Aggregate Data Research Articles

Mobility-aware federated self-supervised learning in vehicular network

The development of the Internet of Things has led to a significant increase in the number of devices, consequently generating a vast amount of data and resulting in an influx of unlabeled data. Collecting these data enables the training of robust models to support a broader range of applications. However, labeling these data can be costly, and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things, where new data continuously emerge. To address this challenge, Self-Supervised Learning (SSL) offers a way to train models without the need for labels. Nevertheless, the data stored locally in vehicles are considered private, and vehicles are reluctant to share data with others. Federated Learning (FL) is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously. Additionally, vehicles capture images while driving through cameras mounted on their rooftops. If a vehicle’s velocity is too high, the captured images, donated as local data, may be blurred. Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL. This paper proposes a FL algorithm for aggregation based on image blur levels, which is called FLSimCo. This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks. Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.

Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.

Evaluating Audience Engagement as a Measure of Digital Diplomacy Effectiveness

This article presents relevant tools for analyzing engagement in digital diplomacy, where ‘engagement’ is defined as the two-way interaction between a digital diplomacy channel on social media and its users. Existing methods for evaluating engagement often fail to fully utilize the available data or lack the flexibility needed for specific diplomatic purposes. Using the case study of US digital diplomacy in Russia, this paper addresses two research questions: (1) To what extent are the automated evaluation metrics developed by data aggregator platforms applicable for analyzing engagement in digital diplomacy? (2) Do these metrics help identify significant patterns in digital diplomacy?The findings of our pilot study indicate that the automated engagement metrics provided by the Popsters and TGStat data aggregator services effectively measure engagement in terms of audience reach and expansion, geographic distribution of traffic, overall engagement, and engagement based on content type (text, photo, video, or link) or text length. The analysis of selected metrics reveals two key trends in US digital diplomacy in Russia: since 2022 there has been a notable shift in focus toward Telegram as the primary platform, and while the content partially reaches the existing Russian-speaking audience, it struggles to expand its overall reach.The authors provide six recommendations for diplomatic agencies and consulting firms to enhance the tools for analyzing digital diplomacy engagement: (1) adopt automated engagement metrics that account for the interface differences among social media platforms; (2) incorporate built-in topic modeling of posts; (3) integrate sentiment analysis of comments; (4) develop mechanisms to detect traffic manipulation; (5) track multi-level reposts, the timing and spread of hashtag diplomacy, and audience growth considering these processes; and (6) evaluate digital diplomacy activity concerning responses to comments. The authors advocate for closer collaboration between the academic community and diplomatic practitioners to improve engagement metrics and thereby enhance the effectiveness of digital diplomacy.

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation. ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.

Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves

Data centralization is increasingly non-viable in the changing landscape of data privacy and data governance. New models of data sharing are needed, in particular when privacy-preserving record linkages (PPRL) are being used to connect data within restricted data enclaves and public open enclaves. We share a privacy-preserving data-sharing infrastructure that employs a linkage honest broker implementation of PPRL connecting structured electronic health record data between the National Center for Advancing Translational Science (NCATS) National Clinical Cohort Collaborative (N3C), a restricted data enclave, with the National Cancer Institute (NCI) Cancer Imaging Archive (TCIA), a public open enclave. In particular we will address: Data governance models needed when linking data from restricted vs. public enclaves Specific privacy-preserving consideration when linking to imaging data which contain specific structured data and imaging considerations Privacy-preserving data aggregation when combining multi-modal data from restricted data enclaves with publicly discoverable imaging data

Implementing Linkage Quality Assessments in Large-Scale Privacy Preserving Infrastructure: Did we kill them a second time and why are there zombies?

