Short-term Risk Assessment Research Articles

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide

The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the potential for enhanced accuracy and reliability. This study evaluates the performance of traditional models (ARIMA), machine learning models (LightGBM, ConvLSTM2D), and optimized machine learning solutions (Bayes residual optimization ConvLSTM2D LightGBM, Bayes_CL) in predicting Sentinel 5P columnar CO levels. This study findings demonstrate that machine learning models and their optimized versions significantly outperform traditional ARIMA models in cross-validation (CV), visualization, and overall prediction performance. Notably, machine learning model based on Bayes and residual optimization (Bayes_CL) achieved the highest CV score (Bayes_CL R2 = 0.8, LightGBM R2 = 0.79, ConvLSTM2D R2 = 0.75, ARIMA R2 = 0.61), along with superior visualization and other metrics. Using Bayes_CL, we effectively quantified a 2.4% increase in columnar CO levels in mainland China in the second half of 2023, following the complete lifting of COVID-19 lockdowns. This study confirms that machine learning models can effectively replace traditional methods for short-term risk assessment of Sentinel 5P columnar CO. This transition holds significant implications for policy formulation, greenhouse effect assessment, and population health risk evaluation, especially in uncertain situations where human activities are severely disrupted, thereby affecting environmental safety.

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Pesticide residues and risk assessment of trunk-injected pesticides in pine nut (Pinus koraiensis) seeds

Pine nuts, the edible seeds of pines (Family: Pinaceae, Pinus spp.), are popular worldwide, particularly those from the Korean pine tree (Pinus koraiensis), which is economically significant and widely exported. The spread of pine wilt disease (PWD) caused by pinewood nematodes (PWNs) has necessitated the use of trunk injections of pesticides in Korea, raising concerns about pesticide residues in edible pine nuts. This study investigated the concentration of pesticide residues in pine nuts from trunk-injected trees and assessed their potential health risks to consumers. After Korean pine tree was trunk-injected with pesticide, the pine nuts were subsequently harvested and analyzed for pesticide residues using LC‒MS/MS. Short- and long-term risk assessments of trunk-injected pesticides in pine nuts were conducted. Abamectin and emamectin benzoate were not detected, while acetamiprid and sulfoxaflor were detected, but their levels were below the maximum residue limits (MRLs) set by Korea, the EU, and the US. The short-term and long-term risk assessments indicated negligible health risks for consumers from these residues. Despite the low risk, we recommend continued monitoring of pesticide residues in pine nuts intended for sale to ensure consumer safety.

A nomogram for predicting short-term mortality in ICU patients with coexisting chronic obstructive pulmonary disease and congestive heart failure

ObjectiveThis study aimed to develop and validate a nomogram for predicting 28-day and 90-day mortality in intensive care unit (ICU) patients who have chronic obstructive pulmonary disease (COPD) coexisting with congestive heart failure (CHF). MethodsAn extensive analysis was conducted on clinical data from the Medical Information Mart for Intensive Care IV database, covering patients over 18 years old with both COPD and CHF, who were were first-time ICU admissions between 2008 and 2019. The least absolute shrinkage and selection operator (LASSO) regression method was employed to screen clinical features, with the final model being optimized using backward stepwise regression guided by the Akaike Information Criterion (AIC) to construct the nomogram. The predictive model's discrimination and clinical applicability were evaluated via receiver operating characteristic (ROC) curves, calibration curves, the C-index, and decision curve analysi s (DCA). ResultsThis analysis was comprised of a total of 1948 patients. Patients were separated into developing and validation cohorts in a 7:3 ratio, with similar baseline characteristics between the two groups. The ICU mortality rates for the developing and verification cohorts were 20.8 % and 19.5 % at 28 days, respectively, and 29.4 % and 28.3 % at 90 days, respectively. The clinical characteristics retained by the backward stepwise regression include age, weight, systolic blood pressure (SBP), respiratory rate (RR), oxygen saturation (SpO2), red blood cell distribution width (RDW), lactate, partial thrombosis time (PTT), race, marital status, type 2 diabetes mellitus (T2DM), malignant cancer, acute kidney failure (AKF), pneumonia, immunosuppressive drugs, antiplatelet agents, vasoactive agents, acute physiology score III (APS III), Oxford acute severity of illness score (OASIS), and Charlson comorbidity index (CCI). We developed two separate models by assigning weighted scores to each independent risk factor: nomogram A excludes CCI but includes age, T2DM, and malignant cancer, while nomogram B includes only CCI, without age, T2DM, and malignant cancer. Based on the results of the AUC and C-index, this study selected nomogram A, which demonstrated better predictive performance, for subsequent validation. The calibration curve, C-index, and DCA results indicate that nomogram A has good accuracy in predicting short-term mortality and demonstrates better discriminative ability than commonly used clinical scoring systems, making it more suitable for clinical application. ConclusionThe nomogram developed in this study offers an effective assessment of short-term mortality risk for ICU patients with COPD and CHF, proving to be a superior tool for predicting their short-term prognosis.

