Reliable Risk Research Articles

Tracking Workplace Violence over 20 Years

Introduction. Violence against healthcare workers (HCWs) is a widespread, underreported, and inadequately prevented problem. Only a few companies have efficient systems for assessing the extent of the phenomenon. Methods. In 2005, the health surveillance service of a public health company introduced a system that monitored violence experienced by HCWs by means of three items from the Violent Incident Form (VIF) integrated with departmental in-depth analyses using the participatory ergonomics group technique. Results. In 2005, the annual rate of physical assaults was 8.2%, that of threats was 12.0%, and the harassment rate was 19.6%. Over the past twenty years of observation (2005–2024), the percentage of workers who reported experiencing a physical attack in the previous year at their periodic medical examination has fluctuated between 5.8% and 11.1%, except for the years 2020 and 2021 when, during the COVID-19 pandemic, the rate was 3.9% and 3.2%, respectively. During the same pandemic period, the annual threat rate, which ranged from 9.4% to 20.1%, dropped to 7.7%, while the prevalence of harassment, which was between 13.5 and 19.6, fell to 7.2%. HCWs believe that (i) limiting visitor access, (ii) a better balance of the demand for services, and (iii) a better attitude towards HCWs were the causes of the reduced rate of violence during the pandemic. Conclusions. Recording the violence experienced during health surveillance is an economical, reliable, and sustainable risk assessment method.

Validation of fat mass metrics in pediatric obesity

Introduction Hudda-Index is a prediction model for fat mass (FM) based on simple anthropometric measures., FM is a crucial factor in the development of comorbidities, i.e., type 2 diabetes. Hence, Hudda-Index is a promising tool to facilitate identification of children at risk for metabolic comorbidities. It has been validated against deuterium dilution assessments, however, independent validation against the gold-standard for body composition analysis, magnetic resonance imaging (MRI), is lacking. The aim of this study is to validate FM calculated by Hudda-Index against FM measured by MRI. The secondary aim is to compare Hudda-Index to other anthropometric measures including body mass index (BMI), BMI-standard deviation score (BMI-SDS), waist/hip-ratio, waist circumference (WC) and skinfold thickness. Methods The study cohort consists of 115 individuals between the age of 9 and 15 years, recruited at Paracelsus Medical University Hospital in Salzburg (Austria) and Uppsala University Children's Hospital (Sweden). Anthropometry, blood samples, and oral glucose tolerance tests followed standard procedures. MRI examinations were performed to determine visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Results BMI and WC showed slightly stronger associations with the reference standard VAT (r=0.72 and 0.70, p<0.01, respectively) than Hudda-Index (r= 0.67, p<0.01). There is an almost perfect linear association between BMI and Hudda-Index. Accordingly, BMI and Hudda-Index both showed an acceptable association with cardiometabolic parameters. VAT was strongly associated with markers of liver status (LFF r=0.59, p<0.01) and insulin resistance (HOMA-IR r=0.71, p<0.01) and predicted metabolic dysfunction-associated steatotic liver disease (MASLD). Conclusion BMI, although an imperfect measure, remains the most reliable tool and estimates cardiometabolic risk more reliably than other anthropometry-based measures.

Machine learning for prediction of all-cause mortality in acute coronary syndrome

Abstract Background Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the Global Registry of Acute Coronary Events (GRACE) and the Thrombolysis In Myocardial Infarction (TIMI) risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS. Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality. Results Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p<0.05). Three models undertook external validation, and calibration was inconsistently reported. Conclusion ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing models and the need for greater validation.

