Standardized Data Collection Research Articles

Pediatric erythromelalgia from multidisciplinary perspectives: a scoping review

Abstract Erythromelalgia is a rare, chronic pain disorder characterized by the triad of intense burning sensation, warmth, and redness, primarily involving the hands and feet, and usually alleviated by cold and worsened by heat. The objective of this scoping review was to: 1) map the existing literature on erythromelalgia in youth, 2) identify knowledge gaps, and 3) inform directions for future research in pediatric erythromelalgia. One hundred and sixty-seven studies reporting 411 cases of childhood-onset erythromelalgia were identified. Variability was found in reporting of clinical symptoms, the clinical presentations and diagnostic criteria used for classification of erythromelagia, the clinical assessments and investigations performed, and the types of interventions and management plans utilised. While factors to aid early recognition and optimize management have been identified, there are also significant gaps for future research to address. Ongoing efforts to develop a multicenter registry of pediatric erythromelalgia cases, with standardized data collection and reporting, will be beneficial to establish consensus recommendations for the diagnosis and management of pediatric erythromelalgia. Impact This scoping review maps the existing literature on pediatric erythromelalgia. Variability was found in reporting of clinical symptoms, the clinical presentations and diagnostic criteria used for classification of erythromelagia, the clinical assessments and investigations performed, and the types of interventions and management plans utilised. The development of an international registry would immensely benefit multidisciplinary experts involved in the care of pediatric erythromelalgia and those with lived experience.

Alveolar Measurements in Dental Anthropology: An Alternative Metric Technique in Cases of Postmortem Missing Teeth

Background/Objectives: The aim of this study was to examine the relationship between the mesiodistal and buccolingual alveolar diameters and the usual crown and cervical diameters and to test the reliability of these alveolar measurements for their application in dental anthropology in cases of missing teeth. Methods: A total of 127 skeletal individuals from the identified osteological collection of Certosa Cemetery (Bologna, Italy) were used in this study. After the evaluation of limiting factors, only the central incisor to the second premolar was analysed due to a null or small sample size for the molars. The mesiodistal and buccolingual diameters were measured at the level of the crown, cervix and alveolus. The relationship between the mesiodistal and buccolingual crown or cervical diameters and the alveolar measurements was assessed. Results: The buccolingual alveolar diameters showed consistently significant relationships with their equivalent cervical and crown diameters, while the mesiodistal alveolar diameters did not show consistent relationships. Furthermore, the patterns of phenotypic variation were similar for the alveolar, cervical and crown areas. Thus, the alveolar areas appear to show similar levels of variability compared with the equivalent crown and cervical areas. Conclusions: Alveolar measurements may serve as suitable proxies in comparative phenotypic variation studies and can be considered a useful supplement to the standard odontometric data collection strategy. The measurements proposed in this study for the evaluation of the alveoli are a valid alternative in situations in which teeth are not available for measurement, such as in forensic and archaeological contexts.

Leveraging Digital Mobility Data to Estimate Visitation in National Wildlife Refuges

The US Fish and Wildlife Service (USFWS) manages over 560 National Wildlife Refuges and dozens of National Fish Hatcheries across the United States. Accurately estimating visitor numbers to these areas is essential for understanding current recreation demand, planning for future use, and ensuring the ongoing protection of the ecosystems that refuges safeguard. However, accurately estimating visitation across the entire refuge system presents significant challenges. Building on previous research conducted on other federal lands, this study evaluates methods to overcome constraints in estimating visitation levels using statistical models and digital mobility data. We develop and test a visitation modeling approach using multiple linear regression, incorporating predictors from eight mobility data sources, including four social media platforms, one community science platform, and three mobile device location datasets from two commercial vendors. We find that the total number of observed visitors to refuges correlates with the volume of data from each mobility data source. However, neither social media nor mobile device location data alone provide reliable proxies for visitation due to inconsistent relationships with observed visitation; these relationships vary by data source, refuge, and time. Our results demonstrate that a visitation model integrating multiple mobility datasets accounts for this variability and outperforms models based on individual mobility datasets. We find that a refuge-level effect is the single most important predictor, suggesting that including site characteristics in future models will make them more generalizable. We conclude that statistical models which incorporate digital mobility data have the potential to improve the accuracy of visitor estimates, standardize data collection methods, and simplify the estimation process for agency staff.

