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Background: Surveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021. Methods: Data Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated. Results: Throughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs. Conclusions: While adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies.

Background and Objective: One of the primary challenges faced by telehealth systems is the accurate transmission of patient information to remote doctors. In this context, portable medical sensors deployed at the remote patients' end play a crucial role in measuring vital information. There are many sensors available in the market. However, the accuracy of the sensors has been always a concern. The objective of this study is to verify different sensors and create awareness for using accurate sensors to avoid misdiagnosis for the patients’ safety. Methods: This study considered the test result of a Japanese clinical pathology laboratory as the reference gold standard. The clinical pathology laboratory uses 1) Hexokinase UV method for blood glucose, 2) Enzymatic Determination method for cholesterol, 3) Automatic Analyzer (EDTA-2 K) of Hemoglobin, and 4) Uricase POD method for uric acid. To assess the performance of a medical sensor, its test results were compared to the gold standard test results obtained from the laboratory using the same sample. A Normalized Root Mean Square Error (NRMSE) threshold of less than 0.2 was established as the criterion for determining whether the medical sensor's performance fell within an acceptable range. Results: Among the eight most commonly used blood glucose devices in the Asian market, only one device was deemed acceptable with NRMSE less than 0.2. However, all four devices found in the Japanese market showed their acceptability. In the case of cholesterol, hemoglobin, and uric acid devices, only a limited number of items were available in Asian markets. Some of the hemoglobin and uric acid devices were found to be somewhat acceptable, while all the cholesterol sensors were found erroneous. Conclusions: This study has clearly shown the issues with the portable medical sensors and recommends the device approval authority of each country to approve sales of the quality sensors only for patients’ safety.

Resumen: Objetivo: Evaluar el sistema de información en salud (SIS) de México según la información reportada a la Organización para la Cooperación y el Desarrollo Económicos (OCDE). El fin último es evidenciar las mejoras que se deberían considerar. Método: Se analizan indicadores sobre salud publicados por la OCDE (2017 a 2021) según 11 grupos temáticos. Se valoraron cobertura (cantidad y tipo de indicadores reportados por grupo temático) y calidad de la información, según lineamientos de la OCDE. Resultados: México reportó anualmente 14 de 378 indicadores (3,7%) y de forma discontinua 204. En ningún grupo se reportaron anualmente todos los indicadores, excepto los dos sobre COVID-19. Se reportan anualmente tres de 88 sobre utilización de servicios y ninguno sobre estado de salud, calidad de la atención y mercado farmacéutico. Con calidad óptima y reporte anual fueron 12 indicadores (5,5% de los reportados por México, 3,2% del set completo OCDE). El 57,7% de los indicadores reportados tuvieron al menos un defecto de calidad. Conclusiones: En el marco de los estándares marcados por la OCDE, de la cual México es miembro, el SIS mexicano presenta déficits importantes de cobertura y de calidad de la información. Estos resultados deberían considerarse para implementar iniciativas de mejora. Abstract: Objective: To evaluate the health information system (HIS) of Mexico according to the information reported to the Organization for Economic Co-operation and Development (OECD). The ultimate goal is to identify the improvements that should be considered. Method: Health indicators published by the OECD (2017 to 2021) are analyzed according to 11 thematic groups. Coverage (quantity and type of indicators reported by thematic group) and quality of information were assessed, according to OECD guidelines. Results: Mexico reported annually 14 of 378 indicators (3.7%), and discontinuously 204. In no group were all indicators reported annually, except for the two on COVID-19. Three out of 88 were reported annually on use of services; and none on health status, quality of care and pharmaceutical market. Twelve indicators (5.5% of those reported by Mexico, 3.2% of the full OECD set) had optimal quality and annual reporting. 57.7% of the reported indicators had at least one quality defect. Conclusions: Within the framework of the standards set by the OECD, of which Mexico is a member, the Mexican HIS presents significant deficits in coverage and quality of information. These results should be considered to implement improvement initiatives.

Background/purpose: The scanning accuracy of intraoral scanners’ data collection plays a key role in the success of the final treatment. However, few studies start from scanning technology itself to directly evaluate it. The aim of this study was to evaluate the scanning accuracy of three intraoral scanners, to provide a reference for relevant research and clinical applications. Materials and methods: Six types of resin models containing different numbers of crown-prepared abutments were three-dimensionally printed, and a model scanner, as well as three intraoral scanners, were used to digitally scan the six models. The obtained data were uploaded to three-dimensional reverse software for registration and comparison, and the accuracy of the models were analyzed. Results: When scanning the six groups of models, the Omnicam outperformed both the TRIOS and iTero in terms of accuracy in all groups except the second molar group. The TRIOS and iTero scanners also exhibited decreased degrees of accuracy when scanning the long dental arch. The accuracy decreased as the scanning scope increased; however, the Omnicam scanner exhibited a relatively high degree of accuracy when scanning the three-unit fixed bridge and anterior areas. All scanners exhibited the lowest degree of accuracy when scanning the full-arch model. Certain deviations were observed, and the scanning areas at the incisal edges of the anterior teeth and end of the dental arch exhibited relatively large deviations. Conclusion: With the model scanner data as reference, the scanning accuracy of the three scanners exhibited differences and certain deviations, which were within clinical tolerance.