Potential use of pharmacogenetics in cardiology: genome-wide association studies of amiodarone-induced thyroid disorders

Abstract Background Amiodarone is a commonly prescribed antiarrhythmic drug used to manage supraventricular and ventricular arrhythmias. The use of amiodarone is associated with a broad range of adverse effects, including amiodarone-induced hypothyroidism (AIH) or amiodarone-induced thyrotoxicosis (AIT) (1). Both conditions are linked with increased mortality and have no known reliable risk predictors (2). Purpose We investigated the genetic underpinnings of amiodarone-induced thyroid disorders, and the clinical validity and utility of screening for genetic variants. Methods We conducted the first genome-wide association studies (GWAS) of AIH and AIT using data from three large biobanks. We compared the AUCs for SNPs identified through the above GWAS with polygenic risk scores for both hypo- and hyperthyroidism. Finally, we tested the clinical validity and utility of screening for risk variants. Results We found two risk loci for AIH. The lead variant at the first locus, rs1443438, was intronic to FOXE1 (OR 0.39, MAF = 35%, P = 1.59 × 10-42). The other lead variant, rs2209796, was located 93 kb upstream to FOXA2 (OR 0.56, MAF = 29%, P = 2.64 × 10-15: Figure 1). For AIT, we found one risk locus. The lead variant, rs2268803, was intronic to CAPZB (OR 1.81, MAF = 42%, P = 2.43 × 10-8: Figure 1). Next, we added GWAS SNPs or polygenic risk scores to risk prediction models consisting of age, sex and 4 principal components (PCs), and compared AUC estimates between risk models. Using GWAS SNPs, we found that the AIT variant increased the AUC with 6% (95%CI 1.1 – 10.3, AUC: 0.72) and 9% (95%CI 6.2 – 11.2, AUC: 0.75) for AIH, compared to the benchmark model. Both GWAS SNP models were superior to polygenic risk scores in terms of AUC (P for difference <0.05). The positive and negative predictive value (PPV and NPV) for the AIT-variant was 14.6% and 95.7%, respectively. For the AIH-variants combined, we found a PPV of 17% and a NPV of 95%. Conclusions Common genetic variants are associated with both amiodarone-induced hypo- and hyperthyroidism and are at this point, superior to polygenic risk scores in predicting amiodarone-induced thyroid disorders. These findings add to the potentials of pharmacogenetics in cardiology. Figure 1. Double Manhattan plot of amiodarone induced thyroid disorders. The Y-axis is the negative log10 of the P-value for each single nucleotide polymorphism. The X-axis is the chromosome, with positions on each chromosome ranging from lower to higher (left to right). The dotted line marks the threshold for genome-wide significance. Amiodoarone-induced hypothyroidism at the top, and amiodarone-induced thyrotoxicosis at the bottom.Figure 1

Analysis of risk factors for the development of major adverse cardiovascular and cerebrovascular events in patients after aortic valve replacement with myocardial revascularization

Background. Acquired aortic valve disease and coronary heart disease often occur together, and almost two thirds of patients with aortic valve stenosis have severe coronary heart disease. Given the presence of both pathologies, this cohort of patients requires an individual diagnostic and therapeutic approach. The aim of the study was to analyze risk factors for the development of major adverse cardiovascular and cerebrovascular events (MACCE) in patients after aortic valve replacement with myocardial revascularization. Materials and methods. For this study, a selection of disease histories of adult patients (over 18 years old) who underwent surgical aortic valve replacement (SAVR) together with myocardial revascularization at the State Institution “Heart Institute of the Ministry of Health of Ukraine” from 2018 to 2022 was carried out. Results. We selected 130 case histories of patients who underwent SAVR together with myocardial revascularization: the first group (n = 51) — a combination of SAVR and percutaneous coronary interventions; the second group (n = 79) — a combination of SAVR and coronary artery bypass grafting. The analysis of intraoperative data showed that the duration of cardiopulmonary bypass and aortic cross-clamping in patients of the first group was significantly lower, by 1.5 times (p = 0.0001) and 1.4 times (p = 0.0001), compared to the second group. There was no significant difference between the study groups regarding the frequency of MACCE (p = 0.589). Reliable risk factors for the development of MAССE in the early postoperative period were the evaluation of the operative risk according to the EuroSCORE II scale (odds ratio (OR) 2.13, confidence interval (CI) 1.54–4.48, p = 0.012), the history of myocardial infarction (OR 1.43, CI 1.13–2.45, p = 0.002) and right coronary artery damage (OR 1.79, CI 1.44–3.78, p = 0.021). Conclusions. Patients who underwent percutaneous coronary interventions before SAVR were characterized by a shorter duration of cardiopulmonary bypass and aortic cross-clamping, however, no significant difference in major early postoperative complications was observed between the study groups. In particular, there were no significant differences regarding the frequency of MACCE in the early postoperative period.