Development of a standardized data collection and intelligent fabric quality prediction system for the weaving department

The diversity of weaving equipment has led to inconsistencies in communication protocols, impeding data collection and interoperability between devices, and ultimately reducing production efficiency. Additionally, fabric defects significantly impact product quality, while current visual inspection technologies are primarily reactive and traditional quality prediction methods often exhibit considerable errors. This study leverages the standardization and interoperability features of open platform communications unified architecture technology to facilitate data acquisition within the weaving department, establishing a reliable Internet of Things framework that supports subsequent fabric quality prediction, and optimizing the back propagation neural network through the K-means clustering algorithm and particle swarm optimization to predict the type and number of fabric defects. A comparative analysis with traditional BP and PSO-BP prediction models was conducted, ultimately verifying the feasibility of using OPC UA to transmit weaving data for fabric quality prediction. The research results demonstrate that using OPC UA technology enables the unified transmission of weaving equipment data, addressing the issue of heterogeneity in weaving department equipment. The K-means-PSO-BP model can effectively predict defects such as double weft, hundred feet, and broken warp with minimal error, achieving a root mean square error of less than 0.15.

Paediatric Personalized Research Network Switzerland (SwissPedHealth): a joint paediatric national data stream

IntroductionChildren represent a large and vulnerable patient group. However, the evidence base for most paediatric diagnostic and therapeutic procedures remains limited or is often inferred from adults. There is an...

Delivering More from Land: A Review of Integrated Land Use Modelling for Sustainable Food Provision

We conduct a literature review on integrated land use modelling to guide policy on sustainable food provisioning. Modelling approaches are discussed in the spatial, temporal, and human dimensions, as well as environmental and socio-economic considerations. Many studies have focused on model development over their application to specific policy objectives, often relying on top-down approaches. While ecosystem services are a frequent focus, indicators for their assessment are inconsistently quantified. Socio-economic considerations are coarse in scale compared to biophysical ones, limiting their use in nuanced policy development. Recommendations are made such as standardising data collection and sharing to streamline modelling processes and collaboration. Comprehensive ecosystem services frameworks would benefit from a more uniform classification of values. More bottom-up modelling, connected with top-down models, could account for the heterogeneity between smaller units of analysis such as the landscape, farms, or people. This could reveal further insights into the local contexts and decision-making responses essential for effective land use policy. These advancements would help to design policies that address the complexities of sustainable food provisioning. By connecting local and global perspectives, future models can support more resilient and adaptive food systems, ensuring sustainability in the face of environmental and socio-economic challenges.

Pioneering automation in agricultural subsidy processing through deep learning for computer vision

ABSTRACT Validating farmers’ eligibility for subsidies under the European Union’s Common Agricultural Policy (CAP) requires substantial resources. An automated solution can minimize human errors and save time. This study leverages deep learning to automate the validation process using ground-level landscape images (GLLP), an approach not previously explored. We conducted two rigorous experiments to identify the optimal model architecture and assess the impact of data cleaning, accurate labelling, reduced feature ambiguity, and stricter photo collection protocols on performance. Our results reveal that while the ResNet-18 architecture achieved a recall of 0.61 on the original dataset, distinguishing between eligible and ineligible grasslands was challenging. By addressing image quality and enforcing standardized data collection, accuracy improved to 0.89 and recall to 0.94. Crucially, our research emphasizes the development and validation of effective data collection strategies, ensuring uniformity and high-quality data essential for accurate model performance. Our findings affirm the potential of deep learning to automate subsidy eligibility classification, contingent on resolving challenges related to the quality and standardization of GLLP data.

Unveiling the role of DHIS2 in enhancing data quality and accessibility in primary healthcare facilities: Evidence from Ethiopia.

