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Introduction: Data quality is essential for trauma registries, but few tools have been developed to maximize it. The author's center created a new application to automatically identify >500 logic errors in registry data and produce individualized data quality reports for staff. Objective metrics indicated the application is effective, but staff perceptions were unknown. The aim of this project was to assess registry staff satisfaction with and perceived usefulness of the new application. Methods: Anonymous cross-sectional online survey with 5-point Likert scales and free-text responses. Results: Of 10 eligible staff members who use the new application, 8 responded to the survey. Confidence in data accuracy before the new application was generally low but unanimously higher after implementation (8/8; 95% CI, 5/8-8/8). Respondents found the application at least somewhat helpful overall (6/6; 95% CI, 3/6-6/6; 2 nonresponses), with 5/6 finding it very helpful. Respondents were at least somewhat satisfied with the new application (8/8; 95% CI, 5/8-8/8), with 4/8 being very satisfied. There was minimal negative feedback other than the new process being initially overwhelming. Conclusions: Respondents found the new application to be beneficial in several ways, including indirectly. Additional research is needed to be able to generalize these single center findings and determine best practices for data validation, but software-based approaches to augment more common data validation processes may be a beneficial and welcomed job aid for registry staff.

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This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture's advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture's capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.

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Cold sores require Healthcare professionals to employ specific approaches for prevention and management, with the need for effective therapeutic guidelines and ongoing improvement in patient care. AIM: To evalúate the methodological quality of Clinical Guidelines (CG), clinical guides and manuals for care of the population affected by herpes labialis, to verify their compliance with evidence-based health standards. MATERIALS AND METHOD: A search was conducted for CG on labial herpes in the MedicalLiteratureAnalysis andRetrieval System Online (Medline) database, Google Scholar, Brazilian Virtual Health Library (BVS), and sites of institutions/professional categories, using the descriptors "herpes labialis" or "oral herpes". Document quality was assessed using the Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II). The Kappa test was used to avoid randomness or poor agreement between results. RESULTS: Analysis of the 12 selected publications on the management of labial herpes revealed flaws in quality, as the publications did not follow a quality standard. The main quality flaws identified were in "rigor in development" and "applicability. ". CONCLUSIONS: Priorities need to be redefined in the development of CG for clinical practice related to fever blisters to reduce the variability of the quality standard, and generate reliable, applicable recommendations.

A Herpes labial requer dos profissionais abordagens específicas para prevenido e manejo, com a ne-cessidade de diretrizes terapéuticas eficazes e continuo aprimoramento do cuidado ao paciente. OBJETIVO: avaliar a qualidade metodológica de documentos que abordaram Diretrizes Clínicas (DC), guias clínicos e manuais para o cuidado da populando afetada pelo herpes labial, verificando sua conformidade com padroes de saúde baseados em evidencias. MATERIAIS E MÉTODO: As DC sobre herpes labial foram pesquisadas na base de dados Medical Literature Analysis and Retrieval System Online (Medline), Google Académico, Biblioteca Virtual em Saúde (BVS) e em sites de instituigoes/categorias profissionais, utilizando os descritores "herpes labial" ou "herpes oral". Utilizamos aferramenta The Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II) para a avaliagdo da qualidade. O teste Kappa também foi utilizado para evitar aleatoriedade ou baixa concordáncia entre os resultados. RESULTADOS: Na análise das 12 publicagoes selecionadas sobre o manejo do herpes labial, foram identificadas falhas na qualidade dos documentos, que ndo seguiram um padrdo de qualidade. As principais falhas de qualidade identificadas foram em "rigor no desenvolvimento" e "aplicabilida-de". CONCLUSÃO: é necessário um reenfoque para definir prioridades no desenvolvimento de DC para a prática clínica do herpes labial, a fim de reduzir a variabilidade do padrdo de qualidade e gerar recomendagoes que possam ser confiáveis e aplicáveis.