The Use of a Single Risk Assessment Tool for Mortality and Numerous Hospital-Acquired Conditions.

Quality assessment organizations leverage numerous patient safety measures to evaluate hospital performance, resulting in significant financial, administrative, and operational burdens on health systems. Low-intensity approaches that allow for reliable risk stratification of patients can reduce the required investment. The Braden score is a routinely performed bedside nursing evaluation validated to assess risk for hospital-acquired pressure injury. We hypothesized that the tool can be used to evaluate risk for other hospital-related adverse outcomes, including mortality, catheter-associated urinary tract infection (CAUTI), and central line-associated blood stream infection (CLABSI). We found that abnormal Braden scores have significant association with numerous adverse outcome measures, including mortality, CLABSI, CAUTI, and iatrogenic hypoglycemia. Because of its frequency of reevaluation, we have found preliminary evidence that leveraging this tool can reduce harm by quickly identifying the most at-risk patients for various types of iatrogenic harm. We conclude that in the face of increasing automation and technical applications, for example, artificial intelligence-driven tools, highly reliable clinician bedside physical examination and evaluation can still have significant, low-cost, and high-value impact in improving patient safety.

Optimising nanoparticles: harnessing quality by design through 3-level factorial design and box-behnken methodology

Nanoparticles (NPs) have become essential elements in a number of scientific and industrial domains, such as materials research, drug delivery, and diagnostics, because of their special qualities and potential for focused uses. An ideal formulation procedure is essential to maximising the effectiveness and efficiency of nanoparticles. In order to optimise the formulation and production processes for nanoparticles, this research investigates the application of Quality by Design (QbD) principles in conjunction with sophisticated statistical approaches, namely the 3-level factorial design and the Box-Behnken methodology. The Quality by Design methodology is a methodical approach that prioritises predetermined goals, in-depth process comprehension, and careful control grounded in reliable science and quality risk management. When QbD is incorporated into nanoparticle optimisation, reliable and repeatable formulations that adhere to specifications with little variation are guaranteed. To look into how different factors interact and affect the properties of nanoparticles, the 3-level factorial design is used. With this design approach, a thorough understanding of the process variables can be obtained by exploring a broad variety of component values. Critical quality attributes (CQAs) include important parameters such drug encapsulation efficiency, zeta potential, polydispersity index (PDI), and particle size. These parameters are methodically examined to determine the ideal amounts of each. To further refine the 3-level factorial design, the Box-Behnken methodology is applied. Without the requirement for extensive combination testing, this response surface methodology (RSM) is especially useful for building second-order polynomial models and investigating quadratic response surfaces. By enabling a more accurate comprehension of the interactions between inputs and responses, the Box-Behnken design improves the optimisation process and makes it easier to identify the ideal circumstances for nanoparticle synthesis. By utilising both approaches in tandem, this work methodically determines ideal formulation parameters that minimise nanoparticle size and polydispersity while optimising stability and drug loading efficiency. The outcomes highlight how important a QbD framework is for directing the creation of reliable nanoparticle systems that function consistently and predictably.

Antenatal Risk of Coarctation for Newborns at Hartford Hospital (ARCH) Pathway: A Predictor of Postnatal Management Strategy.