The implementation of DHIS2 in healthcare systems has transformed data management practices worldwide. However, its specific impact on data quality, availability, and performance in Primary Health Unit (PHU) facilities in Ethiopia remains underexplored. Therefore, we investigated the contribution of DHIS2 to enhancing data quality, availability, and performance within PHU facilities in Ethiopia. We employed qualitative methods, specifically Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs), to gather insights from stakeholders, including healthcare providers and administrators at PHCUs across Ethiopia. Convenience sampling was used for FGDs, while purposive sampling targeted key informants with relevant expertise. Data were systematically analysed thematically, identifying patterns and themes related to DHIS2's impact on data management within PHUs. This approach offered a comprehensive understanding of the system's effectiveness and the factors influencing its implementation, highlighting both successes and challenges in integrating DHIS2 into healthcare practices. Participants from various regions reported significant enhancements in the timeliness, completeness, accuracy, and accessibility of health data following the implementation of DHIS2. While some concerns were raised regarding variations in reporting intervals, the consensus indicated marked improvements in data management processes. DHIS2 standardized data collection methods, enabling healthcare providers to input and access data in real-time. This advancement fostered greater accountability and transparency within the healthcare system. Additionally, unexpected benefits arose, including increased digital literacy among staff, equipping them with necessary skills for effective data management, and the creation of job opportunities, particularly for youth. Ultimately, DHIS2 emerged as a pivotal tool for enhancing data quality and promoting health service equity across Ethiopia. DHIS2 has significantly improved data quality and accessibility in Ethiopia, enhancing healthcare management and accountability across facilities. Healthcare providers should continue to leverage its robust features and prioritize ongoing staff training to improve digital literacy and data management skills. Establishing consistent reporting practices and regular audits will further maintain data integrity and foster a culture of accountability within the healthcare system.

Using data from routine health information systems as a public good in Trinidad and Tobago.

This study aimed to explore the use of data from routine health information systems (RHIS) as a public good in Trinidad and Tobago, the challenges faced in doing this and opportunities for strengthening the health information system. For this descriptive qualitative study, purposive sampling was utilized to recruit 19 people who used or produced RHIS data. Online interviews were conducted via Zoom, and all interviews were recorded and transcribed. Pseudonyms were used to protect participants' identity. Transcripts were cleaned and analyzed using Dedoose (v. 9.0.17; Dedoose, Los Angeles, CA, USA). There was significant underutilization of RHIS data as a public good, primarily due to challenges related to data access and quality. Access to the data was stymied by burdensome bureaucratic processes, paper-based recording and storage systems, and ownership and security concerns. Data quality was adversely affected by a lack of standardized data collection forms and processes, staff training, data completeness, and also by technological and infrastructural constraints. Key opportunities for increasing the use of Trinidad and Tobago's RHIS data would include addressing the need for a national electronic health information system, ensuring adequate training for staff involved in data management, and developing a comprehensive monitoring and evaluation plan. Data quality and access must be improved to enable greater use of RHIS data as a public good in Trinidad and Tobago. The planned change from a paper-based to a national electronic data recording and storage system must be expedited, and it should be accompanied by the standardization of processes, and investments in adequate staffing and timely training. Appropriate technological and infrastructural support, as well as an improved data governance system, are also required.

Policy Brief: Better Data for Breast Cancer in Libya

Breast cancer represents a growing health challenge in Libya, where data management gaps hinder effective diagnosis, treatment, and research efforts. This policy brief highlights the urgent need for a consolidated data framework to improve the quality and accessibility of breast cancer data across the country. Through standardized data collection protocols and a centralized database, healthcare professionals can better monitor trends, enabling early diagnosis and timely interventions. The proposed approach includes training healthcare personnel in data management best practices and establishing a multi-stakeholder steering committee to oversee data governance, privacy, and security. By addressing these issues, Libya can enhance breast cancer treatment outcomes, support evidence-based policymaking, and contribute to international research efforts. This initiative underscores the critical role of data-driven approaches in advancing public health and improving patient outcomes.