Background: As artificial intelligence within digital processes continues to advance and replace conventional manual workflows, it is crucial that digital data are consistent with analog data. The aim was to evaluate the validity and time efficiency of digital cast analysis on digital models in comparison with the manual, gold standard, cast analysis on plaster models., Methods: Cast analysis was performed on 30 patients in three various methods: manually measured variables on plaster models (MP), manually measured variables on digital three-dimensional models (MD), and automatically measured variables on digital three-dimensional models (AD) on digital models. Digital cast analysis was performed in CS Model+. Analyses included metrical and categorical variables and the required work time. Measurements in MD and AD were validated to MP. Validity of the metrical variables was analyzed with Bland-Altman, Dahlberg's formula, and paired sample t test. Categorical variables were validated by Cohen's Kappa. Work time was analyzed with Wilcoxon signed-rank test., Results: Metrical variables had measurement errors ranging 0.4 to 1.4 mm between MP-MD, and 0.6 to 3.2 mm between MP-AD. Observations of categorical variables had a moderate to strong (0.65 to 0.9) level of agreement between MP-MD, and a weak to moderate (0.4 to 0.68) level of agreement between MP-AD. Data for dental stage, vertical, and transversal relation was not provided in AD. Cast analysis was performed quicker digitally, P ≤ 0.05., Conclusions: Digital cast analysis is consistent with manual cast analysis for metrical variables. Analyses of categorical variables show a weak level of agreement with automatic digital analysis, such as space conditions and midline assessments. Digital cast analysis optimizes time compared with manual cast analysis, with automatic analysis being the fastest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Enhancing data sharing, quality, and use across siloed HIV and STI programs is critical for national and global initiatives to reduce new HIV infections and improve the health of people with HIV. As part of the Enhancing Linkage of STI and HIV Surveillance Data in the Ryan White HIV/AIDS Program initiative, four health departments (HDs) in the U.S. received technical assistance to better share and link their HIV and STI surveillance data. The process used to develop evaluation measures assessing implementation and outcomes of linking HIV and STI data systems involved six steps: 1) measure selection and development, 2) review and refinement, 3) testing, 4) implementation and data collection, 5) data quality review and feedback, and 6) dissemination. Findings from pilot testing warranted slight adaptations, including starting with a core set of measures and progressively scaling up. Early findings showed improvements in data quality over time. Lessons learned included identifying and engaging key stakeholders early; developing resources to assist HDs; and considering measure development as iterative processes requiring periodic review and reassessment to ensure continued utility. These findings can guide programs and evaluations, especially those linking data across multiple systems, in developing measures to track implementation and outcomes over time., Competing Interests: Declaration of Competing Interest The views expressed in this publication are solely the opinions of the authors and do not necessarily reflect the official policies of HHS or HRSA, nor does mention of the department or agency names imply endorsement by the U.S. Government. The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach., Methods: We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites)., Results: The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site., Conclusions: We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials., Original Trial Registration Number: NCT02475850., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Good data quality is essential when rare disease registries are used as a data source for pharmacovigilance studies. This study investigated data quality of the Swiss cystic fibrosis (CF) registry in the frame of a European Cystic Fibrosis Society Patient Registry (ECFSPR) project aiming to implement measures to increase data reliability for registry-based research., Methods: All 20 pediatric and adult Swiss CF centers participated in a data quality audit between 2018 and 2020, and in a re-audit in 2022. Accuracy, consistency and completeness of variables and definitions were evaluated, and missing source data and informed consents (ICs) were assessed., Results: The first audit included 601 out of 997 Swiss people with CF (60.3 %). Data quality, as defined by data correctness ≥95 %, was high for most of the variables. Inconsistencies of specific variables were observed because of an incorrect application of the variable definition. The proportion of missing data was low with <5 % for almost all variables. A considerable number of missing source data occurred for CFTR variants. Availability of ICs varied largely between centers (10 centers had >5 % of missing documents). After providing feedback to the centers, availability of genetic source data and ICs improved., Conclusions: Data audits demonstrated an overall good data quality in the Swiss CF registry. Specific measures such as support of the participating sites, training of data managers and centralized data collection should be implemented in rare disease registries to optimize data quality and provide robust data for registry-based scientific research., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kathleen Jahn, Andreas Jung, Philipp Latzin, Alexander Moeller, Christian Murer, Lutz Naehrlich, Alain Sauty and Jakob Usemann have received honoraria from Vertex Pharmaceuticals. Andreas Jung has received consulting fees and honoraria from EffRx Pharmaceuticals and Viatris. Philipp Latzin has received honoraria from CSL Vifor., (Copyright © 2023. Published by Elsevier B.V.)

