Actual Risk Research Articles

Artificial Nourishment Schemes along the Polish Coast and Lagoon Shores between 1980 and 2020, with a Particular Focus on the Hel Peninsula

This article reviews the literature covering the period from 1965 to 2020 dedicated to the issue of artificial beach nourishment along the Polish coast, with a particular focus on the Hel Peninsula. The primary sources used in this work include 34 reports from unpublished case studies and projects implemented by the Department of Maritime Hydrotechnics, Maritime Institute in Gdańsk, between 1971 and 2020. This paper also presents detailed information about the total fill volume in cubic meters of dredged material deposited along the Polish coast and lagoon shores in 1980–2020. During these 40 years, approximately 40.5 million m3 of sediment was deposited along the Polish coast and lagoon shores. Particular consideration was given to beach fills along the Hel Peninsula, which was at actual risk of breaking in its basal and central sections after intense storms at the turn of 1988 and 1989. The survey materials collected enabled the assessment of the coastal morphodynamics of the peninsula under the Coastal Protection Program through the prism of changes in the fill volume along the coastal sections, which were replenished with material coming from submarine deposits. The peninsula’s stability was also assessed, taking into account the ongoing climate change. Moreover, this article discusses the proposed rules and terms for protecting the Polish coast by way of artificial nourishment.

Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates

Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China’ second Clean Air Action (2018–2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.

Risk sharing rule and safety loading in a peer to peer cooperative insurance model

AbstractThe evolution of digital technologies is reshaping consumer habits and needs, driving process automation, and giving rise to innovative business models like Insurtech. Peer-to-peer (P2P) insurance is emerging as part of this trend. P2P involves purchasing an insurance policy by sharing the risk with a group of peers. This group transparently monitors real-time savings and tracks claims filed by its members. At the policy’s expiration, if the actual risk is lower than anticipated, the peers receive a partial refund of their premium. This paper introduces a model to determine the entry price in a broker-based P2P scheme using a cooperative game approach. We employ the Shapley Value method to distribute the risk among participants. Numerical examples are included for illustration and discussion.

Awareness Patterns Regarding Radiation Safety Management in Fields Related to Radiation Safety Regulations: Focusing on Companies that Must Report Radiation Sources

Background: This study aims to analyze radiation safety management and regulatory perceptions, focusing on companies that must report radiation sources. The intent is to reduce the gap between regulation measures and addressing real concerns while improving practical safety management measures and regulations for all stakeholders.Materials and Methods: Radiation safety officers at a total of 244 reporting companies using radiation generators (79.8%) and sealed radioisotopes (15.1%) were surveyed using a questionnaire.Results and Discussion: The perception that regulation is stronger than the actual risk of the radiation source used was 3.47 points (out of 5 points), indicating a score above average. The most important factors and considerations were education and training (48%) as a human factor, safety devices of the radiation source (71.3%) as a hazardous material factor, the use of radiation (50.8%) as an organizational environment, and the radiation effect of nearby facilities (67.2%) as a physical environment. Radiation safety management educational experience (<i>F</i>=5.030, <i>p</i><0.01), the group with high subjective knowledge (<i>t</i>=6.017, <i>p</i><0.001), and the group with high objective knowledge (<i>t</i>=1.989, <i>p</i><0.05) was found to be better at radiation safety management.Conclusion: It is necessary to standardize the educational experience regarding radiation safety management because each staff member has individual differences in educational experience. It is necessary to provide more information on how to solve radiation accidents via educational content. Applying radiation safety regulations based on the factors that significantly affect radiation safety management shown in this survey will help improve safety.

Influencing factors of traumatic arthritis after ankle fracture surgery and construction of risk prediction model

