Data Quality Assessment Research Articles

Data quality assessment of aggregated LCI datasets: A case study on fossil‐based and bio‐based plastic food packaging

AbstractEnvironmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.

Epidemiology of obesity during pregnancy in Europe: a systematic review

Abstract Background Obesity during pregnancy poses inter-generational health risks and places added burden on healthcare systems. Characterising levels of obesity during pregnancy is necessary to guide future research, policy and practice. Methods The systematic review was performed according to PRISMA guidelines. PubMed, Embase and Web of Science were searched. Studies published between 2009 and 2023 that report the prevalence of obesity during pregnancy in countries in the World Health Organization (WHO) European region were eligible. The Joanna Briggs Institute Prevalence Critical Appraisal tool was used. Two reviewers screened studies independently. A single reviewer completed data extraction and quality assessments, a second checked 20% of extractions and a third resolved disagreements. Narrative synthesis was completed. Results 100 studies reporting the prevalence of obesity during pregnancy in the WHO European region were identified. 59% from Western Europe, 36% Northern, 4% Southern and 1% Eastern. Data within the studies was collected between 1950 and 2019. 35% of studies reported national estimates, 65% regional. 39% studies had measured height and weight, 34% did not report how either were ascertained, 6% used self-report for both, 17% had self-reported height but measured weight, 4% a mixture of self-report and measurement. The smallest sample was 157 while the largest was 1,891,097. Estimated prevalence of obesity during pregnancy ranged from 2.3% (Sweden, data collected 1982-1989) to 28.3% (England, 2007). Conclusions This review identifies variation in the prevalence of obesity during pregnancy in the WHO European region. This may reflect diverse sociocultural contexts, different methodological approaches and temporal trends. There was limited evidence available for Southern and Eastern Europe. Key messages • There are high levels of obesity during pregnancy within the WHO European region. • Enhanced primary prevention and resourcing of obstetric services is needed.

The Effects of Subsidies for Healthy Foods on Food Purchasing Behaviors, Consumption Patterns, and Obesity/Overweight: A Systematic Review.

Taxing unhealthy foods and drinks is an essential measure against the double burden of malnutrition that affects every nation worldwide. In turn, subsidizing the consumption of healthy foods can also be a critical measure for changing the population's behavior and improving health indicators. A systematic review was conducted of food subsidies and their potential impact on food purchases, consumption, overweight/obesity, and changes in body mass index (BMI). The PubMed, Embase, LILAC, Scientific Electronic Library Online, and Google Scholar databases were searched to identify studies investigating the effects of subsidies on the amount of food purchased, food consumption, caloric intake, nutrient intake, and their impact on overweight, obesity, and BMI changes. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Two investigators independently performed data screening, extraction, and quality assessment. Of the 6135 studies screened, 149 were read in full and 18 were included in this systematic review. Most studies investigated the effects of subsidy scenarios on food purchases and observed increases in fruit and vegetable (F&V) purchases when these were subsidized. In randomized controlled studies, subsidized healthy foods were purchased more often. However, when the subsidy was suspended, F&V consumption decreased. Although quasi-experimental studies have demonstrated increased F&V consumption due to subsidies, some studies showed increased saturated fat and sodium intake. Only 2 studies evaluated the relationship between subsidies and obesity, yielding conflicting results. Although the subsidies appear to encourage purchase and consumption of healthier foods, enhancing the quality and diversity of dietary choices, the removal of subsidies can lead to a decline in the consumption of healthier foods. Additionally, their impact on obesity and BMI remains uncertain and requires further research. PROSPERO registration by the number CRD42023442122.