A correlational study on teacher-student relationships and students’ engagement in EFL classes

This study aims to investigate the levels of teacher-student relationships (TSR) and student engagement (SE) within an English as a Foreign Language (EFL) context among general English (GE) level students enrolled in an English course at a private language school in Indonesia. This study also seeks to examine the correlation between TSR and SE in EFL classes. In finding the correlation between TSR and SE in EFL classes, this study attempts to fill a gap in the existing literature by employing a quantitative method, specifically a survey-based approach, to systematically collect and analyze data from a diverse pool of participants. The choice of quantitative method is grounded in the need for standardized data collection and analysis to determine the degree of relationship between TSR and SE among GE-level students. By administering surveys to GE-level students, this study seeks to gain insights into their perceptions of TSR and its impact on their engagement in EFL classes. This study found that the TSR and the SE levels were high. Moreover, this study found a statistically significant positive correlation between TSR and SE. The findings of this research endeavor to provide valuable insights into how TSR can catalyze SE and subsequently enhance language proficiency among EFL learners in Indonesia, with implications for both theory and practice in EFL education.

Comparing two large data repositories to understand the differences in demographics, health history, and behavioral attributes in populations.

This study conducted a comparative analysis between two large data repositories, the All of Us (AoU) medical data and BigMouth dental data repositories. The comparison analysis includes variables related to behavioral and systemic health, health literacy, and overall health status across race, ethnicity, and gender. The analytic approach used descriptive statistics, Chi-square, odds ratio, and 95% confidence intervals; significant comparisons were measured with Cohen's D effect sizes. In the AoU dataset, 80.6% of Hispanic or Latino participants reported alcohol use compared to 16.8% in the BigMouth data repository. The female cohort in AoU showed 87.9% alcohol use, a contrast to BigMouth's 26.0%. Additionally, the diabetes prevalence among females was 8.8% in AoU vs. 21.6% in BigMouth. Differences in health literacy were observed, with 49.2% among Hispanic or Latino participants in AoU, in contrast to BigMouth's 3.2%. Despite this, 70.1% of Hispanic or Latino respondents in AoU reported satisfactory health status, while BigMouth indicated a much higher figure at 98.3%. These variations highlight the importance of targeted health interventions addressing racial/ethnic and gender influences. Differences may arise from recruitment approaches, participant demographics, and healthcare access. There is a need for collaboration, standardized data collection, and inclusive recruitment to remedy these discrepancies. Further research is imperative to understand the underlying causes, facilitate interventions that address the disparities, and advocate for a more inclusive healthcare system.

Nonparametric Testing of Body Mass Index as a Predictor of Severe Noma: Assessing Potential Association.

Noma, a devastating facial disease, primarily affects impoverished populations in low- and middle-income countries, often leading to severe disfigurement or fatality if untreated. Studies suggest malnutrition is a significant risk factor for noma, yet the association between body mass index (BMI) in kilograms per square meter and tissue damage severity remains underexplored. This study aimed to investigate the relationship between BMI and tissue damage severity among noma cases in Ethiopia. A retrospective analysis of medical records from Facing Africa's database covering noma cases treated between 2007 and 2019 was conducted. A standardized data collection template recorded demographic and clinical data, including patient demographics, BMI, and tissue damage severity levels. JASP statistical software was used for analysis. Analysis of 228 noma cases revealed a mean BMI of 17.8 kg/m2, ranging from 11.5 to 28.3 kg/m2. Females exhibited a higher mean BMI (18.3 kg/m2) than males (16.9 kg/m2). Differences in BMI distributions between major (67) and minor (30) tissue damage categories were observed, with "minor" cases having a slightly higher mean BMI. The study found a statistically significant association between BMI and tissue damage severity (P = 0.03), indicating that tissue damage due to noma worsened as BMI decreased. Nonparametric tests confirmed this association's significance. This study highlights low BMI as a predictor of severe noma. Public health interventions should focus on malnutrition to reduce noma severity and psychosocial and functional morbidity and improve outcomes. However, additional factors influencing the progression of noma and the severity of associated tissue damage require further investigation.

