmed counterintuitive data is handled by assigning reduced weights. To validate the practicality of the approach, a case study on predicting the building tilt rate (BTR) in tunnel construction is conducted. The results of the case study demonstrate that the proposed multi-profile multi-model approach yields more accurate predictions of BTR compared to direct prediction and several other machine learning approaches. Furthermore, the proposed approach aids in identifying counterintuitive data in the testing dataset. Additionally, the effectiveness and superiority of the proposed approach are verified by comparing the prediction results when traditional abnormal data or random data is identified and treated as counterintuitive data.

A Contemporary Study of Pre-hospital Traumatic Cardiac Arrest: Distinguishing Exsanguination From Non-exsanguination Arrest With a Review of Current Literature.

Background Traumatic cardiac arrest (TCA) remains a challenging problem in terms of diagnosis and management. This is due to difficulty distinguishing the TCA cause and therefore understanding the pathophysiology. The goal of this study was to analyze a contemporary series of TCA patients and classify the causes of TCA into exsanguination (EX) arrest and non-exsanguination (non-EX) arrest. Methods This was a retrospective review of patients suffering TCA during 2019 at a level I trauma center. We excluded patients whose arrests were from medical causes such as ventricular fibrillation, ventricular tachycardia, pulmonary embolus, etc., hanging, drowning, thermal injury, outside transfer, and pediatric patients (age <13 as this is our institutional definition for pediatric trauma patients). We reviewed pre-hospital run-sheets, hospital charts including autopsy findings, and classified patients into EX and non-EX TCA. We defined a witnessed arrest (WA) using the traditional outside hospital cardiac (non-trauma) arrestdefinition. Outcomes included the incidence of EX arrest, survival to discharge, and hospital costs. Descriptive statistics were used.Significancewas set atp< 0.05. Results After exclusion, 54 patients suffered TCA with a mean age of 45.9 (±19.8) years.Eighty-three percent of patients were male. The average cost per TCA was ~$16,000. Of the 54 TCAs, 26 (48%) were WA, with one (1.85%) survivor (no non-WA TCA patients survived). Twenty-two (41%) patients died from EX-arrest; 59% penetrating vs. 28% blunt (p= 0.03). The one EX-arrest survivor was a 19-year-old gunshot wound to the leg whose arrest was witnessed, with a short downtime, and the cause of arrest (bleeding leg wound) was quickly reversible. Conclusion We classified 41% of TCAs to have died from EX-arrest with only a 1.85% survival rate. This study calls for a TCA pre-hospital registry with accurate and consistent data definitions and collection. The registry should capture the cause of arrest for future research, management decision-making, and prognostication.

Use of Big Data in the Cloud Computing

Abstract: Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized. This paper introduces several big data processing techniques from system and application aspects here provide an organized picture of challenges that are focused by the application developers and DBMS designers in developing cum deployment of the internet scale applications. Then we see about the security issues in the cloud computing along with the big data and Hadoop. We show some possible solutions for the issues of the cloud computing and Hadoop.

Вивчення ролі лейоміоми матки в прогнозуванні та лікуванні генітальних пролапсів

Uterine leiomyoma is one of the most common benign pelvic neoplasms in women. Uterine leiomyoma is diagnosed in perimenopausal or postmenopausal periods in more than 60% of cases. That is age category of women with a high frequency of genital prolapse with causes the frequent combination of these two pathologies. Purpose - to determine the role of uterine leiomyoma in the development and progression of genital prolapse. Materials and methods. A retrospective cohort study was conducted. It included 240 consecutively recruited patients, including: 117 women with prolapses of the internal genital organs, who made up the study group, and 123 women with normal pelvic anatomy - the comparison group. Analysis of life history, family, somatic, reproductive, gynecological and obstetric history, definition of anthropometric data was carried out. Statistical data processing was carried out using the SPSS 21 program. Results. According to the results of multivariate regression analysis, significant risk factors for the development of genital prolapse are: age, sedentary lifestyle, excessive physical activity, family history of genital prolapse, chronic obstructive pulmonary disease, uterine leiomyoma, number of pregnancies, spontaneous miscarriages in the early stages, number of deliveries, age of first childbirth, total number of intrauterine manipulations, perineal tears. Conclusions. Uterine leiomyoma is an independent risk factor for the development of genital prolapse (odds ratio: 5.93; 95% confidence interval: 1.77-19.91). The research was carried out in accordance with the principles of the Helsinki Declaration. The study protocol was approved by the Local Ethics Committee of the participating institution. The informed consent of the patient was obtained for conducting the studies. No conflict of interests was declared by the authors.

Bringing home the bacon: do politicians on boards increase firms’ government contracts?