Weaning Failure in Elderly Patients: A Systematic Review and Meta-Analysis

Background/Objectives: Weaning failure in elderly patients undergoing mechanical ventilation presents a complex clinical challenge due to age-related physiological changes and comorbidities. Understanding the dynamics of this phenomenon through systematic analysis can provide valuable insights for clinical management. This meta-analysis aims to investigate the factors contributing to weaning failure in elderly patients and to assess the efficacy of different strategies in mitigating this challenge. Design: The design of this study is a systematic review and meta-analysis. Methods: A systematic search of electronic databases was conducted to identify relevant studies focusing on weaning failure in elderly patients. Studies reporting outcomes related to mechanical ventilation weaning failure were included. Data extraction, quality assessment, and statistical analysis were performed following established guidelines. Results: A total of 15 studies met the inclusion criteria and were included in the meta-analysis. The average age of participants throughout the studies was 66.24 ± 10.21 years. This suggests that the study population largely consisted of older adults and displayed a moderate range of ages centered around the mean. The rate of weaning failure across these studies was slightly above 31.56%, indicating a significant occurrence of this complication in the patient cohorts. The analysis revealed age-related physiological changes, such as decreased respiratory muscle strength and increased chest wall stiffness, as significant contributors to weaning failure in elderly patients. Comorbidities, including chronic pulmonary diseases and cardiovascular conditions, further exacerbated the challenge. Various interventions, including tailored weaning protocols and respiratory therapies, showed promising results in improving weaning outcomes in this population. Conclusions: Weaning failure in elderly patients undergoing mechanical ventilation is influenced by a combination of age-related physiological changes and comorbidities. Tailored interventions addressing these factors are essential for optimizing weaning success rates in this vulnerable population. Further research is warranted to refine the strategies and enhance outcomes in elderly patients requiring mechanical ventilation.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

Background/Objective: Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. Results: The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. Conclusions: While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Biological Invasion Data Gaps in China: Examples of Distribution, Inventories, and Impact

The impact of invasive alien species (IAS) on nature and society is increasing globally. It is crucial to utilize information systems for evidence-based management, enabling the assessment and supporting survey and control actions. However, the lack of accessible and comprehensive baseline IAS data often impedes the ability to prioritize and allocate resources efficiently. Despite the increased public awareness of biological invasions in China over the past decades, the critical importance of data requirements has not been fully recognized, leading to gaps in available data. Here, we outline the key data demands for the management of biological invasions and highlight the current lack of high-quality data for invasion management in China, and critically assess data gaps in IAS distribution, inventory, and impact. Additionally, we propose a conceptual framework to illustrate the data requirements throughout the invasion management process, along with indicators to assess data quality within three dimensions: relevance, resolution, and reliability.

Scrub typhus-leptospirosis co-infection in India: a systematic review and meta-analysis.

Scrub typhus and leptospirosis are re-emerging zoonotic infections with significant morbidity and mortality rates in India. Overlapping aetiological and epidemiological patterns indicate a high possibility of their co-infection in India, which can be a diagnostic challenge due to non-specific clinical features. A systematic search of the PubMed/MEDLINE, Scopus and Embase databases was conducted to identify relevant studies published through 31 August 2023. Studies reporting co-infection of scrub typhus and leptospirosis among the Indian population were included. Data extraction, quality assessment and statistical analysis were performed in accordance with established guidelines. Six studies met the inclusion criteria, comprising a total of 58 co-infection cases. Pooled prevalence of scrub typhus-leptospirosis co-infection among acute undifferentiated febrile illness patients was 3.7% (95% confidence interval [CI] 0.00 to 0.126). Among scrub typhus patients, the prevalence of co-infection with leptospirosis was 13.7% (95% CI 0.027 to 0.304). Significant heterogeneity was observed among the included studies, highlighting the need for cautious interpretation of prevalence estimates. This meta-analysis underscores the clinical importance of scrub typhus-leptospirosis co-infection in India and emphasizes the need for enhanced clinical awareness, improved diagnostic strategies and targeted research efforts to address this emerging infectious disease threat.

Association between pulse wave velocity and cerebral microbleeds: a systematic review and meta-analysis.

