Risk Categories Research Articles

Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences

Abstract Earthquakes are a rather complex natural phenomenon that cannot be prevented, and their effects can be catastrophic and have profound implications on various economic sectors (especially tourism). This study investigates the relationships between subjective factors (gender, age, education, previous experience, and disaster anxiety), the perceived travel risk, and the travel intention of tourists from Serbia to destinations in Türkiye. The pilot study was done in March 2023 with 110 respondents from the Faculty of Science, Department of Geography, Tourism, and Hotel Management, Novi Sad. After the validation of the instruments, the main survey was conducted in the period from March to June 2023. The subjects of interest were residents of Serbia over the age of 18. The data from this study are analyzed using the confirmatory factor analysis and path analyses. This study aims to enhance the understanding of perceived risk and travel intention, specifically focusing on Türkiye. The assessed outcome relates to how tourists perceive three specific categories of risk when traveling to destinations with a high risk of natural disasters, such as earthquake-prone areas. The findings suggest that older respondents tend to perceive lower physical and financial travel risks, even though the influence of age on the perception of socio-psychological risks was inconclusive. Further results reveal that individuals with prior experience tend to hold a heightened perception of physical, financial, and socio-psychological risks. An examination of the relationship between disaster anxiety and perceived travel risks indicates that physical, financial, and socio-psychological risks exert a negative influence on travel intention. Thus, this study may provide a conceptual foundation for both theoretical and practical implications for the improvement of risk management techniques at a specific travel destination in areas prone to earthquake hazards.

The K9 lymphoma assay allows a genetic subgrouping of canine lymphomas with improved risk classification

We present here the K9 lymphoma assay, a novel 31-gene targeted next-generation sequencing panel designed for genomic profiling of canine lymphoid neoplasms. Addressing the growing demand for advanced diagnostics in veterinary oncology, this assay enables sensitive identification of known and actionable mutations specific to canine lymphomas, while evaluating its prognostic potential to facilitate diagnosis and prognosis. Our analysis, spanning several B- and T-cell lymphoma histotypes, unveiled distinct mutational landscapes distinguishing tumors derived from immature versus mature lymphocytes. Clustering analysis revealed a shared genetic origin between diffuse large B-cell lymphoma and marginal zone lymphoma, aligning with findings in human lymphomas, with TRAF3 emerging as the most frequently mutated gene across B-cell lymphoma subtypes. Significantly, TP53 mutations demonstrated universal adverse prognostic implications across B-cell lymphomas. Additionally, SETD2 mutations contributed to shorter time-to-progression, underscoring the role of epigenetic dysregulation in B-cell tumors. In T-cell lymphomas, SATB1 and FBXW7 were frequently mutated, warranting further investigation in larger cohorts. Our findings advocate for tailored therapeutic approaches based on the genetic profile, impacting treatment decisions and outcomes in canine lymphoma management. This study provides pivotal insights bridging veterinary and human oncology, paving the way for comprehensive genomic diagnostics and therapeutic strategies in comparative oncology.

Developing a risk score to inform the use of rhBMP-2 in adult spinal deformity surgery.

Recombinant human bone morphogenetic protein-2 (rhBMP-2) has not shown superior benefit overall in cost-effectiveness during adult spinal deformity (ASD) surgery. Retrospective PURPOSE: Generate a risk score for pseudarthrosis to inform the utilization of rhBMP-2, balancing costs against quality of life and complications. ASD patients with 3-year data were included. Quality of life gained was calculated from ODI to SF-6D and translated to quality-adjusted life years (QALYs). Cost was calculated using the PearlDiver database and CMS definitions for complications and comorbidities. Established weights were generated for predictive variables via logistic regression to yield a predictive risk score for pseudarthrosis that accounted for frailty, diabetes, depression, ASA grade, thoracolumbar kyphosis and three-column osteotomy use. Risk score categories, established via conditional inference tree (CIT)-derived thresholds were tested for cost-utility of rhBMP-2 usage, controlling for age, prior fusion, and baseline deformity and disability. 64% of ASD patients received rhBMP-2 (308/481). There were 17 (3.5%) patients that developed pseudarthrosis. rhBMP-2 use overall did not lower pseudarthrosis rates (OR: 0.5, [0.2-1.3]). Pseudarthrosis rates for each risk category were: No Risk (NoR) 0%; Low-Risk (LowR) 1.6%; Moderate Risk (ModR) 9.3%; High-Risk (HighR) 24.3%. Patients receiving rhBMP-2 had similar QALYs overall to those that did not (0.163 vs. 0.171, p = .65). rhBMP-2 usage had worse cost-utility in the LowR cohort (p < .001). In ModR patients, rhBMP-2 usage had equivocal cost-utility ($53,398 vs. $61,581, p = .232). In the HighR cohort, the cost-utility was reduced via rhBMP-2 usage ($98,328 vs. $211,091, p < .001). Our study shows rhBMP-2 demonstrates effective cost-utility for individuals at high risk for developing pseudarthrosis. The generated score can aid spine surgeons in the assessment of risk and enhance justification for the strategic use of rhBMP-2 in the appropriate clinical contexts. III.