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The standard approach for detecting and preventing bots from doing harm online involves CAPTCHAs. However, recent AI research, including our own in this manuscript, suggests that bots can complete many common CAPTCHAs with ease. The most effective methodology for identifying potential bots involves completing image-processing, causal-reasoning based, free-response questions that are hand coded by human analysts. However, this approach is labor intensive, slow, and inefficient. Moreover, with the advent of Generative AI such as GPT and Bard, it may soon be obsolete. Here, we develop and test various automated, bot-screening questions, grounded in psychological research, to serve as a proactive screen against bots. Utilizing hand coded free-response questions in the naturalistic domain of MTurkers recruited for a Qualtrics survey, we identify 18.9% of our sample to be potential bots, whereas Google's reCAPTCHA V3 identified only 1.7% to be potential bots. We then look at the performance of these potential bots on our novel bot-screeners, each of which has different strengths and weaknesses but all of which outperform CAPTCHAs.

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INTRODUCTION: Health care patient records have been digitalised the past twenty years, and registries have been automated. Missing registrations are common, and can result in selection bias. OBJECTIVE: To assess the prevalence and characteristics of missed registrations in a Dutch regional trauma registry. METHODS: An automatically generated trauma registry export was done for ten out of eleven hospitals in trauma region Southwest Netherlands, between June 1 and August 31, 2020. Second, lists were checked for being falsely flagged as 'non-trauma'. Finally, a list was generated with trauma tick box flagged as 'trauma' but were not automatically in the export due to administrative errors. Automated and missed registration datasets were compared on patient characteristics and logistic regression models were run with random intercepts and missed registration as outcome variable on the complete dataset. RESULTS: A total of 2,230 automated registrations and 175 (7.3 %) missed registrations were included for the Dutch National Trauma Registry, ranging from 1 to 14 % between participating hospitals. Patients of the missed registration dataset had characteristics of a higher level of care, compared with patients of automated registrations. Level of trauma care (level II OR 0.464 95 % CI 0.328-0.666, p < 0.001; level III OR 0.179 95 % CI 0.092-0.325, p < 0.001), major trauma (OR 2.928 95 % CI 1.792-4.65, p < 0.001), ICU admission (OR 2.337 95 % CI 1.792-4.650, p < 0.001), and surgery (OR 1.871 95 % CI 1.371-2.570, p < 0.001) were potential predictors for missed registrations in multivariate logistic regression analysis. CONCLUSION: Missed registrations occur frequently and the rate of missed registrations differs greatly between hospitals. Automated and missed registration datasets display differences related to patients requiring more intensive care, which held for the major trauma subset. Checking for missed registrations is time consuming, automated registration lists need a human touch for validation and to be complete.

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BACKGROUND: Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research. OBJECTIVE: This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods. METHODS: The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed. RESULTS: Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only "fair" or "minimal" agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group. CONCLUSIONS: The selection of a fraud detection method can affect the study's sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study's specific context and its participants may be warranted for future survey research.

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Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, ß- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (ß-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of ß-Ni(OH)2. The FESEM and TEM micrographs showed that the ß-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in ß-Ni(x)/ZNs samples. With the photodeposition of ß-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of ß-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that ß-Ni(0.1)/ZNs and ß-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R2, SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (ß-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

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ABSTRACT Cold sores require Healthcare professionals to employ specific approaches for prevention and management, with the need for effective therapeutic guidelines and ongoing improvement in patient care. Aim To evalúate the methodological quality of Clinical Guidelines (CG), clinical guides and manuals for care of the population affected by herpes labialis, to verify their compliance with evidence-based health standards. Materials and Method A search was conducted for CG on labial herpes in the MedicalLiteratureAnalysis andRetrieval System Online (Medline) database, Google Scholar, Brazilian Virtual Health Library (BVS), and sites of institutions/professional categories, using the descriptors "herpes labialis" or "oral herpes". Document quality was assessed using the Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II). The Kappa test was used to avoid randomness or poor agreement between results. Results Analysis of the 12 selected publications on the management of labial herpes revealed flaws in quality, as the publications did not follow a quality standard. The main quality flaws identified were in "rigor in development" and "applicability. " Conclusions Priorities need to be redefined in the development of CG for clinical practice related to fever blisters to reduce the variability of the quality standard, and generate reliable, applicable recommendations.