Purpose The US Federal Government awards contracts worth hundreds of billions of dollars each year. Many firms that rely on these contracts have appointed former government officials to their corporate boards in the hopes of securing government contracts. The purpose of this paper is to examine the relationship between these government experienced directors (GEDs) on boards and firms being awarded government contracts. Design/methodology/approach The paper compiles a panel data set from 2017 to 2020 for S&amp;P 500 firms. This includes hand-collected data for government-experienced directors on boards. This is tested using both regression and analysis of variance methodologies. Findings Results find that former government officials on corporate boards increase the amount of government contracts secured by the firm, both in absolute terms and as a percentage of firms’ revenue. There are significant industry level effects for the health care and financials sectors. Government-experienced directors on boards are also positively related to firms receiving COVID contracts. Lobbying was not found to be related to the securing of regular government contracts but was positively related to firms obtaining COVID contracts. Originality/value This paper contributes to the literature by using panel data, an expanded definition of GEDs and data on COVID contracts. The “revolving door” between government and firms is paying off for companies.

Secure IoT data management and sharing architecture for information security using cryptographic technique

The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture.

Eris: efficiently measuring discord in multidimensional sources

AbstractData integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an accurate merging of values. Thus, in the absence of ways to determine ground truth, it is important to at least quantify how far from being internally consistent a dataset is. Hence, we propose definitions of concordant data and define a discordance metric as a way of measuring disagreement to improve decision-making based on trustworthiness. We define the discord measurement problem of numerical attributes in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different conceptualizations of the same reality (e.g., granularities or units), we wish to assess whether the different sources are concordant, or if not, use the discordance metric to quantify how discordant they are. We also define a set of algebraic operators to describe the alignments of different data sources with correctness guarantees, together with two alternative relational database implementations that reduce the problem to linear or quadratic programming. These are evaluated against both COVID-19 and synthetic data, and our experimental results show that discordance measurement can be performed efficiently in realistic situations.

Thermal Imaging in Robotics as a Privacy-Enhancing or Privacy-Invasive Measure? Misconceptions of Privacy when Using Thermal Cameras in Robots

This article examines the use of thermal imaging in human–robot interactions and its implications for privacy. Thermal imaging is used in robotics research to conceal the identity of individuals by masking identifiable information, such as facial details. The assumption is that by removing clear facial images, individuals become unidentifiable, thus protecting their privacy. However, the definition of personal data does not require information to be linked to a visual observation of a person. Removing the facial image of a person does not exclude the information from being personal data. However, thermal imaging can reveal personal information that is hidden from view. The article argues that privacy in robotics research is often limited to informational privacy and neglects other dimensions of privacy. It emphasises the need for a broader understanding of privacy in the context of robotics, considering the physical embodiment and data processing capabilities of robots. The conclusion of the article suggests that thermal imaging should not be automatically assumed to be more privacy-preserving than regular RGB cameras. It emphasises the importance of understanding personal data and the specific purposes of robot functions when choosing between different sensors. The article calls for a comprehensive approach to privacy in human–robot interaction, taking into account the broader impact of invasive technologies.

Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS

BackgroundDosimetric validation of single isocenter multi-target radiosurgery plans is difficult due to conditions of electronic disequilibrium and the simultaneous irradiation of multiple off-axis lesions dispersed throughout the volume. Here we report the benchmarking of a customizable Monte Carlo secondary dose calculation algorithm specific for multi-target radiosurgery which future users may use to guide their commissioning and clinical implementation. PurposeTo report the generation, validation, and clinical benchmarking of a volumetric Monte Carlo (MC) dose calculation beam model for single isocenter radiosurgery of intracranial multi-focal disease. MethodsThe beam model was prepared within SciMoCa (ScientificRT, Munich Germany), a commercial independent dose calculation software, with the aim of broad availability via the commercial software for use with single isocenter radiosurgery. The process included (1) definition & acquisition of measurement data required for beam modeling, (2) tuning model parameters to match measurements, (3) validation of the beam model via independent measurements and end-to-end testing, and finally, (4) clinical benchmarking and validation of beam model utility in a patient specific QA setting. We utilized a 6X Flattening-Filter-Free photon beam from a TrueBeam STX linear accelerator (Siemens Healthineers, Munich Germany). ResultsIn addition to the measured data required for standard IMRT/VMAT (depth dose, central axis profiles & output factors, leaf gap), beam modeling and validation for single-isocenter SRS required central axis and off axis (5 cm & 9 cm) small field output factors and comparison between measurement and simulation of backscatter with aperture for jaw much greater than MLCs. Validation end-to-end measurements included SRS MapCHECK in StereoPHAN geometry (2%/1 mm Gamma = 99.2% ± 2.2%), and OSL & scintillator measurements in anthropomorphic STEEV phantom (6 targets, volume = 0.1–4.1cc, distance from isocenter = 1.2–7.9 cm) for which mean difference was −1.9% ± 2.2%. For 10 patient cases, MC for individual PTVs was −0.8% ± 1.5%, −1.3% ± 1.7%, and −0.5% ± 1.8% for mean dose, D95%, and D1%, respectively. This corresponded to custom passing rates action limits per AAPM TG-218 guidelines of ±5.2%, ±6.4%, and ±6.3%, respectively. ConclusionsThe beam modeling, validation, and clinical action criteria outlined here serves as a benchmark for future users of the customized beam model within SciMoCa for single isocenter radiosurgery of multi-focal disease.