To explore risk factors of post-operative traumatic arthritis in patients with ankle fracture,and to establish risk prediction model. Totally 550 patients with ankle fracture treated from May 2020 to May 2022 were selected as research objects and divided into modeling group (385 patients) and verification group (165 patients) according to 7:3. In modeling group,patients were classified as occurrence group (112 patients) and non-occurrence group (273 patients) according to whether traumatic arthritis occurred after opertaion. Age,body mass index(BMI),gender,smoking history,diabetes history,injury type,fracture type,operation time,manual labor,open injury,osteoporosis,poor reduction,postoperative weight-bearing time,vascular injury,and surgical method were recorded; risk factors of traumatic arthritis in ankle fracture patients were analyzed by single factor and multi factor logistic regression analyses; R software was used to build the prediction model of line graph;receiver operating characteristic (ROC) curve and calibration graph were applied to verify the discrimination and consistency of the model. One hundred and twelve of 385 patients with ankle fracture were developed to post-operative traumatic arthritis,and 275 did not. Univariate analysis showed that there were significant differences in age,BMI,fracture type,operation time,physical labor aboveⅡ,open injury,osteoporosis and poor reduction between two groups (P<0.05). Multivariate Logistic regression analysis showed that age (OR=2.887),BMI (OR=4.042),fracture type (OR=4.244),operation time (OR=2.665),physical labor above gradeⅡ(OR=5.099),osteoporosis (OR=10.219),and poor reduction (OR=3.112) were independent risk factors for traumatic arthritis after ankle fracture (P<0.05). Based on the above risk factors,an nomogram model was established to predict the risk of postoperative traumatic arthritis in ankle fracture patients,and internal and external verification was conducted. The results showed calibration curve of modeling group and verification group showed a good fit between correction curve and ideal curve,indicating that the predicted risk of postoperative traumatic arthritis by the model was basically consistent with actual risk. Area runder ROC curve analysis results showed 0.867[(95%CI(0.826,0.908)] and 0.882 [95%CI(0.827,0.938)],respectively,indicating that the prediction model had good prediction ability. Age,BMI,fracture type,operation time,physical labor above gradeⅡ,osteoporosis and poor reduction are all risk factors for post-operative traumatic arthritis in patients with ankle fracture. The prediction model based on the above risk factors could effectively evaluate risk of post-operative traumatic arthritis in patients with ankle fracture.

Low Dose Radiation and Solid Tumors Mortality Risk.

US nuclear capable shipyard workers have increased potential for occupational radiation exposure. The aim of the study is to examine solid tumor mortality risks at low doses. 437,937 workers working from 1957 to 2004 at eight US shipyards were studied. Radiation workers with a median life-time dose at 0.82 mSv had a significantly lower solid tumor mortality risk (relative risk [RR]: 0.96, 95% confidence interval [CI]: 0.94-0.98) than nonradiation workers. Among 153,930 radiation workers, the RRs of solid tumors increased with increasing dose categories without statistical significance. The dose category >0-<25 mSv had significantly lower RR (0.95, 95% CI: 0.91-0.99) versus 0 dose and the excess relative risk was 0.05/100 mSv (95% CI: 0.01-0.08). Solid tumor risk might increase with radiation dose, but not linearly at low doses. Actual mortality risk may be dependent on dose received.

Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.

Abstract 2317: Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study

Abstract Background: Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and is a major cause of cancer-related mortality. Early diagnosis is critical to increase survival rates. While CRC screening has shown significant benefit, adherence remains low, and there is a need for better tools to identify high-risk patients. Risk prediction models were demonstrated to identify such patients. Aim: To establish an individualized risk prediction model for CRC diagnosis based on Electronic Health Records (EHR). Methods: This is a retrospective cohort study utilizing EHR data of Clalit Health Services (CHS) members aged 50-74 that were eligible for CRC screening, from January 2013 to January 2019. The model was trained to predict CRC diagnosis within two years using approximately 20,000 EHR features including socio-demographic information, laboratory results and medical history. Model performance, as a complementary screening method, was evaluated. Results: The study included 2679 subjects with CRC diagnosis and 1,133,713 subjects without CRC diagnosis. The model was trained on subjects from 2013-2017, and performance was validated on subjects from 2019 and a cohort of subjects that underwent fecal occult blood test (FOBT). Incidence values of CRC among subjects in the top 1% risk scores were higher than baseline (2.3% vs. 0.3%; lift 8.38; P-value &amp;lt;0.001). Characteristics of subjects by risk scores percentiles are presented in Table 1. Cumulative event probabilities increased with higher model scores, indicating a correlation between predicted and actual risk of CRC diagnosis. Model-based risk stratification among subjects with a positive FOBT, identified subjects with more than twice the risk for CRC compared to FOBT alone. Conclusions: We developed an individualized risk prediction model for CRC that can be utilized as a complementary decision support tool for healthcare providers to precisely identify patients at high risk for CRC and refer them to confirmatory testing. Citation Format: Samah Hayek. Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2317.

Self-Perception of Risk for HIV Acquisition and Calculated Risk for HIV Acquisition Among Active Duty Air Force Members With Newly Diagnosed HIV Infection.