The National Clinical Cohort Collaborative (N3C) uses privacy-preserving record linkage (PPRL) to formulate longitudinal patient histories and for de-duplication across disparate data including electronic health records, administrative claims, and mortality data. The N3C employs a linkage honest broker implementation model which separates the entity holding de-identified PPRL tokens, from the entity holding the substantive data payloads. As a result of this data governance structure, linkage quality assessments are necessarily distributed into 2 distinct processes: 1) Linkage Honest Broker receipt and processing of linkages, 2) Data Aggregation entity assembling data based on received linkage crosswalks. Data is released on a weekly basis. This presentation will focus on Step 2 for the Mortality data linkages where the downstream data linkage pipelines must assess linkage quality across several dimensions of quality, focused on plausibility. There are 4 types of mortality data spanning the electronic health record, government Social Security Administration death master files, third-party obituary data, and administrative claims. Each of these sources has different levels of authoritativeness and data latency, compounding the challenges of reconciling fact of death including accurate death reporting dates. To address these challenges, we implemented an automated linkage quality pipeline which generates reports that assess linkage plausibility across the various data types, classifying concordance and discordance between data sources including age, dates of death, surfacing plausible and non-plausible activities post-death. Sharing our real-world implementation will aid conference attendees in understanding how to plan for such activities when considering specific implementation models for privacy-preserving linkages.

An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network

An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network

RDSC: Range-Based Device Spatial Clustering for IoT Networks.

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms.

Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment

SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.

Hybrid optimization‐based topology construction and DRNN‐based prediction method for data reduction in IoT

SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.

A systematic review and meta-analysis of HHV-6 and mortality after hematopoietic cell transplant.

Human herpesvirus-6B (HHV-6B) reactivation has been associated with non-relapse mortality (NRM) and overall mortality (OM) following allogeneic hematopoietic stem cell transplant (HCT). We performed a systematic review and meta-analysis to better quantify the association. Studies were included if they systematically tested a cohort of HCT recipients for HHV-6 infection or reactivation and described mortality for patients with and without HHV-6B. Random effects models were used to assess the pooled effect of HHV-6B positivity on each outcome of interest. Bayesian aggregation was additionally performed if models included 10 or fewer studies. Eight studies were included in the NRM analysis, which demonstrated a significant association between HHV-6 detection and NRM (pooled effect: 1.84; 95% CI: 1.29-2.62) without significant heterogeneity (I2 = 0.0%, p = 0.55). A Bayesian aggregation of the raw data used to construct the NRM random effects model supported these findings (95% credible interval: 0.15-1.13). Twenty-five studies were included in OM analysis, which showed a significant positive association (pooled effect: 1.37; 95% CI: 1.07-1.76), though considerable heterogeneity was observed (I2 = 36.7%, p < 0.05). HHV-6 detection is associated with NRM and OM following HCT. Randomized trials are warranted to evaluate if preventing or treating HHV-6B reactivation improves outcomes.

Engaging healthcare teams to increase access to medications for opioid use disorder.

To assess the effectiveness of evidence-based quality improvement (EBQI) as an implementation strategy to expand the use of medications for opioid use disorder (MOUD) within nonspecialty settings. We studied eight facilities in one Veteran Health Administration (VHA) region from October 2015 to September 2022 using administrative data. Initially a pilot, we sequentially engaged seven of eight facilities from April 2018 to September 2022 using EBQI, consisting of multilevel stakeholder engagement, technical support, practice facilitation, and data feedback. We established facility-level interdisciplinary quality improvement (QI) teams and a regional-level cross-facility collaborative. We used a nonrandomized stepped wedge design with repeated cross sections to accommodate the phased implementation. Using aggregate facility-level data from October 2015 to September 2022, we analyzed changes in patients receiving MOUD using hierarchical multiple logistic regression. Eligible patients had an opioid use disorder (OUD) diagnosis from an outpatient or inpatient visit in the previous year. Receiving MOUD was defined as having been prescribed an opioid agonist or antagonist treatment or a visit to an opioid substitution clinic. The probability of patients with OUD receiving MOUD improved significantly over time for all eight facilities (average marginal effect [AME]: 0.0057, 95% CI: 0.0044, 0.0070) due to ongoing VHA initiatives, with the probability of receiving MOUD increasing by 0.577 percentage points, on average, each quarter, totaling 16 percentage points during the evaluation period. The seven facilities engaging in EBQI experienced, on average, an additional 5.25 percentage point increase in the probability of receiving MOUD (AME: 0.0525, 95%CI: 0.0280, 0.0769). EBQI duration was not associated with changes. EBQI was effective for expanding access to MOUD in nonspecialty settings, resulting in increases in patients receiving MOUD exceeding those associated with temporal trends. Additional research is needed due to recent MOUD expansion legislation.