RESUMO A Herpes labial requer dos profissionais abordagens específicas para prevenido e manejo, com a ne-cessidade de diretrizes terapéuticas eficazes e continuo aprimoramento do cuidado ao paciente. Objetivo avaliar a qualidade metodológica de documentos que abordaram Diretrizes Clínicas (DC), guias clínicos e manuais para o cuidado da populando afetada pelo herpes labial, verificando sua conformidade com padroes de saúde baseados em evidencias. Materiais e Método As DC sobre herpes labial foram pesquisadas na base de dados Medical Literature Analysis and Retrieval System Online (Medline), Google Académico, Biblioteca Virtual em Saúde (BVS) e em sites de instituigoes/categorias profissionais, utilizando os descritores "herpes labial" ou "herpes oral". Utilizamos aferramenta The Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II) para a avaliagdo da qualidade. O teste Kappa também foi utilizado para evitar aleatoriedade ou baixa concordáncia entre os resultados. Resultados Na análise das 12 publicagoes selecionadas sobre o manejo do herpes labial, foram identificadas falhas na qualidade dos documentos, que ndo seguiram um padrdo de qualidade. As principais falhas de qualidade identificadas foram em "rigor no desenvolvimento" e "aplicabilida-de". Conclusao é necessário um reenfoque para definir prioridades no desenvolvimento de DC para a prática clínica do herpes labial, a fim de reduzir a variabilidade do padrdo de qualidade e gerar recomendagoes que possam ser confiáveis e aplicáveis.

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We investigated the validity of the 10th Revision Canadian modification of International Statistical Classification of Disease and Related Health Problems (ICD-10-CA) diagnostic codes for surgery for benign gynaecologic conditions in the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD), the main source of routinely collected data in Canada. Reabstracted data from patient charts was compared to ICD-10-CA codes and measures of validity were calculated with 95% confidence intervals. A total of 1068 procedures were identified. More objective, structural diagnoses (fibroids, prolapse) had higher sensitivity and near-perfect Kappa coefficients, while more subjective, symptomatic diagnoses (abnormal uterine bleeding, pelvic pain) had lower sensitivity and moderate-substantial Kappa coefficients. Specificity, positive predictive values, and negative predictive values were generally high for all diagnoses. These findings support the use of CIHI-DAD data for gynaecologic research.

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Data validation in cancer registration is a critical operation but is resource-intensive and has traditionally depended on proprietary software. Ontology-based AI is a novel approach utilising machine reasoning based on axioms formally described in description logic. This is a different approach from deep learning AI techniques but not exclusive of them. The advantage of the ontology approach lies in its ability to address a number of challenges concurrently. The disadvantages relate to computational costs, which increase with language expressivity and the size of data sets, and class containment restrictions imposed by description logics. Both these aspects would benefit from the availability of design patterns, which is the motivation behind this study. We modelled the European cancer registry data validation rules in description logic using a number of design patterns and showed the viability of the approach. Reasoning speeds are a limiting factor for large cancer registry data sets comprising many hundreds of thousands of records, but these can be offset to a certain extent by developing the ontology in a modular way. Data validation is also a highly parallelisable process. Important potential future work in this domain would be to identify and optimise reusable design patterns, paying particular attention to avoiding any unintended reasoning efficiency hotspots.

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Ambient PM2.5 (particles less than 2.5 µm in diameter) is monitored in many countries including Australia. Occasionally PM2.5 instruments may report negative measurements, although in realty the ambient air can never contain negative amounts of particles. Some negative readings are caused by instrument faults or procedural errors, thus can be simply invalidated from air quality reporting. There are occasions, however, when negative readings occur due to other factors including technological or procedural limitations. Treatment of such negative data requires consideration of factors such as measurement uncertainty, instrument noise and risk for significant bias in air quality reporting. There is very limited documentation on handling negative PM2.5 data in the literature. This paper demonstrates how a threshold is determined for controlling negative hourly PM2.5 readings in the New South Wales (NSW) air quality data system. The investigation involved a review of thresholds used in different data systems and an assessment of instrument measurement uncertainties, zero air test data and impacts on key reporting statistics when applying different thresholds to historical datasets. The results show that a threshold of -10.0 µg/m3 appears optimal for controlling negative PM2.5 data in public reporting. This choice is consistent with the measurement uncertainty estimates and the zero air test data statistics calculated for the NSW Air Quality Monitoring Network, and is expected not to have significant impacts on key compliance reporting statistics such as data availability and annual average pollution levels. The analysis can be useful for air quality monitoring in other Australian jurisdictions or wider context.