There have been nearly 1,600 new diagnoses of Human Immunodeficiency Virus (HIV) across the US Armed Forces between 2017 and 2022. While treatment has improved overall survival, self-perception of acquiring HIV may not align with actual risk of acquiring HIV, thus slowing diagnosis and treatment. We aim to evaluate self-perceived risk (SPR) versus calculated risk (CR) of HIV infection in US Air Force (USAF) members with incident HIV diagnosis. All USAF members with new HIV diagnosis evaluated at a specialty care military medical center between January 2015 and March 2020 with case report forms were included (n = 142). SPR was compared to CR using the Denver HIV Risk Score (DHRS). The study was approved by the Army Public Health Center's Public Health Review Board (#14-311) and the Walter Reed Army Institute of Research Human Subjects Protection Branch (#1861E). Patients were predominantly male (98%), with a median age of 26 (IQR 22-30) years, and the majority (85%) reported same-sex partners. Most patients reported a low SPR (n = 78; 55%). A higher proportion of low SPR patients were married or partnered than high SPR patients (29% versus 14%; P = 0.04). Both groups had median DHRS scores in the highest risk category with similar results by reason for HIV screening. The majority of USAF members with incident HIV infection reported a low SPR despite risk factors and CRs identical to high SPR patients. In order to inform HIV prevention strategies in the military, further efforts are needed to educate the military population and providers about HIV risk perception.

Landslide Risks to Bridges in Valleys in North Carolina

This research delves into the intricate dynamics of landslides, emphasizing their consequences on transportation infrastructure, specifically highways and roadway bridges in North Carolina. Based on a prior investigation of bridges in Puerto Rico after Hurricane Maria, we found that bridges above water and situated in valleys can be exposed to both landslide and flooding risks. These bridges faced heightened vulnerability to combined landslides and flooding events due to their low depth on the water surface and the potential for raised flood heights due to upstream landslides. Leveraging a dataset spanning more than a century and inclusive of landslide and bridge information, we employed logistic regression (LR) and random forest (RF) models to predict landslide susceptibility in North Carolina. The study considered conditioning factors such as elevation, aspect, slope, rainfall, distance to faults, and distance to rivers, yielding LR and RF models with accuracy rates of 76.3% and 82.7%, respectively. To establish that a bridge’s location is at the bottom of a valley, data including landform, slope, and elevation difference near the bridge location were combined to delineate a bridge in a valley. The difference between bridge height and the lowest river elevation is established as an assumed flooding potential (AFP), which is then used to quantify the flooding risk. Compared to traditional flood risk values, the AFP, reported in elevation differences, is more straightforward and helps bridge engineers visualize the flood risk to a bridge. Specifically, a bridge (NCDOT ID: 740002) is found susceptible to both landslide (92%) and flooding (AFT of 6.61 m) risks and has been validated by field investigation, which is currently being retrofitted by North Carolina DOT with slope reinforcements (soil nailing and grouting). This paper is the first report evaluating the multi-hazard issue of bridges in valleys. The resulting high-fidelity risk map for North Carolina can help bridge engineers in proactive maintenance planning. Future endeavors will extend the analysis to incorporate actual flooding risk susceptibility analysis, thus enhancing our understanding of multi-hazard impacts and guiding resilient mitigation strategies for transportation infrastructure.

Prevalence of strabismus and risk factors in adults born preterm with and without retinopathy of prematurity: results from the Gutenberg Prematurity Eye study

AimThe purpose of this study was to assess the prevalence of strabismus and nystagmus and to analyse associated factors in preterm and full-term infants in adulthood.MethodsThe Gutenberg Prematurity Eye Study...

Ticks (Acari: Ixodidae) and rickettsiae associated with wild boars in a rural area of Minas Gerais, Brazil