Meta-analysis application to hERG safety evaluation in clinical trials

ABSTRACT One objective of meta-analysis, which synthesizes evidence across multiple studies, is to assess the consistency and investigate the heterogeneity across studies. In this project, we performed a meta-analysis on moxifloxacin (positive control in QT assessment studies) data to characterize the exposure–response relationship and determine the safety margin associated with 10-msec QTc effects for moxifloxacin based on 26 thorough QT studies submitted to the FDA. Multiple meta-analysis methods were used (including two novel methods) to evaluate the exposure–response relationship and estimate the critical concentration and the corresponding confidence interval of moxifloxacin associated with a 10-msec QTc effect based on the concentration-QTc models. These meta-analysis methods (aggregate data vs. individual participant data; fixed effect vs. random effect) were compared in terms of their precision and robustness. With the selected meta-analysis method, we demonstrated the homogeneity and heterogeneity of the moxifloxacin concentration-QTc relationship in studies. We also estimated the critical concentration of moxifloxacin that can be used to calculate the hERG safety margin of this drug.

The use of the Bennet indicators and their transitive versions for scanner data analysis

Although modern price index theory is based on comparisons of ratios of prices, quantities and expenditures, we may be more interested in the magnitude of differences in these characteristics in many business applications. The benefit of using these differences is that there is no problem associated with the occurrence of zero prices and quantities, a problem that arises when we work with ratios. In practice, we most often care about decomposing value difference into indicators of contributions from price and quantity differences. The best-known price and quantity indicators are the Bennet indicators, which are not transitive. Although there have been papers in the literature that propose a transitive version of the Bennet indicators, they deal with comparisons across firms in cross-section or panel contexts. This paper revises the price and quantity Bennet indicators and their multilateral versions for the analysis of scanner data. Specifically, i nstead o f c onsidering c omparisons across firms, c ountries or r egions, the t ransitive versions of t he Bennet i ndicators a re a dapted to work on scanner data sets observed over a fixed time w indow. Since the scanner data sets have a high turnover of products, which can make it difficult to interpret the difference in sales values over the compared time periods, the paper also considers a matched sample approach. One of the objectives of the study is to compare bilateral and multilateral Bennet indicator results across all available products or strictly matched products over time. It also examines the impact of data filters used and the level of data aggregation on the price and quantity Bennet indicators. According to the best author’s knowledge, this study is a pioneer in the field of implementing the multilateral Bennet indicators in scanner data analysis.

Global comparison of urban bike-sharing accessibility across 40 cities

The global expansion of bike-sharing networks offers a cost-effective and environmentally friendly transportation alternative that complements public transit and promotes active, healthy lifestyles. Despite significant research, most studies focus on individual locations, specific environmental or health impacts, or infrastructure elements like bike lanes. Instead, this paper elaborates a comprehensive global comparison of bike-sharing systems by introducing a novel database that aggregates data from 40 cities worldwide. Our study integrates this data with population data and urban metrics to classify these networks topologically and assess their effective coverage concerning the population served and their relation with existing public transit systems. We introduce the “Bike-Share Service Accessibility Index” (BSAI), a new metric to evaluate and compare the performance of bike-sharing networks. Our findings provide valuable insights for urban planners and policymakers, offering data-driven strategies to enhance sustainable urban mobility through better-integrated and more spatially equitable bike-sharing systems.