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Machine learning (ML) techniques promise to revolutionize environmental research and management, but collecting the necessary volumes of high-quality data remains challenging. Environmental sensors are often deployed under harsh conditions, requiring labor-intensive quality assurance and control (QAQC) processes. The need for manual QAQC is a major impediment to the scalability of these sensor networks. Existing techniques for automated QAQC make strong assumptions about noise profiles in the data they filter that do not necessarily hold for broadly deployed environmental sensors, however. Toward the goal of increasing the volume of high-quality environmental data, we introduce an ML-assisted QAQC methodology that is robust to low signal-to-noise ratio data. Our approach embeds sensor measurements into a dynamical feature space and trains a binary classification algorithm (Support Vector Machine) to detect deviation from expected process dynamics, indicating whether a sensor has become compromised and requires maintenance. This strategy enables the automated detection of a wide variety of nonphysical signals. We apply the methodology to three novel data sets produced by 136 low-cost environmental sensors (stream level, drinking water pH, and drinking water electroconductivity), deployed by our group across 250,000 km2 in Michigan, USA. The proposed methodology achieved accuracy scores of up to 0.97 and consistently outperformed state-of-the-art anomaly detection techniques.

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Ontologies can provide a valuable role in the work of cancer registration, particularly as a tool for managing and navigating the various classification systems and coding rules. Further advantages accrue from the ability to formalise the coding rule base using description logics and thereby benefit from the associated automatic reasoning functionality. Drawing from earlier work that showed the viability of applying ontologies in the data validation tasks of cancer registries, an ontology was created using a modular approach to handle the specific checks for childhood cancers. The ontology was able to handle successfully the various inter-variable checks using the axiomatic constructs of the web ontology language. Application of an ontological approach for data validation can greatly simplify the maintenance of the coding rules and facilitate the federation of any centralised validation process to the local level. It also provides an improved means of visualising the rule interdependencies from different perspectives. Performance of the automatic reasoning process can be a limiting issue for very large datasets and will be a focus for future work. Results are provided showing how the ontology is able to validate cancer case records typical for childhood tumours.

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Recently, with the increasing application of the Internet of Things (IoT), various IoT environments such as smart factories, smart homes, and smart grids are being generated. In the IoT environment, a lot of data are generated in real time, and the generated IoT data can be used as source data for various services such as artificial intelligence, remote medical care, and finance, and can also be used for purposes such as electricity bill generation. Therefore, data access control is required to grant access rights to various data users in the IoT environment who need such IoT data. In addition, IoT data contain sensitive information such as personal information, so privacy protection is also essential. Ciphertext-policy attribute-based encryption (CP-ABE) technology has been utilized to address these requirements. Furthermore, system structures applying blockchains with CP-ABE are being studied to prevent bottlenecks and single failures of cloud servers, as well as to support data auditing. However, these systems do not stipulate authentication and key agreement to ensure the security of the data transmission process and data outsourcing. Accordingly, we propose a data access control and key agreement scheme using CP-ABE to ensure data security in a blockchain-based system. In addition, we propose a system that can provide data nonrepudiation, data accountability, and data verification functions by utilizing blockchains. Both formal and informal security verifications are performed to demonstrate the security of the proposed system. We also compare the security, functional aspects, and computational and communication costs of previous systems. Furthermore, we perform cryptographic calculations to analyze the system in practical terms. As a result, our proposed protocol is safer against attacks such as guessing attacks and tracing attacks than other protocols, and can provide mutual authentication and key agreement functions. In addition, the proposed protocol is more efficient than other protocols, so it can be applied to practical IoT environments.