Wild boars or feral pigs are classified by the Brazilian Institute for the Environment and Renewable Resources (IBAMA) in “Category I of invasive exotic species”. They cause economic losses, harm the environment, serve as hosts and reservoirs for several zoonotic disease agents, and provide a blood meal for tick species that act as vectors for zoonotic diseases. The objective of this study was to identify tick species on wild boars, assess host-seeking ticks in the related environment, and identify other potential tick hosts coexisting with wild boars on a farm located in the state of Minas Gerais, southeastern Brazil. Additionally, the study aimed to determine the presence of rickettsiae in these arthropods and assess the exposure of wild boars to rickettsiae species from the Spotted Fever Group and Rickettsia bellii through serology. A total of 3585 host-seeking ticks from three species (Amblyomma sculptum – 41.58%; Amblyomma dubitatum – 0.39% and Rhipicephalus microplus – 0.05%) were collected in the environment and A. sculptum was the most abundant species. Thirty-one wild boars were evaluated, resulting in the collection of 415 ticks, all of which were A. sculptum. Rickettsia DNA was not detected in samples of A. sculptum and R. microplus from the environment or in A. sculptum ticks from wild boars. However, all A. dubitatum ticks (n = 14) had Rickettsia bellii DNA confirmed by the species-specific PCR protocol. Out of the 31 serum samples from wild boars, 24 reacted with at least one Rickettsia antigen. Among these, seven individuals exhibited a reaction to a probable homologous antigen (PHA) of three rickettsiae species: R. rickettsii (n = 3), R. amblyommatis (n = 3) and R. rhipicephali (n = 1). Despite the high prevalence of seroreactivity, titers were low, indicating limited exposure to Rickettsia spp. Camera traps generated 874 animal records, capturing a total of 1688 individuals. At least 11 species of birds and 14 species of mammals (12 wild and two domestic) shared the environment with wild boars and potentially shared ticks with them. These findings provide baseline information for understanding the sharing of ticks and tick-borne pathogens between wild boars and other animals within the Cerrado biome. Further studies are necessary to monitor the potential and actual risk of wild boars to harbor infected ticks and their role in the transmission and maintenance cycle of Rickettsia spp.

Impaired perception of cardiovascular risk and health literacy in individuals with high cardiovascular diseases risk from different socioeconomic levels in Antalya, Turkey.

This study aimed to determine how the frequency of impaired perception of cardiovascular disease (CVD) risk is correlated with health literacy in individuals with high CVD risk in two regions with different socioeconomic levels. A descriptive study was conducted. The Systematic Coronary Risk Estimation, recommended by the European and Turkish Cardiology Societies, provides recommendations based on individuals' actual CVD risk levels. According to the Systematic Coronary Risk Estimation, participants suffering from diseases were identified as high and very high risk. The perception of risk was determined by subjectively assessing the risk level of the individuals. The European Health Literacy Survey Questionnaire was used. The Mann-Whitney U test, Chi-squared test and Spearman's correlation analysis were used to analyse the data. We also conducted a logistic regression analysis to identify factors contributing to impaired perception of CVD risk. A total of 384 individuals, aged between 40 and 70years (with a mean age of 58.35±8.33years), were included in the study. These individuals had a high risk of CVD and were registered to family health centres from two different socioeconomic levels. Out of the 384 participants, 201 individuals belonged to the lower socioeconomic level region and 183 individuals belonged to the higher socioeconomic level region. A total of 61.7% of the participants with high CVD risk had an impaired perception of CVD risk. The health literacy level of 59.9% of individuals was either inadequate or problematic. The health literacy score of participants with impaired risk perception (29.59±9.07) was lower than those with correct risk perception (35.83±10.94; P <0.001). Factors that affected the perception of CVD risk included age, education level, working status, occupation, a family history of CVD, the CVD risk assessment by health professionals and the recommendation of health professionals to individuals for CVD screening. It is necessary for health care professionals to make risk assessments and perform interventions to improve health literacy to raise the awareness of individuals with high CVD risk on their actual risk.

Where you go is who you are: a study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

Taring the scales: Weight-of-evidence framework for biocompatibility evaluations

ISO 10993-1:2018 describes evaluating the biocompatibility profile of a medical device from a risk-based approach. This standard details the battery of information that should be considered within the assessment of a device, including raw material composition data, manufacturing processes, and endpoint testing. The ISO 10993/18562 series requires worst-case assumptions and exposure scenarios to be used in the evaluation, which may result in an over-estimation of patient safety risk. Currently, biocompatibility assessments evaluate each data set independently, and the consequence of this individualized assessment of exaggerated inputs is potential false alarms regarding patient safety. To evaluate these safety concerns, the ISO standards indicate that professional judgement should be used to estimate patient risk but does not provide guidance on incorporating a holistic review of the data into the risk assessment. Recalibrating these worst-case data to evaluate them in a weight-of-evidence (WoE) approach may provide a more realistic data set to determine actual patient risk. This proposed WoE framework combines understanding data applicability with a method for gauging the strength of data that can provide additional support for the final safety conclusion. Using a WoE framework will allow risk assessors to contextualize the data and utilize it to comprehensively estimate patient safety.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BackgroundThe histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.MethodsIn this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.ResultsStatistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.ConclusionOur results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Evaluation Research on Energy-Saving Retrofitting of Roofs of Traditional Wood-Structured Dwellings Based on the Continuation of Historical Features: A Case Study of Guangdu Village No. 280 Dwelling in Zhejiang