Background: The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single-lead electrocardiographic (ECG) recordings. The accuracy of AW-HR and diagnostic performance of AW-ECGs among children during both sinus rhythm and arrhythmias have not been explored., Objective: The purposes of this study were to assess the accuracy of AW-HR measurements compared to gold standard modalities in children during sinus rhythm and arrhythmias and to identify non-sinus rhythms using AW-ECGs., Methods: Subjects ≤18 years wore an AW during (1) telemetry admission, (2) electrophysiological study (EPS), or (3) exercise stress test (EST). AW-HRs were compared to gold standard modality values. Recorded AW-ECGs were reviewed by 3 blinded pediatric electrophysiologists., Results: Eighty subjects (median age 13 years; interquartile range 1.0-16.0 years; 50% female) wore AW (telemetry 41% [n = 33]; EPS 34% [n = 27]; EST 25% [n = 20]). A total of 1090 AW-HR measurements were compared to time-synchronized gold standard modality HR values. Intraclass correlation coefficient (ICC) was high 0.99 (0.98-0.99) for AW-HR during sinus rhythm compared to gold standard modalities. ICC was poor comparing AW-HR to gold standard modality HR in tachyarrhythmias (ICC 0.24-0.27) due to systematic undercounting of AW-HR values. A total of 126 AW-ECGs were reviewed. Identification of non-sinus rhythm by AW-ECG showed sensitivity of 89%-96% and specificity of 78%-87%., Conclusions: We found high levels of agreement for AW-HR values with gold standard modalities during sinus rhythm and poor agreement during tachyarrhythmias, likely due to hemodynamic effects of tachyarrhythmias on PPG-based measurements. AW-ECGs had good sensitivity and moderate specificity in identification of non-sinus rhythm in children., Competing Interests: Disclosures Dr Shah serves as a consultant for Medtronic. All other authors have no conflicts of interest to disclose., (Copyright © 2024 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

DLCA has been applied to several food waste streams, however, to date no critical assessment of its strengths, weaknesses, opportunities, and threats (SWOT) is available in the scientific literature. Accordingly, the present review aims to provide a comprehensive overview of the available literature on DLCA and its application to Household and Commercial Food Waste (HCFW) by providing critical assessment and perspectives for future research. The Population, Intervention, Comparison, and Outcome (PICO) framework for literature review was employed, with just 12 relevant studies identified between 1999 and 2022, highlighting a dearth of research on DLCA of food waste and the need for further research. Identified studies exhibit significant variations with respect to DLCA methodology, boundary settings, and data quality and reporting, with more attention typically given to combining conventional LCA with dynamic characterization models, thus making it difficult to draw conclusive findings or identify consistent trends. Additionally, most identified studies employed DLCA for a specific case study and comparison with traditional LCA outcomes was typically ignored; just one study presented the projected impact from both LCA and DLCA for the entire life cycle of a product. Employed functional/reference units ranged from specific quantities such as 1 kg of refined crystals or syrup, 1 g L -1 Sophorolipid solution, and 1 kg of dry food with packaging material, to broader indicators like 1 kg of biofuel or 1 MJ of primary energy. Monte Carlo simulation was the most frequently employed method for uncertainty analyses within identified studies. Sensitivity analyses were conducted in just 4 studies, but it was not always clearly reported. While DLCA is undoubtedly a more realistic approach to impact assessment, and thus likely more accurate, a need exists for increasingly standardized and regulated versions of DLCA for global and multi-criteria practices., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Majid Bahramian reports financial support was provided by Technological University Dublin., (Copyright © 2024. Published by Elsevier B.V.)

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Surveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021., Methods: Data Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated., Results: Throughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs., Conclusions: While adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies., Competing Interests: Declaration of Competing Interest We have no conflict of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In the United States, the governance of unnatural death certification varies greatly by state. Although cross-sectional research has linked mortality data quality with variation in medicolegal death investigation systems across states-especially with regards to drug-related deaths-this relationship has not be sufficiently tested using longitudinal data. This research assesses the impact of system governance reform on the quality of drug mortality data by assessing the impact of transitioning from a coroner system to a medical examiner system on data quality. The research finds no evidence that system-level reform is associated with improved drug-related mortality data quality. These findings suggest that alternative methods should be examined for improving public health data concerning drug-related mortality. These likely include focusing on individual-level characteristics and practices of officials and offices, rather than system-level variables., Competing Interests: Declaration of competing interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The author reports there are no competing interests to declare., (Copyright © 2024 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.)