Short-term risk of fracture is increased by deficits in cortical and trabecular bone microarchitecture independent of DXA BMD and FRAX: Bone Microarchitecture International Consortium (BoMIC) prospective cohorts.

Identifying individuals at risk for short-term fracture is essential to offer prompt beneficial treatment, especially since many fractures occur in those without osteoporosis by DXA-aBMD. We evaluated whether deficits in bone microarchitecture and density predict short-term fracture risk independent of the clinical predictors, DXA-BMD and FRAX. We combined data from eight cohorts to conduct a prospective study of bone microarchitecture at the distal radius and tibia (by HR-pQCT) and 2-year incidence of fracture (non-traumatic and traumatic) in 7327 individuals (4824 women, 2503 men, mean 69 ± 9years). We estimated sex-specific hazard ratios (HR) for associations between bone measures and 2-year fracture incidence, adjusted for age, cohort, height, and weight, and then additionally adjusted for FN aBMD or FRAX for major osteoporotic fracture. Only 7% of study participants had FN T-score ≤ -2.5, whereas 53% had T-scores between -1.0 and -2.5 and 37% had T-scores ≥-1.0. Two-year cumulative fracture incidence was 4% (296/7327). Each SD decrease in radius cortical bone measures increased fracture risk by 38%-76% for women and men. After additional adjustment for FN-aBMD, risks remained increased by 28%-61%. Radius trabecular measures were also associated with 2-year fracture risk independently of FN-aBMD in women (HRs range: 1.21 per SD for trabecular separation to 1.55 for total vBMD). Decreased failure load (FL) was associated with increased fracture risk in both women and men (FN-aBMD ranges of adjusted HR = 1.47-2.42). Tibia measurement results were similar to radius results. Findings were also similar when models were adjusted for FRAX. In older adults, FL and HR-pQCT measures of cortical and trabecular bone microarchitecture and density with strong associations to short-term fractures improved fracture prediction beyond aBMD and FRAX. Thus, HR-pQCT may be a useful adjunct to traditional assessment of short-term fracture risk in older adults, including those with T-scores above the osteoporosis range.

Optimization of Weibull Distribution Parameters with Application to Short-Term Risk Assessment and Strategic Investment Decision-Making

Accurate parameter estimation is fundamental in financial modeling, especially in investment analysis, where the Modified Internal Rate of Return (MIRR) plays a key role in evaluating investment performance. This study aims to enhance risk and return predictions in Sharia-compliant property investments by exploring the efficacy of various optimization techniques for estimating Weibull distribution parameters within the MIRR framework. To achieve this, we employed a comparative analysis of optimization methods, including Simulated Annealing (SA), Differential Evolution (DE), Genetic Algorithm (GA), and traditional Numerical Methods (NM). Performance was assessed through metrics such as Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), R-squared (R2) values, and Kolmogorov-Smirnov (KS) statistics. The results reveal that metaheuristic algorithms (SA, DE, GA) significantly outperform traditional numerical methods in terms of parameter estimation accuracy. Specifically, SA achieved the lowest RMSE of 0.042, with a Weibull shape parameter estimate of 1.254 and variance of 0.004, followed closely by DE with an RMSE of 0.048, and GA with 0.046. In contrast, NM exhibited a higher RMSE of 0.067, with a shape parameter estimate of 1.310 and a variance of 0.006. The AIC values for metaheuristic methods ranged from 14.25 to 14.68, compared to 15.12 for NM, and R2 values for metaheuristic methods ranged from 0.932 to 0.945, compared to 0.910 for NM. KS statistics further underscored the superior model fit of metaheuristics, with SA showing the lowest KS value of 0.045. The study underscores the critical role of metaheuristic optimization in improving the accuracy of parameter estimation based on MIRR models. This enhancement provides more reliable risk assessments and returns predictions, offering valuable insights for informed investment decision-making and contributing to optimized financial outcomes in the property sector.