The diagnosis of coarctation of the aorta (CoA) prior to birth can be challenging due to the physiologic changes during postnatal transition. Prenatal risk stratification can standardize postnatal management and improve outcome. CT Children's Fetal Cardiology created the Antenatal Risk of CoA in Hartford (ARCH) clinical pathway defining four distinct postnatal order sets based on degree of suspicion for ductal dependency on fetal evaluation: low, low-moderate, moderate-high, and high risk. This study aims to evaluate safety and efficacy of the ARCH pathway in neonates with suspected CoA. This study was a single-center, retrospective chart review evaluating maternal-infant dyads with findings concerning for CoA between July 2004 and July 2021, before and after ARCH pathway implementation. Neonates were evaluated for the presence or absence of critical CoA and postnatal clinical data were collected. Statistical analysis was performed using chi square and Fisher's exact test. There were 108 maternal-infant dyads studied, comprising 53 non-pathway patients and 55 ARCH pathway participants. Thirty-three neonates had critical CoA, comprising 23 non-pathway and 10 ARCH pathway subjects.Patients categorized in the high-risk group were highly associated with critical CoA (P = 0.003). Non-pathway neonates with CoA demonstrated higher likelihood of hospital transfer compared to ARCH pathway neonates (56.5% vs. 10.0%, P = 0.021).NICU admission, prostaglandin administration, and intubation were not significantly different between before and after ARCH implementation (P < 0.05). More echocardiograms were performed in ARCH pathway neonates without CoA than their non-pathway counterparts (1.586 vs. 2.133, P = 0.049). The ARCH pathway is a safe, reliable prenatal risk stratification system to help guide management of patients with critical CoA. These results identify effective targets of modification to the pathway to reduce resource utilization without compromising safety.

Plastic debris exposure and effects in rivers: Boundaries for efficient ecological risk assessment.

Until recently, plastic pollution research was focused on the marine environments, and attention was given to terrestrial and freshwater environments latter. This discussion paper aims to put forward crucial questions on issues that limit our ability to conduct reliable plastic ecological risk assessments in rivers. Previous studies highlighted the widespread presence of plastics in rivers, but the sources and levels of exposure remained matters of debate. Field measurements have been carried out on the concentration and composition of plastics in rivers, but greater homogeneity in the choice of plastic sizes, particularly for microplastics by following the recent ISO international standard nomenclature, is needed for better comparison between studies. The development of additional relevant sampling strategies that are suited to the specific characteristics of riverine environments is also needed. Similarly, we encourage the systematic real-time monitoring of environmental conditions (e.g., topology of the sampling section of the river, hydrology, volumetric flux and velocity, suspended matters concentration) to better understand the origin of variability in plastic concentrations in rivers. Furthermore, ingestion of microplastics by freshwater organisms has been demonstrated under laboratory conditions, but the long-term effects of continuous microplastic exposure in organisms are less well understood. This discussion paper encourages an integrative view of the issues involved in assessing plastic exposure and its effects on biota, in order to improve our ability to carry out relevant ecological risk assessments in river environments.

Tabular deep learning: a comparative study applied to multi-task genome-wide prediction

PurposeMore accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets.MethodsThe study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods.ResultsExtensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency.ConclusionThrough series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Engineering ergonomic risk assessment of karate choku zuki straight punch

This study utilizes the Rapid Upper Limb Assessment (RULA) technique to evaluate suggested improvements aimed at enhancing the safety and efficacy of the Choku Zuki (Straight Punch), a high-risk karate exercise. The primary objective is to identify and address potential musculoskeletal issues associated with this technique. Given the prevalence of musculoskeletal injuries in sports, particularly in activities with high intensity like karate, the need for reliable ergonomic risk assessment tools such as RULA is paramount. By applying RULA to analyze the ergonomic aspects of the Choku Zuki punch, this study provides crucial insights into potential areas of concern. It identifies specific postures and movements that may predispose practitioners to injury, allowing for the development of targeted interventions to mitigate risks and enhance safety. Furthermore, this research underscores the importance of integrating ergonomic considerations into sports training regimens to optimize performance and minimize injury risks. Beyond its implications for karate training, the study emphasizes the broader significance of ergonomic assessments in sports science and engineering. It showcases how engineering methodologies can be leveraged to improve the safety, efficiency, and overall performance of sporting activities, fostering collaboration between sports scientists, engineers, athletes, coaches, and healthcare professionals to advance sports performance and safety standards.lities.

HIV deathrate prediction by Gaidai multivariate risks assessment method.