Information technology applications for patient-collection of family health history (FHH) increase identification of elevated-risk individuals compared to usual care. It is unknown if the method of collection impacts data collected or if simply going directly to the patient is what makes the difference. The objective of this study was to examine differences in data detail and risk identification rates between FHH collection directly from individuals using paper-based forms and an interactive web-based platform. This is a non-randomized epidemiologic study in Singaporean population from 2016 to 2018. Intervention was paper-based versus web-based interactive platform for FHH collection. Participant demographics, FHH detail, and risk assessment results were analyzed. 882 participants enrolled in the study, 481 in the paper-based group and 401 in the web-based group with mean (SD) age of 45.4 (12.98) years and 47.5% male. Web-based FHH collection participants had an increased number of conditions per relative (p-value

Objective: The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within an existing dataset of complex medication data to improve machine learning model prediction of fluid overload., Materials and Methods: This retrospective cohort study evaluated patients admitted to an ICU ≥ 72 h. Four machine learning algorithms to predict fluid overload after 48-72 h of ICU admission were developed using the original dataset. Then, two distinct synthetic data generation methodologies (synthetic minority over-sampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN)) were used to create synthetic data. Finally, a stacking ensemble technique designed to train a meta-learner was established. Models underwent training in three scenarios of varying qualities and quantities of datasets., Results: Training machine learning algorithms on the combined synthetic and original dataset overall increased the performance of the predictive models compared to training on the original dataset. The highest performing model was the meta-model trained on the combined dataset with 0.83 AUROC while it managed to significantly enhance the sensitivity across different training scenarios., Discussion: The integration of synthetically generated data is the first time such methods have been applied to ICU medication data and offers a promising solution to enhance the performance of machine learning models for fluid overload, which may be translated to other ICU outcomes. A meta-learner was able to make a trade-off between different performance metrics and improve the ability to identify the minority class., Competing Interests: Declaration of competing interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Flexible wearable devices are garnering significant interest, with conductive hydrogels emerging as a particularly notable category. While many of these hydrogels offer impressive conductivity, they often lack the innate ability to adhere autonomously to human skin. The ideal hydrogel should possess both superior adhesion properties and a wide responsive range. This study introduces a novel double-network conductive hydrogel, synthesized from lignosulfonate sodium and ionic liquid using a one-pot method. The gel's mechanical robustness (fracture elongation of ∼3500 % and tensile strength of ∼130 kPa) and exceptional conductivity sensing performance arise from the synergistic effects of electrostatic interactions, dynamic hydrogen bonding, and a three-dimensional network structure. Additionally, the phenolic hydroxyl and sulfonic groups from lignosulfonate sodium imbue the hydrogel with adhesive qualities, allowing it to easily bond with varied material surfaces. This hydrogel excels in human physiological signal detection and wireless monitoring, demonstrating a rapid response time (149 ms) and high sensitivity (a maximum gauge factor of 10.9 for strains between 400 and 600 %). Given these properties, the flexible, self-adhesive, and conductive hydrogel showcases immense promise for future applications in wearable devices and wireless transmission sensing., Competing Interests: Declaration of competing interest There are no conflicts of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Federated Learning (FL) is an emerging distributed learning paradigm which offers data privacy to contributing nodes in the collaborating environment. By exploiting the Individual datasets of different hospitals in FL setting could be used to develop reliable screening, diagnosis, and treatment predictive models to tackle major challenges such as pandemics. FL can enable the development of very diverse medical imaging datasets and thus provide more reliable models for all participating nodes, including those with low quality data. However, the issue with the traditional Federated Learning paradigm is the degradation of generalization power due to poorly trained local models at the client nodes. The generalization power of the FL paradigm can be improved by considering the relative learning contribution of client nodes. Simple aggregation of learning parameters in the standard FL model faces a diversity issue and results in more validation loss during the learning process. This issue can be resolved by considering the relative contribution of each client node participating in the learning process. The class imbalance at each site is another significant challenge that greatly impacts the performance of the aggregated learning model. This work considers Context Aggregator FL based on the context of loss-factor and class-imbalance issues by incorporating the relative contribution of the collaborating nodes in FL by proposing Validation-Loss based Context Aggregator (CAVL) and Class Imbalance based Context Aggregator (CACI). The proposed Context Aggregator is evaluated on several different Covid-19 imaging classification datasets present on participating nodes. The evaluation results show that Context Aggregator performs better than standard Federating average Learning algorithms and FedProx Algorithm for Covid-19 image classification problems., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Research examining the extent that people's attitudes toward abortion vary across the life course is mixed. Some studies do not show a strong relationship between abortion attitudes and life stage, while others do find strong associations in both directions-older age associated with both more and less favorable attitudes toward legal abortion. Taken together, these findings suggest that individual attitudes toward abortion are static for some but malleable for others. Little is known about the prevalence, reasons, and directionality of attitude changes. This explanatory sequential mixed methods study investigates people's perceptions of whether, how, why, and for whom their abortion attitudes may have changed over their life course. We qualitatively investigated the reasons (e.g., experiences, life events) that triggered changes in respondents' abortion attitudes and quantitatively explored the sociodemographic factors associated with the perceived direction of those changes. The quantitative data come from a 2020 online survey completed by 1501 English and Spanish-speaking adults in the US. Qualitative data were collected from a subsample (n = 24) of the survey respondents who indicated interest in a follow-up in-depth interview. Our findings indicate that access to information and knowledge played an important role in changing abortion attitudes across a spectrum of