Learning from post-COVID-19 condition for epidemic preparedness: a variable catalogue for future post-acute infection syndromes.

The emergence of post-COVID-19 condition (PCC) after SARS-CoV-2 infection underscores the critical need for preparedness in addressing future post-acute infection syndromes (PAIS), particularly those linked to epidemic outbreaks. The lack of standardized clinical and epidemiological data during the COVID-19 pandemic has significantly hindered timely diagnosis and effective treatment of PCC, highlighting the necessity of pre-emptively standardizing data collection in clinical studies to better define and manage future PAIS. In response, the Cohort Coordination Board, a consortium of European-funded COVID-19 research projects, has reviewed data from PCC studies conducted by its members. This paper leverages the Cohort Coordination Board's expertise to propose a standardized catalogue of variables, informed by the lessons learned during the pandemic, intended for immediate use in the design of future observational studies and clinical trials for emerging infections of epidemic potential. The early implementation of standardized data collection, facilitated by the PAIS data catalogue, is essential for accelerating the identification and management of PAIS in future epidemics. This approach will enable more precise syndrome definitions, expedite diagnostic processes, and optimize treatment strategies, while also supporting long-term follow-up of affected individuals. The availability of harmonized data collection protocols will enhance preparedness across European and international cohort studies, and trials enabling a prompt and coordinated response, as well as more efficient resource allocation, in the event of emerging infections and associated PAIS.

Clinical Insights and Outcomes in Community-Acquired Acute Bacterial Meningitis versus Postoperative Bacterial Meningitis.

To compare the demographics, clinical characteristics, and in-hospital mortality rates between community-acquired bacterial meningitis cases and postoperative bacterial meningitis. Analytical study. Place and Duration of the Study: Department of Infectious Diseases and Clinical Microbiology, Diskapi Yildirim Beyazit Training and Research Hospital, Ankara, Turkiye, from 2016 to 2022. A total of 153 patients diagnosed with bacterial meningitis were included and categorised into two groups: 95 (62.1%) with community-acquired bacterial meningitis (CABM) and 58 (37.9%) with postoperative bacterial meningitis (POBM). Demographics, clinical features, laboratory, paraclinical findings, treatments, and outcomes of the cases were compared. Data were retrieved using a standard data collection form from the electronic medical records. A substantial portion (58.8%) of all patients had comorbidities. Fever was the common symptom in all groups. Headache, neck stiffness, nausea, and vomiting were more often observed in the CABM group (p <0.001). Upon admission, the CABM exhibited higher levels of white blood cell count, C-reactive protein, and procalcitonin (p = 0.017, p = 0.004, p = 0.007, respectively). Overall 33.1% had positive cerebrospinal fluid cultures. The overall mortality rate was 26.8%. POBM was associated with longer hospital and intensive care unit (ICU) stays (p <0.001). Shorter treatment durations, lower Glasgow coma scale scores (GCS), higher Charlson Comorbidity Index values, and elevated markers of inflammation were related to mortality. This study illuminates the differences in clinical presentations and outcomes between community-acquired and postoperative bacterial meningitis. It also suggests that factors such as lower GCS scores, comorbidities, and elevated inflammation markers at the last follow-up may be associated with unfavourable clinical outcomes in bacterial meningitis. Postoperative meningitis, Central nervous system infection, Cerebrospinal fluid, Antibiotics.

Standardizing social determinants of health data: a proposal for a comprehensive screening tool to address health equity a systematic review.