HIV is a contagious disease with reportedly high transmissibility, being spread worldwide, with certain mortality, allegedly presenting a burden to public health worldwide. The main objective of this study was to determine excessive HIV death risks at any time within any region or country of interest. Current study presents a novel multivariate public health system bio-risk assessment approach that is particularly applicable to environmental multi-regional, biological, and public health systems, being observed over a representative period of time, yielding reliable long-term HIV deathrate assessment. Hence, the development of a new bio-statistical approach, that is, population-based, multicenter, and medical survey-based. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. Firstly, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case, - not to mention challenges with system dimensionality higher than 2D. Existing bio-statistical methods that process spatiotemporal clinical observations of multinational bio-processes often do not have the advantage of efficiently dealing with high regional dimensionalities and complex nonlinear inter-correlations between different national raw datasets. Hence, this study advocates the direct application of the novel bio-statistical Gaidai method to a raw unfiltered clinical data set. This investigation described the successful application of a novel bio-risk assessment approach, yielding reliable long-term HIV mortality risk assessments. The suggested risk assessment methodology may be utilized in various public bio and public health clinical applications based on available raw patient survey datasets.

Probabilistic seismic risk analysis of electrical substations considering equipment-to-equipment seismic failure correlations

When an earthquake occurs, electrical equipment in a substation exhibits a certain level of seismic failure correlation since they suffer similar ground motions and share similar structural characteristics. However, this equipment-to-equipment seismic failure correlation (E2ESFC) was neglected in previous substation-level probabilistic seismic risk analyses due to the lack of awareness and practical approach. To investigate the effect of different degrees of the E2ESFC on the substation seismic risk, an efficient method for considering partially correlated seismic failure was proposed. The concepts of “damage demand probability” and “damage capacity probability” were derived from the equipment's fragility curve. Then the partial correlation of equipment's capacity probabilities can be easily introduced and incorporated into the substation-level risk analysis through the combination of Copula functions and the Monte Carlo simulation. A case study on a real-world 220/110 kV substation using an equi-correlation model demonstrated that ignoring the E2ESFC among the same type of equipment will lead to an underestimate of the probability of seeing high seismic loss. Furthermore, a general method to assess the E2ESFC coefficients between equipment was also proposed, laying the foundation to facilitate applications of the introduced E2ESFC simulation method and to generate a more reliable system risk assessment result.

Ergonomic Risk Factors and Musculoskeletal System Problems In Healthcare Professionals Working In Central Sterilization Unit

This study was conducted to determine ergonomic risk factors and musculoskeletal system problems in healthcare professionals working in the central sterilization unit. The study is of descriptive cross-sectional type. The sample of the study consisted of 87 health professionals working in the central sterilization unit of seven hospitals of a private health group in Istanbul. Data were collected by face-to-face interview method using the sociodemographic characteristics form, the validated and reliable Ergonomic Risk Factors and Musculoskeletal Disorders Scale (ERKISO) and the Cornell Musculoskeletal Disorder Scale (T-CMDQ) between February and September 2020. Factor analysis, descriptive statistics, Mann Whitney U test, Kruskal-Wallis H test, post-Hoc analysis test and correlation test were used in the evaluation of the data. The level of significance was accepted as p

Intrarater and Interrater Reliability of Quantitative Ultrasound Speed of Sound by Trained Raters at the Distal Radius in Postmenopausal Women.

Quantitative ultrasound (QUS) performed at the distal radius shows potential as an effective tool for evaluating fracture risk, especially in postmenopausal women, but the reliability of this tool has not been reported in this patient population. This study investigated the within-day intrarater and interrater reliability of trained raters performing QUS measures at the distal radius of postmenopausal community-dwelling women. Eighteen women between the ages of 56 and 87years were recruited from senior centers in the Greenville area of South Carolina and consented to participate. A QUS bone sonometer was used in this study; the trained raters performed 3 repeated speed of sound (SOS) measures at the nondominant radius of each participant on the same day. Intraclass correlation coefficients (ICCs) were calculated (model 3,1 for intrarater and 2,3 for interrater reliability) to determine the reliability within and between raters. In addition, we calculated the standard error of measurement (SEM) and the minimal detectable change at 95% confidence interval (MDC 95 ). The within-day intrarater reliability was excellent for both physical therapist raters (ICC (3,1) ranging from 0.93 to 0.91). The SEM and MDC for the within-day intrarater reliability measures were small for both raters (SEM=36.79-38.75 or 0.92%-0.97% of the pooled mean; MDC 95 =101.99-107.40). The interrater reliability for SOS measurements using radial QUS was good with an ICC (2,3) of 0.77 (95% CI, 0.36-0.92) for 3 averaged measures. The SEM and MDC were larger for the interrater reliability measures (SEM=62.79 or 1.57% of the pooled mean; MDC 95 =174.05). Quantitative ultrasound SOS measurement at the distal radius showed excellent within-day intrarater and good interrater (when 3 measures were averaged) reliability in community-dwelling postmenopausal women. The SEM and MDC 95 were also low for interrater reliability when a mean of 3 trials was used. This study is the first to demonstrate that this particular QUS device at the radius may be a reliable fracture risk screening tool for postmenopausal women.