Cerebral microbleeds are associated with events that are among the highest mortality and disability events combined worldwide, as well as with hypertensive vasculopathy. The aim of the present study was to investigate the relationship between a marker of hypertensive vasculopathy, arterial stiffness assessed by pulse wave velocity, and cerebral microbleeds. A systematic review and meta-analysis was performed using PubMed, Scopus, and Web of Science, according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Cochrane Collaboration Handbook statements. Data extraction, quality assessment and statistical analyses were performed following pre-established criteria. Twenty-one studies involving 18,436 participants were included. Higher levels of pulse wave velocity were associated with a higher presence of cerebral microbleeds p-OR = 1.26 (95% CI; 1.09-1.45), with considerable heterogeneity; even adjusting for potential confounding variables p-OR = 1.12 (95% CI, 1.05-1.20), with substantial heterogeneity. Only the percentage of women was related to p-OR in the adjusted model. Sensitivity analyses confirmed the robustness of our results. Adjusted models showed publication bias. Higher levels of arterial stiffness are associated with greater presence of cerebral microbleeds. This phenomenon may be caused by damage to the brain under higher blood flow loads, in turn due to age-induced reversal of the stiffness gradient between large and small vessels. As the world's population is undergoing demographic ageing, our results underline the importance of establishing pulse wave velocity as a cardiovascular marker for early screening and delaying the onset of the characteristic signs of both diseases.

A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study leverages big data and machine learning to identify key parameters influencing student dropout, develop a predictive model, and enable real-time monitoring and timely interventions by educational authorities. Two preliminary trials refined machine learning models, established evaluation standards, and optimized hyperparameters. These trials facilitated the systematic exploration of model performance and data quality assessment. Achieving close to 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having a significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates.

Early Postoperative Outcomes of the Direct Superior Approach versus the Posterior Approach in Total Hip Arthroplasty: A Systematic Review and Meta-Analysis

Background/Objectives: The direct superior approach (DSA) is a tissue-sparing alternative to the traditional posterior approach (PA) in total hip arthroplasty (THA), potentially offering improved recovery and fewer complications. This study compares perioperative parameters, radiological and functional outcomes, and complications between the DSA and the PA in primary THA. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Databases searched included MEDLINE/PubMed, Cochrane Library, Embase, and Scopus. Studies comparing the DSA and the PA in primary THA were included based on predefined criteria. Data extraction and quality assessment were performed independently by two authors. Statistical analyses included calculating standardized mean differences (SMD), odds ratios (OR), and 95% confidence intervals (CI). Heterogeneity was assessed using the χ2 test, I2 statistics, and sensitivity analyses. Results: Out of 126 identified articles, 10 studies were included, which encompassed 28,063 patients (DSA: 1464; PA: 26,599). Significant advantages of the DSA over the PA were observed in blood loss (SMD −0.26, p < 0.01), transfusion rate (OR 0.59, p = 0.03), length of stay (SMD −0.59, p < 0.01), discharge to home rates (OR 2.32, p < 0.01), and incision length (SMD −2.75, p < 0.01). No significant differences were found in radiological outcomes or most functional scores, although the DSA showed higher Harris Hip Scores at 1 month (SMD 0.77, p < 0.01). Conclusions: The DSA offers significant perioperative advantages over the PA, including reduced blood loss, transfusion rates, LOS, incision length, and improved early functional recovery with higher discharge to home rates. Comparable complication rates and radiological outcomes support the DSA’s safety and efficacy for quicker recovery in THA.

Proton pump inhibitor use and risk of stroke: A systematic review and meta-analysis

Objective: To explore a link between the use of proton pump inhibitor (PPI) and the risk of stroke. Methods: Comprehensive literature search in PubMed, EMBASE, and Cochrane CENTRAL Library databases was carried out for observational studies establishing the link between PPI and a risk of stroke. Data extraction and quality assessment were performed by two reviewers. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) using random-effects models were plotted. Subgroup analyses were conducted based on age, gender, PPI type, duration of follow-up, and propensity score matching (PSM). Results: The analysis included 12 studies, with considerable heterogeneity (I2 = 95%). PPI use did not affect the incidence of ischemic stroke (HR: 1.11, 95% CI: 0.98-1.26). Subgroup analyses revealed that PPI use correlated with the risk of ischemic stroke, in particular in patients<65 years old (HR: 1.25, 95% CI: 1.07-1.45), both males (HR: 1.12, 95% CI: 1.02-1.24) and females (HR: 1.21, 95% CI: 1.10-1.33). The correlation varied depending on the PPI type, with pantoprazole showing elevated risk (HR: 1.66, 95% CI: 1.43-1.93). Duration of follow-up or propensity score matching (PSM) did not impact the association. Conclusion: PPI use may be linked with ischemic stroke, particularly in individuals <65 years old and of all genders. The specific PPI type may also influence the risk. However, the cumulative analysis did not find any statistically significant association, and heterogeneity among studies was substantial. PROSPERO Registration Number: CRD42023422247. doi: https://doi.org/10.12669/pjms.40.10.10409 How to cite this: Feng Y, Zhong Y. Proton pump inhibitor use and risk of stroke: A systematic review and meta-analysis. Pak J Med Sci. 2024;40(10):2432-2440. doi: https://doi.org/10.12669/pjms.40.10.10409 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Comorbid health outcomes in patients with schizophrenia: an umbrella review of systematic reviews and meta-analyses.