Social and Environmental Determinants of Health and Cardio-Kidney-Metabolic Syndrome–Related Mortality

It is not well understood if and how various social and environmental determinants of health (SEDoH) are associated with mortality rates related to cardio-kidney-metabolic syndrome (CKM) across the US. To study the magnitude of the association strength of SEDoH with CKM-related mortality at the county level across the US. This cross-sectional, retrospective, population-based study used aggregate county-level data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) data portal from 2010-2019. Data analysis occurred from September 2023 to January 2024. A total of 7 diverse SEDoH were chosen, including median annual household income, percentage of racial and ethnic minority residents per county, fine particulate air pollution (PM2.5) concentrations, high-school completion rate, primary health care access, food insecurity, and rurality rate. The primary outcome was county-level age-adjusted mortality rate (aaMR) attributable to CKM. The association of county-level CKM-related aaMR with the 7 SEDoH was analyzed using geographically weighted models and the model median coefficients for each covariate studied. Data from 3101 of 3243 counties (95.6%) were analyzed. There was substantial variation in SEDoH between states and counties. The overall pooled median (IQR) aaMR (2010-2019) in the US was 505.5 (441.3-578.9) per 100 000 residents. Most counties in the lower half of the US had rates much higher than the pooled median (eg, Southern US median [IQR] aaMR, 537.3 [466.0-615.9] per 100 000 residents). CKM-related mortality was positively associated with the food insecurity rate (median [IQR] β = 6.78 [2.78-11.56]) and PM2.5 concentrations (median [IQR] β = 5.52 [-11.06 to 19.70]), while it was negatively associated with median annual household income (median [IQR] β = -0.002 [-0.003 to -0.001]), rurality (median [IQR] β = -0.32 [-0.67 to 0.02]), high school completion rate (median [IQR] β = -1.89 [-4.54 to 0.10]), racial and ethnic minority rate (median [IQR] β = -0.66 [-1.85 to 0.89]), and primary health care access rate (median [IQR] β = -0.18 [-0.35 to 0.07]). In this cross-sectional study of county-level data across the US, there were substantial geographical differences in the magnitude of the association of SEDoH with CKM-related aaMR. These findings may provide guidance for deciding local health care policy.

Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland I. Dynamic linear models at production cycle level

The mortality of Atlantic salmon is one of the main challenges to achieving its sustainable production. This sector benefits from generating many data, some of which are collated in a standardized way, on a monthly basis at site level, and are accessible to the public. This continuously updated resource might provide opportunities to monitor mortality and prompt producers and inspectors to further investigate when mortality is higher than expected. This study aimed to use the available open-source data to develop production cycle level dynamic linear models (DLMs) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. To achieve this, several production cycle level DLMs were created: one univariate DLM that includes just mortality; and various multivariate DLMs that include mortality and different combinations of environmental variables. While environmental information is not collated in a standardized way across all sites, open-source remote-sensed satellite resources provide continuous, standardized estimates. By combining environmental and mortality data, we seek to investigate whether adding environmental variables enhanced the estimates of mortality, and if so, which variables were most informative in this respect. The multivariate model performed better than the univariate DLM (P = .004), with salinity as the only significant contributor out of 12 environmental variables. Both models exhibited uncertainty related to the mortality estimates. Warnings were generated when any observation fell above the 95% credible interval. Approximately 30% of production cycles and more than 50% of sites experienced at least one warning between 2015 and 2020. Occurrences of these warnings were non-uniformly distributed across space and time, with the majority happening in the summer and autumn months. Recommendations for model improvement include employing shorter time periods for data aggregation, such as weekly instead of on a monthly basis. Furthermore, developing a model that takes hierarchical relationships into account could offer a promising approach.