Substance Use in Forensic Assertive Community Treatment: Development and Implementation of a Pilot Project for Internees in Belgium

Substance use is a significant factor associated with new arrests, detentions and compulsory admissions for patients in forensic assertive community teams (FACT). To enhance community tenure for forensic patients and facilitate their sustainable integration into society, a treatment that offers a community-based alternative to admission following substance use was developed. In this paper, the development of the project, FENIX + is described along with the reporting of a pilot study of all included patients during 12 months (N = 24). Recidivism risk and community integration were assessed utilizing the Short-Term Risk Assessment and Treatability (START) as well as the Measurements in Addiction for Triage and Evaluation (MATE) scales. The included patients avoided 60% of admissions, exhibited positive changes in their desire for assistance, and successfully reduced their risk of recidivism. By accessing the community-based alternative, they were able to delay admissions effectively. These promising results suggest that a community-based treatment could benefit patients at risk of reoffending following substance use.

Modification of the existing maximum residue levels in various plant commodities resulting from the use of potassium phosphonates.

In accordance with Article 6 of Regulation (EC) No 396/2005, the applicants De Sangosse SAS and Tilco-Alginure submitted two requests, respectively, to the competent national authorities in France and Germany to modify the existing maximum residue levels (MRLs) for the active substance potassium phosphonates in various plant commodities. The data submitted in support of the requests were found to be sufficient to derive MRL proposals for the commodities under assessment. For the derived MRL on baby leaf crops, further risk manager consideration is required to decide between two MRL options. Adequate analytical methods for enforcement are available to control the residues of potassium phosphonates in accordance with the residue definition 'phosphonic acid and its salts expressed as phosphonic acid' in the commodities under consideration. Based on the risk assessment results and assuming that the existing MRLs will be amended as proposed by EFSA in previous outputs, EFSA concluded that the long-term intake of residues resulting from the existing uses of fosetyl and phosphonates (previously assessed in a joint MRL review) and new proposed uses of potassium phosphonates is unlikely to present a risk to consumer health. Considering the toxicological profile of the active substance, a short-term dietary risk assessment was not required. The risk assessment shall be regarded as indicative because some MRL proposals derived by EFSA in the framework of the MRL review according to Articles 12 and 43 of Regulation (EC) No 396/2005 require further consideration by risk managers.

Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach

PurposeTo develop prediction models for short-term mortality risk assessment following colorectal cancer surgery.MethodsData was harmonized from four Danish observational health databases into the Observational Medical Outcomes Partnership Common Data Model. With a data-driven approach using the Least Absolute Shrinkage and Selection Operator logistic regression on preoperative data, we developed 30-day, 90-day, and 1-year mortality prediction models. We assessed discriminative performance using the area under the receiver operating characteristic and precision-recall curve and calibration using calibration slope, intercept, and calibration-in-the-large. We additionally assessed model performance in subgroups of curative, palliative, elective, and emergency surgery.ResultsA total of 57,521 patients were included in the study population, 51.1% male and with a median age of 72 years. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.88, 0.878, and 0.861 for 30-day, 90-day, and 1-year mortality, respectively, and a calibration-in-the-large of 1.01, 0.99, and 0.99. The overall incidence of mortality were 4.48% for 30-day mortality, 6.64% for 90-day mortality, and 12.8% for 1-year mortality, respectively. Subgroup analysis showed no improvement of discrimination or calibration when separating the cohort into cohorts of elective surgery, emergency surgery, curative surgery, and palliative surgery.ConclusionWe were able to train prediction models for the risk of short-term mortality on a data set of four combined national health databases with good discrimination and calibration. We found that one cohort including all operated patients resulted in better performing models than cohorts based on several subgroups.