support or opposition. For those who indicated becoming more opposed to abortion over time, experiencing parenthood was an important trigger for change and family/religious upbringing were key to shaping attitudes. For those who became more supportive of abortion over time, empathy for women was an important trigger for change and disagreeing with or distancing oneself from family/religious upbringing were key to shaping their attitudes. If attitudinal change occurs, becoming more supportive of abortion over the life-course is more common than becoming more opposed, however there are some nuances across age and gender. Understanding the different factors that influence attitudinal change regarding abortion has important implications for public opinion research and possible ramifications for abortion legality., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objectives: To assess the prevalence of overly positive interpretation, also called 'spin',-of results in diagnostic accuracy studies of infectious diseases and to identify suggestions for improvement., Methods: A PubMed search was performed to identify diagnostic accuracy studies of infectious diseases published between January and March 2019. Each article was assessed by two authors independently to identify study characteristics and forms of actual and potential over-interpretation. 'Actual over-interpretation' was defined as conclusions that were not on the basis of study aims or conclusions that were more favourable than justified by the study findings. There are other practices that may result in the over-interpretation of study findings and these have been described as 'potential over-interpretation'., Results: The final analysis included 120 studies. Favourable or promising recommendations were made in the main text of 101 (84%) of the included studies. Evidence of actual over-interpretation (spin) was found in 30 (25%) articles, with 22 (18%) studies reporting a conclusion that did not match the study aims and 56 (47%) studies with a more positive conclusion in the abstract than the main text. All analysed studies exhibited at least one form of potential over-interpretation, with was most commonly a lack of sample size calculation (n = 109, 91%) and not reporting a null hypothesis (n = 115, 96%)., Discussion: Evidence of over-interpretation of results was found in one-third of the included studies. We have proposed possible interventions to prevent overly positive interpretations of results in diagnostic accuracy studies., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: In recent years, we have increasingly observed issues concerning quality of online information due to misinformation and disinformation. Aside from social media, there is growing awareness that questionnaire data collected using online recruitment methods may include suspect data provided by bots. Issues with data quality can be particularly problematic in health and/or biomedical contexts; thus, developing robust methods for suspect data identification and removal is of paramount importance in informatics. In this study, we describe an interactive visual analytics approach to suspect data identification and removal and demonstrate the application of this approach on questionnaire data pertaining to COVID-19 derived from different recruitment venues, including listservs and social media., Methods: We developed a pipeline for data cleaning, pre-processing, analysis, and automated ranking of data to address data quality issues. We then employed the ranking in conjunction with manual review to identify suspect data and remove them from subsequent analyses. Last, we compared differences in the data before and after removal., Results: We performed data cleaning, pre-processing, and exploratory analysis on a survey dataset (N = 4,163) collected using multiple recruitment mechanins using the Qualtrics survey platform. Based on these results, we identified suspect features and used these to generate a suspect feature indicator for each survey response. We excluded survey responses that did not fit the inclusion criteria for the study (n = 29) and then performed manual review of the remaining responses, triangulating with the suspect feature indicator. Based on this review, we excluded 2,921 responses. Additional responses were excluded based on a spam classification by Qualtrics (n=13), and the percentage of survey completion (n=328), resulting in a final sample size of 872. We performed additional analyses to demonstrate the extent to which the suspect feature indicator was congruent with eventual inclusion, as well as compared the characteristics of the included and excluded data., Conclusion: Our main contributions are: 1) a proposed framework for data quality assessment, including suspect data identification and removal; 2) the analysis of potential consequences in terms of representation bias in the dataset; and 3) recommendations for implementation of this approach in practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology., Methods: In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures)., Results: Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients., Significance: Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology., Competing Interests: Declaration of Competing Interest CM is part of pending and approved patents related to seizure detection, seizure prediction, and epilepsy diagnosis. TL has received funding from Empatica in the past, has received device donations from Empatica in the past, and is part of pending and approved patents related to seizure detection, seizure prediction, and epilepsy diagnosis. SV is part of a patent covering technology for seizure forecasting. The rest of the authors declare no conflicting interests., (Copyright © 2023 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Abstract Objectives: This study examines the accuracy of Gestational Diabetes Mellitus (GDM) case‐ascertainment in routinely collected data. Methods: Retrospective cohort study analysed routinely collected data from all births at Cairns Base Hospital, Australia, from 1 January 2004 to 31 December 2010 in the Cairns Base Hospital Clinical Coding system (CBHCC) and the Queensland Perinatal Data Collection (QPDC). GDM case ascertainment in the National Diabetes Services Scheme (NDSS) and Cairns Diabetes Centre (CDC) data were compared. Results: From 2004 to 2010, the specificity of GDM case‐ascertainment in the QPDC was 99%. In 2010, only 2 of 225 additional cases were identified from the CDC and CBHCC, suggesting QPDC sensitivity is also over 99%. In comparison, the sensitivity of the CBHCC data was 80% during 2004–2010. The sensitivity of CDC data was 74% in 2010. During 2010, 223 births were coded as GDM in the QPDC, and the NDSS registered 247 women with GDM from the same postcodes, suggesting reasonable uptake on the NDSS register. However, the proportion of Aboriginal and Torres Strait Islander women was lower than expected. Conclusion: The accuracy of GDM case‐ascertainment in the QPDC appears high, with lower accuracy in routinely collected hospital and local health service data. This limits capacity of local data for planning and evaluation, and developing structured systems to improve post‐pregnancy care, and may underestimate resources required. Implications: Data linkage should be considered to improve accuracy of routinely collected local health service data. The accuracy of the NDSS for Aboriginal and Torres Strait Islander women requires further evaluation.