Social determinants of health (SDoH) significantly impacts health outcomes and disparities. While the Centers for Medicare and Medicaid Services has mandated hospitals to collect standardized SDoH data, existing tools lack key elements. This systematic review identified 78 studies and 20 screening tools addressing various SDoH domains. However, most tools were missing several key domains and lacked standardization. We propose a comprehensive tool meeting essential criteria: validated questions, brevity, actionability, cultural appropriateness, workflow integration, and community linkage. Our tool addresses gaps in available tools and incorporates standardized and validated questions to enable patient-centered screening for diverse social and environmental determinants of health. It uniquely includes detailed race/ethnicity data collection, housing characteristics, physical activity assessment, access to healthy food measures, and environmental exposure evaluation. The tool aims to provide actionable data for immediate interventions while informing broader population health strategies and policy initiatives. By offering a holistic assessment of SDoH across multiple domains, our tool enables standardized data collection, risk stratification, and focused initiatives to address health inequities at both individual and population levels. Further research is needed to develop evidence-based pathways for integrating SDoH data into real-world patient care workflows, improve risk prediction algorithms, address health-related social needs, and reduce disparities.

Brain disease Research Infrastructure for Data Gathering and Exploration (BRIDGE): A government‐led platform for standardized data collection, longitudinal monitoring, and sharing of Korean data sources for brain disorders

Brain disease Research Infrastructure for Data Gathering and Exploration (BRIDGE): A government‐led platform for standardized data collection, longitudinal monitoring, and sharing of Korean data sources for brain disorders

Development of a minimum data set for long COVID: a Delphi study protocol

IntroductionPrevious consensus-based long COVID research has focused on establishing research priorities, developing clinical definitions, core outcomes and a list of recommendations of patient-reported outcome measures that can be used to...

Reversible Electroporation for Cancer Therapy.

Reversible electroporation refers to the use of high voltage electrical pulses on tissues to increase cell membrane permeability. It allows targeted delivery of high concentrations of chemotherapeutic agents including cisplatin and bleomycin, a process known as electrochemotherapy (ECT). It can also be used to deliver toxic concentrations of calcium and gene therapies that stimulate an anti-tumour immune response. ECT was validated for palliative treatment of cutaneous tumours. Evidence to date shows a mean objective response rate of ∼80% in these patients. Regression of non-treated lesions has also been demonstrated, theorised to be from an in-situ vaccination effect. Advances in electrode development have also allowed treatment of deep-seated metastatic lesions and primary tumours, with safety demonstrated in vivo. Calcium electroporation and combination immunotherapy or immunogene electrotransfer is also feasible, but research is limited. Adverse events of ECT are minimal; however, general anaesthesia is often necessary, and improvements in modelling capabilities and electrode design are required to enable sufficient electrical coverage. International collaboration between preclinical researchers, oncologists and interventionalists is required to identify the most effective combination therapies, to optimise procedural factors, and to expand use, indications and assessment of reversible electroporation. Registries with standardised data collection methods may facilitate this.

National Burden and Epidemiological Features of Mass Casualty Incidents in Spain, from 2014 to 2022.

Mass Casualty Incidents (MCIs) pose significant challenges to health care systems, especially regarding emergency preparedness and response. This study aims to analyze the epidemiological characteristics and burden of MCIs in Spain from 2014 to 2022, focusing on the type, frequency, and impact of these incidents on public health and emergency services. A population-based retrospective observational study examined MCIs in Spain between January 2014 and December 2022. Data were collected from various emergency services. Incidents involving 4 or more victims requiring medical assistance and ambulance mobilization were included. The study categorized MCIs into 5 types: road traffic accidents, fires and explosions, chemical poisonings, maritime accidents, and others. A total of 1618 MCIs resulting in 8556 victims were identified, averaging 15 (95% CI, 11-19) incidents per month, with 79% due to road traffic accidents and 13% to fires and explosions, which also had the highest average of 7.6 victims per incident. Despite maritime accidents comprising only 1.9% of incidents, they had the highest fatality rate. MCIs were more frequent on weekends, in January and July, and between 3:00 PM and 9:00 PM. The average response time was 38 minutes, with 35% of victims sustaining severe injuries. Despite a slight decrease in annual MCIs from 2014 to 2022 in Spain, the trend is not statistically significant. The study highlights the need for a national registry and standardized data collection to enhance emergency preparedness and response planning and facilitate the reduction of the MCI burden.