Complex facies architecture of unusual pyroclastic density current deposits at the Rincón de la Vieja Volcano, Costa Rica

Pyroclastic Density Currents (PDCs) constitute a complex, multiphase, and multiscale volcanic process generated in a wide spectrum of eruptive scenarios and being among the most deadly and destructive volcanic hazards. Over the north flank of the Rincón de la Vieja Volcano, Costa Rica, previous works identified PDC deposits related to the plinian Río Blanco eruption, which is the largest Holocene explosive event recorded at this volcano. However, so far, there have been no in-depth studies aimed at understanding the eruptive style, and dynamics of the parental PDCs. This work presents, for the first time, a detailed facies analysis and architecture reconstruction (plus a geochemical characterization and novel C14 age determinations) of these outstanding deposits. The presence of cauliflower bombs, juvenile mega-blocks, scorias, and pumices, coupled with the scarcity of accidental and/or accessory lithics, is interpreted here as evidence of an unusual eruptive style. We propose that the Río Blanco eruption was a single complex eruption consisting of two successive phases, each involving the drainage of distinct magma batches. The PDCs were related to the sudden spill out of a lava-pool (Arenal-type PDC), during the collapse of an ultraproximal pyroclastic cone that was built in an initial plinian phase. The collapse of this ultraproximal cone produced a leading-edge pumice-rich PDC, which increased its mobility through fragmentation-induced fluidization. This leading edge PDC was followed by successive high concentrated, mafic, modified granular flow pulses derived from the lava-pool spill out. These modified granular flows were highly channelized in proximal zones, but when they reached the main break in slope, the basal underflow froze en masse while its upper, more diluted part started to decouple and move in a more unconfined manner. After the decoupling, the material feeding towards the basal zone of the surviving current produced a new underflow by rapid suspension sedimentation (“surge-derived pyroclastic flows”). Finally, our results indicate that the generation of Arenal-type PDCs is an underestimated scenario that should be considered in future short-term hazard and risk assessments at the Rincón de la Vieja Volcano, as well as in many other stratovolcanoes worldwide.

Reducing Risk for Future Violence From Forensic Psychiatric Patients by Using Critical Factors of the Short-Term Assessment of Risk and Treatability as a Caring Tool.

The dual task for forensic mental health care is community protection and treatment of mental disorders. The Short-Term Assessment of Risk and Treatability (START) has become an established tool for risk assessment and care planning in forensic psychiatry. When using the START, items judged as critical factors are chosen to be addressed in the care plan. However, it is not known which critical factors that are of special interest need to be addressed to reduce the risk for future violence. The main aim of this study was to explore how staff's choice of critical factors in the care planning influences risk for future violence. The study is based on 787 START assessments from 285 convicted and involuntary admitted inpatients at the Forensic Psychiatric Clinic of Stockholm County, Sweden. The influence on risk for future violence by selecting a certain item as a critical factor in the care plan was measured by comparing patients' sum of vulnerabilities at the time this certain critical factor was selected with this sum at the next assessment. The results show that a patient's overall risk for future violence decreases significantly when the items "insight," "attitudes," "mental state," "coping," and "drug use" were addressed as critical factors in the care planning. The importance of specified critical factors differs between diagnostic groups and time after admission. When staff select certain specified items as critical factors in the care planning, the patients' risk of relapse into acts of violence was significantly reduced.

An assessment of interventions following moderate and high scores on the dynamic appraisal of situational aggression risk assessment tool in a forensic mental health unit

ABSTRACT The Dynamic Appraisal of Situational Aggression (DASA) provides a quick and systematic assessment of short-term violence risk. Risk assessment should be closely aligned to management and result in interventions aimed to reduce risk. The aim of this study was to investigate what interventions followed a moderate/high DASA score and whether they reduced risk. The impact of staff training was also assessed. The study was a retrospective analysis of health records within a medium secure hospital over 6 months. Data was gathered on tool adherence and interventions that were used to reduce risk following a moderate/high score. The change in DASA score following the intervention was recorded. There were 70 patients covering 8224 bed days. There were 24 occasions where a moderate score led to an intervention (n = 24/40%), and 26 occasions for a high score (n = 26/87%). A moderate score was mostly followed by no intervention (n = 35/59%), a high score was mostly followed by seclusion (n = 9/29%). The intervention that led to the largest reduction in DASA score was pro re nata medication following a moderate score and seclusion following a high score. The training of staff led to a reduction in tool adherence and increased intervention use. These results support the need for combining the DASA assessment with operationalised systems to link assessment with risk prevention to help reduce aggression and restrictive practices.