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity., Competing Interests: Declaration of Competing Interest ECO Reports grants to her institution from Pfizer, BMS, and Novartis. KM reports grants and contracts to his institution from Novartis, Amgen, Seqirus, Genentech, BMS, and Boehringer Ingelheim. ADB reports grants from Alike Health, travel from Microsoft. All other authors have nothing to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: We evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset., Study Design and Setting: We simulated height and weight data for hypothetical individuals aged 2-20 years. We changed a proportion of height values according to predetermined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic, and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values, and receiver operating characteristic (ROC) curves., Results: At our chosen thresholds error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g., wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g., transcription errors that are still plausible, although extreme)., Conclusions: The DDC algorithm has the potential to improve error detection processes for observational data., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Nutrient databases are a critical component of nutrition science and the basis of exciting new research in precision nutrition (PN). To identify the most critical components needed for improvement of nutrient databases, food composition data were analyzed for quality, with completeness being the most important measure, and for FAIRness, how well the data conformed with the data science criteria of findable, accessible, interoperable, and reusable (FAIR). Databases were judged complete if they provided data for all 15 nutrition fact panel (NFP) nutrient measures and all 40 National Academies of Sciences, Engineering, and Medicine (NASEM) essential nutrient measures for each food listed. Using the gold standard the USDA standard reference (SR) Legacy database as surrogate, it was found that SR Legacy data were not complete for either NFP or NASEM nutrient measures. In addition, phytonutrient measures in the 4 USDA Special Interest Databases were incomplete. To evaluate data FAIRness, a set of 175 food and nutrient data sources were collected from worldwide. Many opportunities were identified for improving data FAIRness, including creating persistent URLs, prioritizing usable data storage formats, providing Globally Unique Identifiers for all foods and nutrients, and implementing citation standards. This review demonstrates that despite important contributions from the USDA and others, food and nutrient databases in their current forms do not yet provide truly comprehensive food composition data. We propose that to enhance the quality and usage of food and nutrient composition data for research scientists and those fashioning various PN tools, the field of nutrition science must step out of its historical comfort zone and improve the foundational nutrient databases used in research by incorporating data science principles, the most central being data quality and data FAIRness., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Mass photometry (MP) is a label-free, single-molecule technique that can determine molecular mass distribution with very low sample consumption in a short time. Because of the established experimental instrument and analytical software, MP measurements may be readily obtained; thus, the application of MP is expanding, especially in the fields of bioscience and biotechnology. However, because the MP data quality is strongly focus-dependent, optical settings must be intrinsically strict. In this study, we report the importance of the critical calibration of the mass photometer, which is required for the accurate estimation of high-molecular mass samples, such as adeno-associated virus vectors. Additionally, a method for optimizing the instrument settings, including the calibration of the stage, is presented., (Copyright © 2022 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Objective: The Society for Vascular Surgery published abdominal aortic aneurysm (AAA) practice guidelines in 2003, 2009, and 2018 to improve the management and treatment of AAAs. In 2014, our vascular surgery department implemented a quarterly AAA dashboard (AAAdb) to record the perioperative outcomes and guideline compliance with a focus on intervention appropriateness and procedural follow-up, which supplemented our Vascular Quality Initiative data. From the available reported evidence and expert consensus opinions, nine additional criteria for the appropriate treatment of AAAs <5 cm in women and <5.5 cm in men were noted, when applicable. The purpose of our study was to assess the effects of AAAdb implementation on adherence to society and institutional guidelines, documentation of treatment rationale, and the quality of follow-up., Methods: We performed a retrospective review of elective open and endovascular AAA repair at a single institution from 2010 to 2018. The AAAdb was implemented in the middle of this period in 2014. The patient demographics, aortic size, repair indication, repair type, 30-day mortality, and postoperative and 1-year follow-up imaging findings were analyzed. The primary outcome was adherence to intervention appropriateness and the follow-up guidelines. The categorical factors were summarized using frequencies and percentages and compared using the Pearson χ 2 test or Fisher exact test. Continuous measures were summarized using the mean ± standard deviation and compared between study periods using two-sample t tests., Results: From 2010 to 2018, 1549 patients had undergone elective AAA repair: 657 before and 892 after AAAdb implementation. No differences were found in AAA size after AAAdb (5.6 ± 1.2 cm vs 5.6 ± 1.1 cm; P = .88). However, the proportion of size-appropriate repairs increased (64.1% vs 71.3%; P = .003). The proportion of small AAA repairs with a documented rationale had increased (64.4% vs 80.5%; P < .001), with rapid disease progression cited most often. No difference was found in 30-day mortality (1.2% vs 1.5%; P = .69). Follow-up imaging after endovascular abdominal aortic aneurysm repair increased at <60 days postoperatively (76% vs 84%; P = .004) and at 1 year of follow-up (78% vs 86%; P = .0005). The proportion of patients with endoleak at <60 days postoperatively had increased in the post-AAAdb cohort (21% vs 29%; P = .012)., Conclusions: The AAAdb served as a centerpiece for improving the appropriateness of care and compliance with national and institutional guidelines, including treatment of small AAAs