China has numerous traditional wooden dwellings located in regions with hot summers and cold winters. The historic dwellings lack proper thermal insulation and have excessive energy consumption in the building structure, failing to suit the needs of modern people. Hence, it is crucial to enhance their energy efficiency through essential actions. Roofs, being the fifth side of the building envelope, are frequently neglected when it comes to enhancing their insulating capabilities. The study aimed to implement energy-efficient roof alterations while preserving the historical features of traditional structures. This study focuses on enhancing the energy efficiency of a traditional wood-structured dwelling in Guangdu Village, Zhejiang Province, China, by installing composite thermal insulation panels on the interior of the roof using an easy restoration approach. The actual risk of condensation and the impact of retrofit processes on historical features determine the choice and limitations of energy-saving retrofit materials and methods. This study evaluates the transformation using two indicators: energy efficiency and economic feasibility. The numerical simulation results indicate that traditional dwellings have an annual energy savings rate of 16.66% and an investment payback period of 7.25 years. This study demonstrates the feasibility of energy-saving roof renovation measures for these traditional wood-structured dwellings. The measures improve energy efficiency and are affordable for residents. This study can offer technical suggestions for enhancing the energy efficiency of traditional wood-structured dwellings.

Support and employment preferences in online platform work: A cluster analysis of German‐speaking workers

AbstractOnline platform work is an emerging field of non‐standard employment. Up to now, there has been little knowledge of the perspective of online platform workers on social protection and regulation. We provide quantitative data (n = 1727) on their needs for support and on their employment status preferences. Given the heterogeneity of German‐speaking online platform workers, we have conducted a cluster analysis to group workers according to task length, hourly wage, working hours and experience on online platforms. Most of the respondents are solo‐self‐employed and hybrid workers. They prefer support instruments that improve their skills and income over those that aim to strengthen their rights. The majority of platform workers are in favour of working outside of platforms. The study also shows that despite the low dependence on platform income, the actual poverty risk is relatively high.

Establishment and verification of a nomogram that predicts the risk for coronary slow flow.

Coronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors. In this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration. N-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram. The constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care.

Relationships between medications used in a mental health hospital and types of medication errors: A cross-sectional study over an 8-year period

BackgroundParticularities in psychiatry care can increase the risk of medication errors (MEs). ObjectiveTo analyze the MEs that occurred in a psychiatric hospital and to quantify relationships between the use of certain types of medication and the type of MEs. MethodsWe conducted a retrospective register based cross-sectional study in a French psychiatric hospital (2014–2021). All MEs were analyzed using ALARM method to identify type, stage of occurring and interception (defenses), consequences, drug involved and root causes. The prevalence-odds ratio (POR) was calculated to estimate relationships between five selected medication situations (long-acting injectable antipsychotics (LAIA), oral liquid dosage forms in a multiple-unit-container (OLDS-MC), psychotropic drugs (PD), controlled medicines (CM) or high-alert drugs) and the type of MEs occurred. ResultsAmong the 609 MEs reported, wrong dose (32.2%), wrong drug (30.3%), omission (14.2%) and wrong patient (12.9%) were frequently observed. The ME occurrence stage were prescribing (55.3%) and administration (30.2%). Medication order review intercepted 77.9% of MEs. CM or LAIA increased the risk of medication omission (POR: 3.9, 95%CI: 1.8–8.4 and 2.5, 95%CI; 1.2–5.1, respectively) while the use of high-alert medications decreased it (0.2, 95%CI: 0.1–0.8). OLDS-MC and PD were more likely to be administered to the wrong patient (6.1, 95%CI: 3.3–11.4 and 16.1, 95%CI: 7.2–35.8). LAIA were associated with an increased risk of wrong dose (3.4, 95%CI: 1.8–6.3). Actual errors risk was lower with high-alert drugs (0.5, 95%CI: 0.3–09), but higher with CM (3.5, 95%CI: 1.5–8.0), OLDS-MC (2.1, 95%CI: 1.2–3.8) and PD (2.5, 95%CI: 1.8–3.5). Patients exposed to high-alert drugs were likely to have a serious error (3.5, 95%CI: 1.2–10.4). ConclusionsThis study sheds an innovative approach to analyze MEs by demonstrating that certain medication situations were more likely to lead to certain types of error. This enables the most appropriate prevention barriers to be put in place to intercept ME.