There is no comprehensive umbrella review exploring the connection between schizophrenia and various health outcomes. Therefore, we aimed to systematically review existing meta-analyses about schizophrenia-associated comorbid health outcomes and validate the evidence levels. We performed an umbrella review of meta-analyses of observational studies to explore comorbid health outcomes in individuals with schizophrenia. Searches were conducted across PubMed/MEDLINE, EMBASE, ClinicalKey, and Google Scholar up to September 5, 2023, targeting meta-analyses of observational studies related to comorbid health outcomes in individuals with schizophrenia. We applied AMSTAR2 for data extraction and quality assessment, adhering to PRISMA guidelines. Evidence credibility was evaluated and categorized by evidence quality. Our protocol was registered with PROSPERO (CRD42024498833). Risk and protective factors were analyzed and presented through equivalent odds ratios (eRR). In this umbrella review, we analyzed 9 meta-analyses, including 88 original articles, covering 21 comorbid health outcomes with over 66 million participants across 19 countries. Patients with schizophrenia showed significant associations with multiple health outcomes, including asthma (eRR, 1.71 [95% CI, 1.05-2.78], class and quality of evidence [CE] = non-significant), chronic obstructive pulmonary disease (1.73 [1.25-2.37], CE = weak), pneumonia (2.63 [1.11-6.23], CE = weak), breast cancer of female patients (1.31 [1.04-1.65], CE = weak), cardiovascular disease (1.53 [1.12-2.11], CE = weak), stroke (1.71 [1.30-2.25], CE = weak), congestive heart failure (1.81 [1.21-2.69], CE = weak), sexual dysfunction (2.30 [1.75-3.04], CE = weak), fracture (1.63 [1.10-2.40], CE = weak), dementia (2.29 [1.19-4.39], CE = weak), and psoriasis (1.83 [1.18-2.83] CE = weak). Our study underscores the imperative for an integrated treatment approach to schizophrenia, highlighting its broad impact across respiratory, cardiovascular, sexual, neurological, and dermatological health domains. Given the predominantly non-significant to weak evidence levels, further studies are needed to reinforce our understanding.

Pharmacotherapy for patients with calcium oxalate stones and abnormal urine chemistry: A systematic review and meta-analysis for the Japanese Clinical Practice Guidelines for the Management of Urinary Stones, Third Edition.

We performed a systematic review and meta-analysis to evaluate the benefits and harms of pharmacotherapies for patients with calcium oxalate stones and abnormal urine chemistry. This article is a modified and detailed version of the commentary on Clinical Question 10 described in the Japanese Clinical Practice Guidelines for the Management of Urinary Stones, Third Edition. PubMed and Ichushi Web were searched through August 2020 for articles on pharmacotherapies for calcium oxalate stones (thiazides, citrate preparations, uric acid production inhibitors, and magnesium preparations). Two reviewers independently selected randomized controlled trials reporting reduction of stone recurrence and adverse drug reactions as outcomes and performed data extraction and quality assessment. Meta-analyses with random effects models and rating of the strength of evidence were performed. Pharmacotherapies were shown to significantly reduce stone recurrence (risk ratio 0.47, 95% confidence interval 0.35-0.63). Meanwhile, the pharmacotherapies increased adverse drug reactions leading to study dropout (risk ratio 2.51, 95% confidence interval 1.09-5.75) and adverse drug reactions/adverse events (risk ratio 1.95, 95% confidence interval 1.07-3.56). The reported adverse drug reactions were, however, mainly minor and did not frequently require discontinuation of medication (2%-16%). The strengths of evidence for both outcomes were rated as moderate, because the risk of bias, indirectness, inconsistency, imprecision, and publication bias were all serious except for one item. The overall strength of evidence across outcomes was therefore determined to be moderate. These results support the conditional recommendation to initiate pharmacotherapies for patients with calcium oxalate stones and abnormal urine chemistry.