Chronic Atrophic Gastritis and Intestinal Metaplasia: A Latin American Perspective

Gastric cancer (GC), a significant cause of mortality globally, is the leading cause of cancer-related deaths among Latin American men. GC is usually diagnosed at an advanced stage; therefore, therapeutic options are limited, and prognosis is poor. &lt;i&gt;Helicobacter pylori&lt;/i&gt; infection remains the primary risk factor for GC; therefore, primary prevention directed toward diagnosis and treatment (“test-and-treat” strategy) is important. Western medicine guidelines recommend esophagogastroduodenoscopy (EGD) for at-risk individuals aged &gt;40 years with regular surveillance in patients with gastric premalignant conditions (GPMC). However, limited availability of EGD in Latin America necessitates development of risk stratification tools to minimize the endoscopic burden. Results from the Chilean “Endoscopic Cohort and Histological Operative Link on Gastric Assessment (OLGA) Staging” (ECHOS study), propose endoscopic surveillance of advanced GPMC (OLGA/Operative Link for Gastric Intestinal Metaplasia [OLGIM] stages III–IV) with reliable risk stratification to facilitate early GC detection. Ensuring high-quality EGD and enhanced diagnostic yield of GPMC is essential. GPMC grading tools, such as the Kimura-Takemoto or Endoscopic Grading of Gastric Intestinal Metaplasia classification, should be incorporated into the regular risk assessment protocol. However, obtaining mapping gastric biopsies using standardized methods such as the updated Sydney System biopsy protocol, followed by grading of chronic atrophic gastritis with or without intestinal metaplasia using the OLGA and OLGIM staging systems are preferred for GC risk stratification. Recent GC prevention strategies recommended in Chile include a “test-and-treat” approach for &lt;i&gt;H. pylori&lt;/i&gt; in individuals aged 35–44 years and combined &lt;i&gt;H. pylori&lt;/i&gt;/pepsinogen I–II serology and EGD evaluation in patients aged &gt;45 years to optimize the limited preventive resources available in the region.

National risk assessment of Italian school buildings: The MARS project experience

Strategic buildings, such as schools, play a crucial role in civil society. They are essential not only for the well-being of communities but also for their significance during the emergency and recovery phases following an earthquake. Reliable risk assessment is a powerful tool for implementing effective mitigation strategies. While considerable work has been already done in the literature to develop fragility curves and risk studies for residential buildings, there remains a noticeable gap in addressing risk assessment for school portfolios. As part of the 2019–2021 MARS (Seismic Risk MAps) project, funded by the Italian Civil Protection Department and involving the ReLUIS consortium along with the EUCENTRE foundation, a specific task was dedicated to evaluating seismic risk for the Italian school buildings. In this context, eight research units combined their expertise to develop a consensus-based vulnerability model, known as the “School-MARS model”. This model integrates fragility curves based on various approaches (i.e. empirical, mechanical-analytical, mechanical-numerical and hybrid) and is tailored to archetypes representative of the Italian school buildings. The main goal of the paper is outlining the methodology used to define the “School-MARS vulnerability model”. Moreover, the paper demonstrates the application of the latter for performing intensity-based and time-based risk analyses. Specifically, the results are presented in terms of damage distribution and usability ratings expected for the entire stock of Italian school building. Notably, these results indicate that approximately 26 % of Reinforced Concrete (RC) buildings and 31 % of Unreinforced Masonry (URM) structures may become unusable due to potential earthquakes expected in the next 50 years.