in special circumstances. Its implementation was associated with higher quality follow-up and surveillance in a high-volume, regional aortic center. Consideration should be given to adding additional criteria to the Society for Vascular Surgery guidelines and Vascular Quality Initiative reporting., (Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, direct-to-patient, self-applied ECG patch use has substantially increased. There are limited data comparing clinic with self-applied electrocardiogram (ECG) patches., Objective: The purpose of this study was to compare rates of ECG patch return, percentages of time patches yielded analyzable data (analyzable time), and percentages of prescribed time ECG patches were worn between clinic and self-applied ECG patches before and during COVID-19., Methods: A retrospective analysis of patients prescribed an ECG patch during "pre-COVID" (March 1, 2019, through March 1, 2020) and "COVID" (April 4, 2020, through April 1, 2021) years was performed. ECG patch return rates, mean percentages of analyzable time, and mean percentages of prescribed wear time were compared between clinic and self-applied groups., Results: Among the 29,093 ECG patch prescriptions (19% COVID self-applied), the COVID self-applied group had a lower return rate (90.8%) than did both clinic-applied groups (COVID: 97.1%; pre-COVID: 98.1%; P < .001). Among the 28,048 ECG patches (17.5% self-applied) returned for analysis, the COVID self-applied group demonstrated a lower mean percentage of analyzable time (95.9% ± 8.2%) than did both clinic-applied groups (COVID: 96.6% ± 6.6%; pre-COVID 96.6% ± 7.4%; P < .001). There were no differences in the mean percentage of prescribed wear time between groups (pre-COVID clinic-applied: 96.7% ± 34.3%; COVID clinic-applied: 97.4% ± 39.8%; COVID self-applied: 98.1% ± 52.1%; P = .09)., Conclusion: Self-applied ECG patches were returned at a lower rate and had a statistically lower percentage of analyzable time than clinic-applied patches. However, there were no differences between groups in mean percentages of prescribed wear time, and mean percentages of analyzable time were >95% in all groups., (Copyright © 2022 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

A questionnaire that can properly measure communal coping in sport is required to further investigate and understand how individuals in a team collectively cope with stressful sport situations. The Communal Coping Strategies Inventory for Competitive Team Sports (CCSICTS; Leprince et al., 2019) needed to be validated at the collective level and in its situational form to be used in broader sport situations. The aims of the present work were to improve and further validate the factorial structure of the CCSICTS at both individual and team levels. With a sample of 380 French athletes, representing 56 teams, Study 1 showed support for a multilevel, hierarchical and four-dimensional factorial structure of the revised version of the CCSICTS at both individual and team levels. With a sample of 641 French athletes, representing 75 teams, Study 2 confirmed the factorial structure obtained in Study 1 at individual and team levels, and its validity in sport-specific situations. The results of both studies also highlighted a bifactorial structure, allowing interpretation of communal coping as an overall team capacity to adapt to stress. As such, the psychometric qualities of the CCSICTS-R have been established at individual and team levels. The CCSICTS-R enables proper and distinct measurement of the characteristics of communal coping in sport (i.e., hierarchical, multidimensional, multilevel, both situational and dispositional) and can be used from both research and practical perspectives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Background: The Hungarian National Cancer Registry (HNCR) was legally established as a population-based cancer registry in 1999, and its operation started in 2000 supporting the planning and development of the Hungarian oncology network as well as informing national cancer control policies. Ensuring comparable, accurate, and complete data on malignant and in situ neoplasms is critical in determining the applicability of the database. The aim of this study was to perform a comprehensive evaluation of the data quality at the HNCR., Methods: Based on qualitative and semiquantitative methods from current international guidelines, we assess the comparability, completeness, validity, and timeliness of the collected data over the diagnostic period 2000-2019, with a focus on the year 2018., Results: Coding practices and the classification system used at the HNCR are based on the International Classification of Diseases (ICD-10), which differs from the internationally recommended ICD-O. The annual trends in incidence did not indicate major fluctuations, that may have resulted from data collection discrepancies, while comparisons of the mortality-to-incidence ratio (M:I) compared with 1 minus 5-year observed survival indicated some systematic differences requiring further exploration. The age-standardized (European standard) incidence rate per 100 000 measured by the HNCR in 2018 was very high: 647.9 for men and 501.6 for women, 11.6% and 14.6% higher than the International Agency for Research on Cancer (IARC) estimates respectively. Behind the overall differences between the two data sources, we identified that the vast majority were due to ill-defined ICD codes: malignant neoplasm of other and ill-defined sites (C76), and malignant neoplasm without specification of site (C80). Otherwise, there were no major discrepancies by localization. The proportion of morphologically verified cancer cases was 57.8% overall, that of death certificates was 2.3%, and that of unknown primary tumors was 1.4%., Conclusion: Further implementations and interventions are required to ensure that the operations, coding practices, and the classification system used at the national registry are in accordance with international standards, and to increase the completeness and validity of the collected cancer data. In particular, the low morphologically verified proportion questions the overall accuracy of the stated diagnoses within the database. Nevertheless, our examination implies that the data of the HNCR are reasonably comparable, and without doubt fulfill the requirements to support national oncology services and cancer planning. However, most importantly, a review of registry personnel and resource requirements to run the national population-based cancer registry should be an essential part of Hungary's national cancer strategy., (Copyright © 2022. Published by Elsevier Ltd.)