Data Quality-Aware Client Selection in Heterogeneous Federated Learning

Federated Learning (FL) enables decentralized data utilization while maintaining edge user privacy, but it faces challenges due to statistical heterogeneity. Existing approaches address client drift and data heterogeneity issues. However, real-world settings often involve low-quality data with noisy features, such as covariate drift or adversarial samples, which are usually ignored. Noisy samples significantly impact the global model’s accuracy and convergence rate. Assessing data quality and selectively aggregating updates from high-quality clients is crucial, but dynamically perceiving data quality without additional computations or data exchanges is challenging. In this paper, we introduce the FedDQA (Federated learning via Data Quality-Aware) (FedDQA) framework. We discover increased data noise leads to slower loss reduction during local model training. We propose a loss sharpness-based Data-Quality-Awareness (DQA) metric to differentiate between high-quality and low-quality data. Based on the DQA, we design a client selection algorithm that strategically selects participant clients to reduce the negative impact of noisy clients. Experiment results indicate that FedDQA significantly outperforms the baselines. Notably, it achieves up to a 4% increase in global model accuracy and demonstrates faster convergence rates.

From data silos to seamless integration and coordination: a data-asset centric approach to smart hospital facility management

PurposeExisting hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.Design/methodology/approachThis study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.FindingsThe capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.Originality/valueThe initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

Anti-tissue transglutaminase antibody correlation with severity of villous abnormalities in celiac disease: A Systematic Review and Meta-analysis

Abstract Introduction/Objective Celiac disease (CD) is a long-term autoimmune disorder that affects individuals genetically predisposed to gluten intolerance. Biomarkers, particularly anti-tissue transglutaminase antibody (anti-tTG ab), are crucial in indicating disease states and have potential as non-invasive substitutes for mucosal biopsies in assessing the severity of intestinal damage. This study aims to systematically review and analyze the correlation between anti- tTG antibody levels and the severity of villous abnormalities in patients with celiac disease to evaluate the potential of anti-tTG as a non-invasive diagnostic tool. Methods/Case Report Following PRISMA guidelines, a comprehensive literature search selected 15 studies focusing on the correlation between anti-tTG antibodies and villous atrophy. Systematic data extraction and quality assessments were performed, followed by statistical analyses to determine the strength and consistency of associations. Results (if a Case Study enter NA) Analysis of data from four studies on anti-tTG antibody positivity revealed a nearly significant pooled effect size (p = 0.06) with considerable heterogeneity (I^2 = 97%, p < 0.00001). Additionally, data from five studies using the MARSH classification of histological damage, aggregated using a random-effects model, showed overall statistical significance (Z = 3.36, p = 0.0008). Conclusion Anti-tTG antibodies serve as a reliable indicator of villous damage in CD, supporting their dual utility in diagnosis and monitoring. High anti-tTG antibody titers could potentially eliminate the need for invasive biopsy procedures in certain patient subsets, underscoring the promise of more refined and non-invasive diagnostic approaches, although further research is needed to address the implications of high heterogeneity observed in the studies.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

Enhancing Clinical Data Quality to Improve Patient Care in Rural Health Facilities