The threat from ozone to vegetation in Ireland

Ozone is the most damaging air pollutant to vegetation globally. Metrics of accumulated ozone above a concentration threshold (e.g. AOT40, ppb·h) have been widely used to assess ozone risk. However, there is growing consensus that accumulated Phytotoxic Ozone Dose (POD) above a receptor-specific critical stomatal flux threshold (y; nmol O3 m−2 s−1), expressed per unit of projected leaf area, provides a more reliable risk assessment, as it considers ozone entering the leaf (PODy, mmol m−2 leaf area). Few studies have assessed both concentration- and flux-based metrics using site-specific observations of ozone and meteorology. In this study we assessed the risk that ozone poses to five vegetation types across eight sites in Ireland during 2005–2021, using AOT40 and PODy risk metrics, and we predicted impacts using dose–response relationships. Long-term trends in both metrics were also assessed. The PODy critical level for vegetation protection was exceeded for all vegetation types, with exceedances most common at Atlantic coastal sites, and for tree species (beech POD1 15.7–25.7 mmol/m2 PLA). When PODy and AOT40 results were normalised based on their respective critical levels, predicted impacts were higher for PODy. There were significant increases in PODy for three vegetation types at rural sites during the study period, which also experienced increases in temperature and global solar radiation. The long-term trends were consistent with other European studies that show decreases in AOT40 and increases in PODy. While ozone concentrations in Ireland are relatively low (39–75 μg/m3 five-year average range), the humid climate and longer growing season may lead to elevated stomatal ozone uptake and thereby a risk to vegetation.

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

NELA 1 - Actual and predicted 30-day mortality following emergency laparotomy using National Emergency Laparotomy Audit (NELA) and American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) risk prediction tools

Abstract Background Emergency laparotomy (EL) is common, with &amp;gt;30,000 performed annually in the UK. EL has a high 30-day mortality of 9.2%, especially in the elderly. Reliable preoperative risk assessment is essential to aid decision-making and perioperative care arrangements. NELA and ACS-NSQIP risk calculators can both be used to assess risk, and both can under or over-predict mortality. Methods This is a multi-center, retrospective, observational, cohort study investigating the correlation between actual 30-dm (a-30-dm) and predicted 30-dm (p-30-dm) following EL using NELA and ACS-NSQIP in adults over and under 70, over 2-years, in two UK centres. Sample size was calculated at 385 cases. Statistical analysis was carried out using GraphPad Prism 10.1.1. Results 626 cases were analyzed. A-30-dm, NELA, and ACS-NSQIP p-30-dm in the under 70s subgroup were 5.78%, 5.53%, and 3.41% respectively, with NELA and ACS-NSQIP correlating positively with a-30-dm: R²=0.12, p&amp;lt;0.0001 and R²=0.09, p&amp;lt;0.0001. In over 70s, a-30-dm was 13.96%, NELA p-30-dm 14.02% and ACS-NSQIP 9.76%, with a positive correlation between the latter two (R²=0.56, p&amp;lt;0.0001), but a weak one between a-30-dm and NELA (R²=0.04, p&amp;lt;0.0001) or ACS-NSQIP (R²=0.02, p&amp;lt;0.0001). For the entire cohort a-30-dm, NELA and ACS-NSQIP p-30-dm were 9.74%, 9.8%, and 6.59% respectively. NELA and ACS-NSQIP p-30-dm showed a strong positive correlation (R²=0.89; p&amp;lt;0.0001) with only NELA showing a strong positive correlation with a-30-dm (R²=0.31, p&amp;lt;0.0001); and not ACS-NSQIP (R²=0.05, p&amp;lt;0.0001). Conclusion NELA can accurately predict 30-day mortality in patients aged &amp;lt;70 undergoing EL but not &amp;gt;70. ACS-NSQIP and a-30-dm correlated in a very weak manner.