Socio-cultural challenges in collections digital infrastructures

PurposeThis paper aims to explore the accelerations and constraints libraries, archives, museums and heritage organisations (“collections-holding organisations”) face in their role as collection data providers for digital infrastructures. To date, digital infrastructures operate within the cultural heritage domain typically as data aggregation platforms, such as Europeana or Art UK.Design/methodology/approachSemi-structured interviews with 18 individuals in 8 UK collections-holding organisations and 2 international aggregators.FindingsDiscussions about digital infrastructure development often lay great emphasis on questions and problems that are technical and legal in nature. As important as technical and legal matters are, more latent, yet potent challenges exist too. Though less discussed in the literature, collections-holding organisations' capacity to participate in digital infrastructures is dependent on a complex interplay of funding allocation across the sector, divergent traditions of collection description and disciplinaries’ idiosyncrasies. Accordingly, we call for better social-cultural and trans-sectoral (collections-holding organisations, universities and technological providers) understandings of collection data infrastructure development.Research limitations/implicationsThe authors recommend developing more understanding of the social-cultural aspects (e.g. disciplinary conventions) and their impact on collection data dissemination. More studies on the impact and opportunities of unified collections for different audiences and collections-holding organisations themselves are required too.Practical implicationsSustainable financial investment across the heritage sector is required to address the discrepancies between different organisation types in their capacity to deliver collection data. Smaller organisations play a vital role in diversifying the (digital) historical canon, but they often struggle to digitise collections and bring catalogues online in the first place. In addition, investment in existing infrastructures for collection data dissemination and unification is necessary, instead of creating new platforms, with various levels of uptake and longevity. Ongoing investments in collections curation and high-quality cataloguing are prerequisites for a sustainable heritage sector and collection data infrastructures. Investments in the sustainability of infrastructures are not a replacement for research and vice versa.Social implicationsThe authors recommend establishing networks where collections-holding organisations, technology providers and users can communicate their experiences and needs in an ongoing way and influence policy.Originality/valueTo date, the research focus on developing collection data infrastructures has tended to be on the drive to adopt specific technological solutions and copyright licensing practices. This paper offers a critical and holistic analysis of the dispersed experience of collections-holding organisations in their role as data providers for digital infrastructures. The paper contributes to the emerging understanding of the latent factors that make infrastructural endeavours in the heritage sector complex undertakings.

Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study.

The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

The anterior and medial open approaches yield superior outcomes compared to the lateral and posterior open approaches in supracondylar humerus fractures in children: a systematic review and network meta-analysis.

This study aimed to perform a systematic review and network meta-analysis (NMA) to examine which open approach is superior in terms of outcomes and complications in the treatment of pediatric supracondylar humerus fractures. MEDLINE/PubMed, Embase, Web of Science, Clinicaltrials.gov, and Cochrane Library were searched from database inception to December 2022 and screened for relevant studies. Data were collected regarding patient demographics, Flynn's functional and cosmetic outcomes, and complications. Unsatisfactory Flynn's and complications were considered negative events. Comparisons of outcomes from aggregate data from each surgical approach using relative risk (RR) with a 95% confidence interval (95% CI) were performed. The NMA of overall negative events was conducted using a Bayesian hierarchical random-effects model analysis. A total of 26 studies involving 1461 patients were included; 459 (31.4%) patients underwent a closed reduction and percutaneous pinning (CRPP), 84 (5.7%) an anterior approach, 240 (16.4%) a medial, 220 (15%) a lateral, and 458 (31.3%) a posterior. The lateral and posterior approaches demonstrate a higher risk of negative event in the NMA compared to CRPP [RR = 2 (1.03, 3.85); RR = 2.63 (1.96, 3.57), respectively], anterior approach [RR = 3.33 (1.11, 10); RR = 4.35 (1.49, 12.5), respectively], and medial approach [RR = 1.82 (1.16, 2.86); RR = 2.38 (1.23, 4.76), respectively]. The medial approach resulted in a similar negative event rate compared to the anterior [RR = 1.82 (0.58, 5.88)]. The anterior and medial open approaches yield superior functional and cosmetic outcomes with fewer complications compared to the lateral and posterior.