A novel IoT-based framework with Prognostics and Health Management and short term fire risk assessment in smart firefighting system

In recent years, “smart firefighting” becomes a popular research topic which aims at improving the efficiency of fire protection and safety. However, in existing smart firefighting systems: (1) state and analog firefighting IoT data is not fully utilized; (2) fire risk assessment requires a long time to update; (3) few existing works can recognize faults and forecast potential risks with both temporal and spatial information from multiple IoT sensors. Therefore, a novel framework is proposed to solve the above problems as follows: (1) Short-term Fire Risk Assessment (SFRA) is proposed by considering the working status, maintenance and rectification performance of firefighting facilities based on IoT state data; (2) Prognostics and Health Management (PHM) algorithms are applied in the fire safety field for the first time to detect faults and forecast potential risks with temporal and spatial information with IoT analog data from single or multiple sensors. A case study is carried out in a building in Shanghai and a smart firefighting system is deployed with the edge computing gateway. The experimental results of the SFRA and PHM are presented and analyzed in details. The performance evaluation and comparison results are presented with multiple metrics. The reliability of the proposed framework is discussed. The case study concluded that the proposed framework extends the utilization of IoT data, improves the efficiency of fire risk assessment and enhances the accuracy of faults detection and potential risk forecast. Moreover, the deployed firefighting system improves the data privacy and computational efficiency.

Assessing the Alignment of Short-Term Assessment of Risk and Treatability (START) with NANDA-I Taxonomy in Forensic Care Settings

Purpose. To investigate the alignment of START with NANDA-I in forensic psychiatric care. Design. A quantitative design was chosen to compare the START instrument with the NANDA-I taxonomy and analyze their alignment. Each item of the START was attempted to be matched with potentially relevant NANDA-I diagnoses, and the matched diagnoses were extracted and presented. Findings. The study demonstrated a strong alignment between START coding and NANDA-I diagnoses, with 99% of the START diagnoses finding a match within NANDA-I. These results support the use of NANDA-I in forensic psychiatric care, which facilitate providing comprehensive care and avoiding sole focus on psychiatric problems. Practice Implications. It is recommended to use a structured nursing taxonomy, such as NANDA-I, in forensic psychiatric care.

Impact of coupling on neuronal extreme events: Mitigation and enhancement

We focus on the emergence of extreme events in a collection of aperiodic neuronal maps, under local diffusive coupling, as well as global mean-field coupling. Our central finding is that local diffusive coupling enhances the probability of occurrence of both temporal and spatial extreme events, while in marked contrast, global mean-field coupling suppresses extreme events. So the nature of the coupling crucially determines whether the extreme events are enhanced or mitigated by coupling. Further, in globally coupled systems, there exist initial states in a window of coupling strength that exhibit spatial extreme events, but not temporal extreme events, suggesting that spatial extreme events do not imply temporal extreme events. We also explored the existence of discernible patterns in the return maps of successive inter-event intervals in order to gauge short-term risk-assessment. We find that single neuronal maps, as well as systems under strong diffusive coupling, display broad noisy patterns in these return maps, with clusters around characteristic intervals, allowing some short-term predictability in the extreme event sequence. In contrast, under weak diffusive coupling and global coupling, inter-event intervals lose all perceptible correlations, and the distribution extends to very large inter-event intervals. Lastly, we investigated a non-local diffusive coupling form. Interestingly, this coupling yielded a large window where temporal extreme events occurred, but the spatial profile was synchronized, namely, we found synchronized temporal extreme events. Such synchronized extreme spiking is reminiscent of the neuronal activity leading to epileptic seizures and is of potential relevance to extreme events in brain activity.

Coastal erosion and climate change: A review on coastal-change process and modeling.

Coastal erosion is a normal process of nature. However, the rate of coastal erosion, and the frequency and intensity of coastal flooding events, are now on the rise around the world due to the changing climate. Current responses to coastal erosion are primarily determined by site-specific factors, such as coastal elevation, coastal slope, coastal features, and historical coastline change rate, without a systematic understanding of the coastal-change processes in the context of climate change, including spatiotemporal changes in sea level, regional changes in wave climate, and sea ice coverage. In the absence of a clear understanding of the coastal-change processes, most of the current coastal responses have been built upon a risky assumption (i.e., the present-day coastal change will persist) and are not resilient to future climate change. Here, we conduct a literature review to summarize the latest scientific understanding of the coastal-change processes under climate change and the potential research gaps towards the prediction of future coastal erosion. Our review suggests that a coupled coastal simulation system with a nearshore wave model (e.g., SWAN, MIKE21, etc.) can play a critical role in both the short-term and long-term coastal risk assessment and protective measure development.

Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

Falls are a leading cause of morbidity and mortality in older adults with dementia residing in long-term care. Having access to a frequently updated and accurate estimate of the likelihood of a fall over a short time frame for each resident will enable care staff to provide targeted interventions to prevent falls and resulting injuries. To this end, machine learning models to estimate and frequently update the risk of a fall within the next 4 weeks were trained on longitudinal data from 54 older adult participants with dementia. Data from each participant included baseline clinical assessments of gait, mobility, and fall risk at the time of admission, daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system. Systematic ablations investigated the effects of various hyperparameters and feature sets and experimentally identified differential contributions from baseline clinical assessments, ambient gait analysis, and daily medication intake. In leave-one-subject-out cross-validation, the best performing model predicts the likelihood of a fall over the next 4 weeks with a sensitivity and specificity of 72.8 and 73.2, respectively, and achieved an area under the receiver operating characteristic curve (AUROC) of 76.2. By contrast, the best model excluding ambient gait features achieved an AUROC of 56.2 with a sensitivity and specificity of 51.9 and 54.0, respectively. Future research will focus on externally validating these findings to prepare for the implementation of this technology to reduce fall and fall-related injuries in long-term care.

A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer.

Image-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated. We performed a case-cohort study of 8110 women aged 40-74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer-Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up. The AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70-0.80) to 0.68 (95%CI: 0.66-0.69) 1-10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66-0.78) to 0.65 (95%CI: 0.63-0.66) for the imaging-only model and 0.62 (95%CI: 0.55-0.68) to 0.60 (95%CI: 0.58-0.61) for Tyrer-Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model, p < 0.01, and 4.6% for Tyrer-Cuzick, p < 0.01. The lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer-Cuzick models.

Modification of the existing maximum residue levels in leeks and spring onions/green onions/Welsh onions resulting from the use of potassium phosphonates.

In accordance with Article 6 of Regulation (EC) No 396/2005, the applicant BASF Agro B.V. Arnhem (NL) Freienbach Branch submitted a request to the competent national authority in the Netherlands to modify the existing maximum residue levels (MRLs) for the active substance potassium phosphonates in leeks and spring onions/green onions/Welsh onions. The data submitted in support of the request were found to be sufficient to derive MRL proposals for the commodities under assessment. Adequate analytical methods for enforcement are available to control the residues of potassium phosphonates in accordance with the proposed residue definition 'phosphonic acid and its salts expressed as phosphonic acid' on the commodities under consideration. Based on the risk assessment results and assuming that the exiting MRLs will be amended as proposed by EFSA in previous outputs, EFSA concluded that the long-term intake of residues resulting from the use of potassium phosphonates according to the reported agricultural practices is unlikely to present a risk to consumer health. Considering the toxicological profile of the active substance, a short-term dietary risk assessment was not required. The risk assessment shall be regarded as indicative because some MRL proposals derived by EFSA in the framework of the MRL review according to Articles 12 and 43 of Regulation (EC) No 396/2005 require further consideration by risk managers.

Violence and Suicidal/Nonsuicidal Self-Injury Among Adolescents Undergoing Residential Treatment: An Examination of the Predictive Validity of the SAVRY, START:AV, and VRS-YV.

Using a retrospective study design, predictive validity of the Structured Assessment of Violence Risk in Youth, Short-Term Assessment of Risk and Treatability: Adolescent Version (START: AV), and the Violence Risk Scale-Youth Version (VRS-YV) was examined among 87 adolescents referred to a residential treatment program. With few exceptions, moderate to high accuracy was achieved for the three measures in predicting violence and suicidal/nonsuicidal self-injury occurring during the adolescents' time in treatment. Accuracy of the measures peaked within 90 days for violence and gradually increased over the 180-day follow-up for suicidal/nonsuicidal self-injury. Dynamic factors were more predictive of repeated events involving violence relative to static/historical factors, whereas only factors from the START: AV were predictive of repeated events involving suicidal/nonsuicidal self-injury. These results emphasize the need for further examining the risk of adverse outcomes beyond violence among adolescents.