Multiple psychiatric polygenic risk scores predict associations between childhood adversity and bipolar disorder

BackgroundIt remains unclear how adverse childhood experiences (ACE) and increased genetic risk for bipolar disorder (BD) interact to influence BD symptom outcomes. Here we calculated multiple psychiatric polygenic risk scores (PRS) and used the measures of ACE to understand these gene-environment interactions. Method885 BD subjects were included for analyses. BD, ADHD, MDD and SCZ PRSs were calculated using the PRS-CS-auto method. ACEs were evaluated using the Children Life Event Questionnaire (CLEQ). Participants were divided into groups based on the presence of ACE and the total number of ACEs. The associations between total ACE number, PRSs and their interactions were evaluated using multiple linear and logistic regressions. Secondary analyses were performed to evaluate the influence of ACE and PRS on sub-phenotypes of BD. ResultsThe number of ACEs increased with the ADHD PRS. BD participants who had ACEs showed an earlier age of BD onset and higher odds of having rapid cycling. Increased BD PRS was associated with increased odds of developing psychotic symptoms. Higher ADHD PRS was associated with increased odds of having rapid cycling. No prediction effect was observed from MDD and SCZ PRS. And, we found no significant interaction between ACE numbers and any of the PRSs in predicting any selected BD sub-phenotypes. LimitationsThe study was limited by sample size, ACE definition, and cross-sectional data collection method. ConclusionsThe findings consolidate the importance of considering multiple psychiatric PRSs in predicting symptom outcomes among BD patients.

Metadata stewardship in nanosafety research: learning from the past, preparing for an “on-the-fly” FAIR future

Introduction:Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this “silo” approach is hampering their general adoption by the broader research community and individual labs.Methods:State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results:The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable “on-the-fly” (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion:The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.

Lessons learned through the 20-year development of a national fatal drowning database in Australia

BackgroundCo-ordinated, evidence-based policy and programmatic efforts are needed to respond to complex drowning prevention problems. Comprehensive, current, and robust data are vital for agenda setting, burden and risk factor identification, intervention design and evaluation, as well as setting policy. We aim to record methods used in, and identify impacts of, the development of a national fatal drowning database (NFDD) in Australia, including lessons learned across research, policy, and practice.MethodsWe employ a case study method using process mapping and document review to explore the evolution, drivers and impacts of the NFDD. We analyse methodological approaches including those relating to data definitions, drowning case collection, and management, as well as tracking the various outputs of the NFDD. We describe a development timeline that presents impact of drowning prevention policy, and research agendas on database development, and research investments more specifically.ResultsOur study identified that the collected variables grew 20-fold from 2002 to 2022, reaching 259 variables, and 5,692 unique cases of fatal drowning. The NFDD employs data triangulation methodology, combining keyword and targeted searches of coronial files, media report monitoring, and organisational data provision. Database development is influenced by the Australia Water Safety Strategy, policymaker and practitioner-initiated research agendas, and identification of knowledge gaps. We identified numerous outputs spanning publications, media, intervention development, and legislative submissions.ConclusionA comprehensive and robust NFDD informed by policymaker and practitioner input can enhance surveillance, policy, and intervention development for drowning prevention. Employing mixed data collection and validation methods can supplement weaknesses in official data sources. There is a need for the NFDD to continue to evolve in its application while maintaining rigorous case identification and data quality assurance processes. Despite significant investment, the outputs and influence on drowning prevention practice in Australia has been extremely valuable and contributed to sizeable reductions in Australia’s fatal drowning rate.

Data provenance - from experimental data to trustworthy simulation models and standards

FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general. When implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods and tools is outlined.We begin with a motivation on potential use cases for the application of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.In a second part, different options for structuring the data (including metadata schema) are discussed. The first one is the openBIS system, which is an open-source lab notebook and PostgreSQL based data management system. A second option is a semantic representation using RDF based on ontologies for the domain of interest.In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. A metadata schema for a rheological measurement setup is derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behavior, the automatic workflow to use the stored data for model parameter estimation is demonstrated.