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Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

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PURPOSE: The Spanish Incisional Hernia Surgery Registry (EVEREG) was promoted by the Abdominal Wall Section of the Spanish Association of Surgeons, starting data collection in July 2012 and currently has more than 14,000 cases. The objective of this study was to validate the data collected through a pilot audit process. METHODS: A sample of hospitals participating in the EVEREG registry since the beginning was selected. Patients registered in these centers in the 2012-2020 period were included. A stratified random sampling was carried out, with the inclusion of 10% of registered cases per center with a minimum of 20 cases per center. At each participating center, two researchers not belonging to the center undergoing the audit checked (on site or telematically) the concordance between the data in the registry and the data contained in the case history of each patient. RESULTS: 330 patients have been analyzed, out of a total of 2673 registered, in 9 participating centers. The average accuracy has been 95.7%. Incorrect data 1.5% and missing data 2.3% CONCLUSION: The group of pilot hospitals from this EVERG incisional hernia surgery registry shows a very high precision of 95.7%. The confirmation of these findings in all the centers participating in the registry will make it possible to guarantee the quality of the studies made and their comparability with other similar national registries. TRIAL REGISTRATION: nnTrial registration number: ClinicalTrials.gov ID:NCT03899012.

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Background: Pertussis is a reportable disease in many countries, but ascertainment bias has limited data accuracy. This study aims to validate pertussis data measures using a reference standard that incorporates different suspected case severities, allowing for the impact of case severity on accuracy and detection to be explored. Methods: We evaluated 25 pertussis detection algorithms in a primary care electronic medical record database between January 1, 1986 and December 30, 2016. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used sensitivity analyses to explore areas of uncertainty and evaluated reasons for lack of detection. Results: The algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization. Conclusions: Sensitivity improved by reclassifying symptom-only cases but remained low unless multiple data sources were used. Results demonstrate a trade-off between PPV and sensitivity. EMRs can enhance detection through patient history and clinical note data. It is essential to improve case identification of older individuals with vaccination history to reduce ascertainment bias.

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Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III). This tutorial explored methods such as K-fold cross-validation and nested cross-validation, highlighting their advantages and disadvantages across 2 common predictive modeling use cases: classification (mortality) and regression (length of stay). We aimed to provide readers with reproducible notebooks and best practices for modeling with electronic health care data. We also described sets of useful recommendations as we demonstrated that nested cross-validation reduces optimistic bias but comes with additional computational challenges. This tutorial might improve the community's understanding of these important methods while catalyzing the modeling community to apply these guides directly in their work using the published code.

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Recent trends in vaccine hesitancy have brought to light the importance of using accurate school vaccination data. This study evaluated the accuracy of a pilot statewide kindergarten vaccination survey in Oklahoma. School vaccination and exemption data were collected from November 2017 to April 2018 via the Research Electronic Data Capture system. A multivariable linear regression model was used to evaluate the relationship between students who are up to date for all vaccines comparing school reported and Oklahoma State Department of Health-validated data. Adjusted vaccination data were overestimated by 1.0% among public schools and 3.3% among private schools. These results were validated by a random audit of participating schools finding the school-reported vaccination data to be overestimated by 0.6% compared to true student immunization records on file. Our analysis indicates that school-reported vaccination data are sufficiently valid. Immunization record audits provide confidence in available data, which drives evidence-based decision-making.

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Objectives: The objective of this derivation and validation study was to develop and validate a search strategy algorithm to detect patients who used professional interpreter services. Methods: We identified all adults who had at least one intensive care unit admission during their hospital stay across the Mayo Clinic Enterprise between 1 January 2015 and 30 June 2020. Three random subsets of 100 patients were extracted from 60,268 patients to develop the search strategy algorithm. Two physician reviewers conducted gold standard manual chart review and any discrepancies were resolved by a third reviewer. These results were compared with the search strategy algorithm each time it was refined. Sensitivity and specificity were calculated during each phase by comparing the search strategy results to the reference gold standard for both derivation cohorts and the final validation cohort. Results: The first search strategy resulted in a sensitivity of 100% and a specificity of 89%. The second revised search strategy achieved a sensitivity of 100% and a specificity of 87%. The final version of the search strategy was applied to the validation subset and sensitivity and specificity were 100% and 89%, respectively. Conclusion: We derived and validated a search strategy algorithm to assess interpreter use among hospitalized patients. Using a search strategy algorithm with high sensitivity and specificity can reduce the time required to abstract data from the electronic medical records compared with manual data abstraction.