The majority of central European streams are in poor ecological condition. Pesticide inputs from terrestrial habitats present a key threat to sensitive insects in streams. Both standardized stream monitoring data and societal support are needed to conserve and restore freshwater habitats. Citizen science (CS) offers potential to complement international freshwater monitoring while it is often viewed critically due to concerns about data accuracy. Here, we developed a CS program based on the Water Framework Directive that enables citizen scientists to provide data on stream hydromorphology, physicochemical status and benthic macroinvertebrates to apply the trait-based bio-indicator SPEAR pesticides for pesticide exposure. We compared CS monitoring data with professional data across 28 central German stream sites and could show that both CS and professional monitoring identified a similar average proportion of pesticide-sensitive macroinvertebrate taxa per stream site (20 %). CS data were highly correlated to the professional data for both stream hydromorphology and SPEAR pesticides (r = 0.72 and 0.76). To assess the extent to which CS macroinvertebrate data can indicate pesticide exposure, we tested the relationship of CS generated SPEAR pesticides values and measured pesticide concentrations at 21 stream sites, and found a fair correlation similar to professional results. We conclude that given appropriate training and support, citizen scientists can generate valid data on the ecological status and pesticide contamination of streams. By complementing official monitoring, data from well-managed CS programs can advance freshwater science and enhance the implementation of freshwater conservation goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Various high-throughput systems and strategies are employed by the biopharmaceutical industry for early to late-stage process development for biologics manufacturing. The associated increases to experiment productivity and reduction in material consumption makes high throughput tools integral for bioprocess development. While these high-throughput systems have been successfully leveraged to generate high quality data representative of manufacturing scale processes, their data interpretation often requires complex data transformation and time-intensive system characterization. With respect to high throughput purification development, RoboColumns by Repligen operated on Tecan automated liquid handling systems offer superior performance scalability, but lack an optimized liquid delivery system that is representative of preparative chromatography. Particularly, stock Tecan liquid handling systems lack the capability to provide high-capacity continuous liquid flow and ideal linear gradient chromatography conditions. These limitations impact protein chromatography performance and hinder the application of high-throughput gradient elution experiments. In this work, we describe a Tecan Freedom EVO high-throughput purification tool that provides more continuous liquid delivery enabling continuous gradient elution capability for RoboColumn experiments as demonstrated by generation of highly linear conductivity gradients. Results demonstrate that the tool can provide RoboColumn performance and product quality data that is in agreement with larger, bench scale chromatography formats for two model purification methods. The described gradient purification method also provides more consistent performance between RoboColumns and larger column formats compared to step elution methods using the same optimized Tecan system. Lastly, new insights into the impact of discontinuous flow on RoboColumn elution performance are introduced, which may help further improve application of these data towards bioprocess development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Quality management is integral to the practice of cytopathology, especially given the heavily manual workflows and expanding ancillary testing requirements inherent to the cytopathology laboratory. Monitoring quality data like turnaround time, specimen unsatisfactory rates, and diagnostic category utilization rates allows for better understanding of performance with opportunities for targeted improvement if there are variations from that which is expected. However, there are costs to quality monitoring including the time and resources needed, and, in already taxed systems, quality management risks being viewed as just another box to check. While there are mandated quality metrics that must be collected by cytology laboratories, thoughtful selection of key performance indicators can be of tremendous benefit in helping to better understand complex laboratory processes and directing improvement endeavors where needed. The following short communication is a discussion on quality management in the cytopathology laboratory from 3 Cytopathology Quality Management Directors. The discussion focuses on monitoring the atypical reporting category with an emphasis on how trending and visualizing quality metrics can provide laboratories with key data., (Copyright © 2022 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Objective: The Society for Vascular Surgery Vascular Quality Initiative (VQI) has become an increasingly popular data source for retrospective observational vascular surgery studies. There are published guidelines on the reporting of data in such studies to promote transparency and rigor, but these have not been used to evaluate studies using VQI data. Our objective was to appraise the methodological reporting quality of studies using VQI data by evaluating their adherence to these guidelines., Methods: The Society for Vascular Surgery VQI publication repository was queried for all articles published in 2020. The REporting of studies Conducted using Observational Routinely-collected Health Data (RECORD) statement and the Journal of American Medical Association-Surgical Section (JAMA-Surgery) checklist were utilized to assess the quality of each article's reporting. Five and three items from the RECORD statement and JAMA-Surgery checklist were excluded, respectively, because they were either inapplicable or nonassessable. Journal impact factor (IF) was queried for each article to elucidate any difference in reporting standards between high and low IF journals., Results: Ninety studies were identified and analyzed. The median score on the RECORD checklist was 6 (of 8). The most commonly missed item was discussing data cleaning methods (93% missed). The median score on the JAMA-Surgery checklist was 3 (of 7). The most commonly missed items were the identification of competing risks (98% missed), the use of a flow chart to clearly define sample exclusion and inclusion criteria (84% missed), and the inclusion of a solid research question and hypothesis (81% missed). There were no differences in JAMA-Surgery checklist or RECORD statement median scores among studies published in low vs high IF journals., Conclusions: Studies using VQI data demonstrate a poor to moderate adherence to reporting standards. Key areas for improvement in research reporting include articulating a clear hypothesis, using flow charts to clearly define inclusion and exclusion criteria, identifying competing risks, and discussing data cleaning methods. Additionally, future efforts should center on creating tailored instruments to better guide reporting in studies using VQI data., (Copyright © 2022 Society for Vascular Surgery. All rights reserved.)

Background: Unexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database., Methods: We described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal., Main Findings: Funnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors., Conclusions: This paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)