WHO recognizes that high-quality research is not just a scientific pursuit but a crucial factor in achieving global health, health equity, and economic development. It is not just about the numbers but about the lives we can save and improve. In this context, four research priority areas have been identified: prioritizing health needs, building research capacity, ensuring good research practice, and ensuring good evidence is translated into practice. Local health research tailored to local needs remains an important global health goal, with the potential to revolutionize patient care in rural health facilities. Researchers and policymakers in low- and middle-income countries have expressed serious concerns about the limited access to high-quality data, a fundamental requirement for reliable and valid research. The assessment of data quality is typically categorized into five key areas: availability, usability, dependability, relevance, and presentation quality. This concept is further clarified by demonstrating how a robust healthcare data environment can enhance patient management through research efforts. The term ‘health data environment' encompasses the entire process of gathering, storing, managing, analysing, and utilizing health-related data to improve patient care and healthcare policies through informed decision-making. In this context, we present the challenges that undermine the quality of research data and propose solutions. Specifically, we identify the lack of adequate human resources, reliance on paper-based records systems, and the high costs of internet connectivity as the primary barriers to collecting and sharing high-quality data for research in low-resource settings. To tackle these challenges, we advocate for governments and funding institutions to invest in data management and communication systems, recruit and train data management staff to support clinicians, and reduce connectivity costs. Furthermore, we propose the deployment of a decentralized system as a cost-effective and less labor-intensive solution that only requires data entry staff at health facilities in resource-limited settings. This article explores current data collection, storage, and interpretation challenges and proposes innovative improvement solutions. It emphasizes the transformative potential of digitization, standardization, and staff training to enhance data quality. It further addresses the impact of quality data on evidence-based practice, ultimately leading to improved health outcomes. Focusing on rural healthcare facilities sheds light on the unique barriers these settings face, proposing tailored strategies that can bridge the gap between urban and rural health service delivery. These insights offer valuable guidance for policymakers, healthcare administrators, and practitioners striving to improve patient care in rural areas through data-driven strategies.

Comparative Efficacy of Self-ligating Bracket Systems versus Conventional Bracket Systems in Orthodontic Treatment: A Systematic Review

Abstract This systematic review aimed to compare the efficacy of self-ligating bracket (SLB) systems with conventional bracket systems in orthodontic treatment. A comprehensive search of electronic databases was conducted to identify relevant studies published up to 11. Inclusion criteria encompassed comparative studies evaluating orthodontic treatment outcomes between SLB and conventional bracket systems. Data extraction and quality assessment were performed independently by two reviewers. Key outcome measures included treatment duration, complications, patient-reported outcomes and clinical effectiveness. A total of 11 studies met the inclusion criteria and were included in the systematic review. Findings indicated comparable treatment durations between SLB and conventional bracket systems, with significant differences in complication rates. Patient-reported outcomes suggested more levels of comfort and satisfaction with SLB systems. In addition, clinical effectiveness measures, such as tooth alignment and occlusal outcomes, showed significant discrepancies in favour of SLB systems. However, there was variability amongst studies regarding specific clinical parameters assessed. Based on the findings of this systematic review, SLB systems demonstrate comparable efficacy to conventional bracket systems in orthodontic treatment. While both SLB systems exhibit lesser treatment durations, complication rates and patient-reported outcomes, further research is warranted to elucidate potential differences in specific clinical parameters and long-term treatment outcomes.

5185 A Meta-Analysis Evaluating the Efficacy of Liraglutide in Reducing Cardiovascular Complications Among Patients With Type 2 Diabetes

Abstract Disclosure: M.P. Del Rosario: None. C. Ong-Ramos: None. Background: The objective of this study was to determine the efficacy of Liraglutide in reducing cardiovascular complications Methodology: Literature searches of electronic databases (PubMed and ScienceDirect) were performed to identify relevant studies. Relevant journals included were electronically searched for randomized controlled trials (RCTs) regarding the use of Liraglutide versus placebo in reducing the rate of cardiovascular complications. Data extraction and quality assessment were done from independent reviewers. Statistical analysis of the study was accomplished using Cochrane Systematic Review Software Review Manager. A p-value of less than 0.05 for the observed effect size was considered statistically significant. Results: A total of 2 RCTs with 16, 977 patients were included in this meta-analysis. The results showed that in the Liraglutide group there is a lower rate of the following: Major Adverse Cardiovascular Events (MACE) (risk ratio of 0.86 [0.78, 0.95]), cardiovascular events (risk ratio 0.75 [0.63, 0.89]), and all-cause mortality (risk ratio 0.81 [0.70, 0.93]), but is not statistically significant when compared to the placebo group in terms of non-fatal myocardial infarction and and non-fatal stroke. There is no significant difference in rates of severe adverse events in general (p > 0.05). However, Liraglutide caused significant reduction in rates of severe hypoglycemia as an independent secondary outcome (p < 0.05). Conclusions: Compared with placebo, Liraglutide provided significant reduction in MACE, cardiovascular events, all-cause mortality and severe hypoglycemia as an adverse event. Presentation: 6/3/2024