Outcomes of adults with refractory or relapsed acute myeloid leukemia treated with azacitidine and venetoclax compared to other therapies: a multicenter retrospective study

This study reports characteristics and outcomes of adults who received Azacitidine-Venetoclax (AZA-VEN) compared to other salvage therapies (NO-AZA-VEN) as first salvage therapy for acute myeloid leukemia (AML). The clinical data of 81 patients with a diagnosis of relapsed or refractory AML were analyzed. The ORR was comparable for both groups (55% vs 57%, p = 0.852). Median OS (6.8 vs 11.2 months, p = 0.053) and median RFS (6.9 vs 11.2 months, p = 0.488) showed a trend in favor of the NO-AZA-VEN group. OS was significantly longer with NO-AZA-VEN for ELN 2022 risk category sub-group, patients under 60 years old, primary AML and for patients who underwent allo-hematopoietic stem cell transplant after salvage therapy. There was no statistical difference in complications of treatment such as febrile neutropenia, intensive care unit stay, septic shock and total parenteral nutrition. Those results do not support the preferential use of AZA-VEN over other regimens in R/R acute myeloid leukemia.

Bayesian network analysis of risk classification strategies in the regulation of cellular products

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.

Beat-AML 2024 ELN–refined risk stratification for older adults with newly diagnosed AML given lower-intensity therapy

AbstractAlthough the 2022 European LeukemiaNet (ELN) acute myeloid leukemia (AML) risk classification reliably predicts outcomes in younger patients treated with intensive chemotherapy, it is unclear whether it applies to adults ≥60 years treated with lower-intensity treatment (LIT). We aimed to test the prognostic impact of ELN risk in patients with newly diagnosed (ND) AML aged ≥60 years given LIT and to further refine risk stratification for these patients. A total of 595 patients were included: 11% had favorable-, 11% intermediate-, and 78% had adverse-risk AML. ELN risk was prognostic for overall survival (OS) (P < .001) but did not stratify favorable- from intermediate-risk (P = .71). Within adverse-risk AML, the impact of additional molecular abnormalities was further evaluated. Multivariable analysis was performed on a training set (n = 316) and identified IDH2 mutation as an independent favorable prognostic factor, and KRAS, MLL2, and TP53 mutations as unfavorable (P < .05). A “mutation score” was calculated for each combination of these mutations, assigning adverse-risk patients to 2 risk groups: −1 to 0 points (“Beat-AML intermediate”) vs 1+ points (“Beat-AML adverse”). In the final refined risk classification, ELN favorable- and intermediate-risk were combined into a newly defined “Beat-AML favorable-risk” group, in addition to mutation scoring within the ELN adverse-risk group. This approach redefines risk for older patients with ND AML and proposes refined Beat-AML risk groups with improved discrimination for OS (2-year OS, 48% vs 33% vs 11%, respectively; P < .001), providing patients and providers additional information for treatment decision-making.

Assessment of knowledge of the new pregnancy and lactation labeling rule among pharmacists and physicians in Saudi Arabia.

Researchers in Saudi Arabia conducted this study to determine the level of familiarity that pharmacists and physicians possess with the pregnancy and lactation labeling rules established by the Food and Drug Administration. The present study included a cross-sectional survey conducted among pharmacists and physicians working in Saudi Arabia. The sample size was determined using the Rao sample size calculator. We utilized the Statistical Package for the Social Sciences (SPSS) version 25 for our analysis. A total of 122 respondents completed the study. Among them, 72.9% were aged between 25 and 44 years, and approximately 63.9% were male. About 64% of the respondents indicated familiarity with the A, B, C, D, and X letter system of pregnancy category labeling. Over 37% stated that the new pregnancy and lactation labeling rule would continue to use the lettering system. Additionally, 83% believed that the A, B, C, D, and X risk category labeling system is a useful resource, and 82% believed that working with this risk category labeling improves patient care. The study revealed that pharmacists and physicians exhibited good knowledge of the old rule but a low level of knowledge regarding the new rule. Despite significant flaws in the old system, most preferred it. Enhancing knowledge in this area is crucial for improving risk communication and the quality of care for women of reproductive age.

Predicting major clinical events among Canadian adults with laboratory-confirmed influenza infection using the influenza severity scale

We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network’s Serious Outcomes Surveillance Network (2011/2012–2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71–0.74). Aggregated scores classified participants into three risk categories: low (ISS < 30; 79.9% sensitivity, 51% specificity), moderate (ISS ≥ 30 but < 50; 54.5% sensitivity, 55.9% specificity), and high (ISS ≥ 50; 51.4% sensitivity, 80.5% specificity). ISS demonstrated a solid ability to identify patients with hospitalized laboratory-confirmed influenza at increased risk for Major Clinical Events, potentially impacting clinical practice and research.

Prospective Validation of the Pancreatic Fistula Risk Classification by the International Study Group for Pancreatic Surgery (PARIS trial).

The aim of this study was a prospective validation of the recently established ISGPS pancreas classification as a parenchymal risk classification system for pancreatic fistula after pancreatoduodenectomy. Postoperative pancreatic fistula (POPF) is the major driver for complications after partial pancreatoduodenectomy (PD). Recently, the International Study Group for Pancreatic Surgery (ISGPS) published a pancreas classification containing the parameters main pancreatic duct diameter (MPD) and pancreatic texture to help assess the risk of POPF development following pancreatoduodenectomy. From January 2020 to July 2021, 271 patients receiving elective PD were included after informed consent. The postoperative course was documented prospectively up to postoperative day 30. Among the pancreas characteristics, MPD and pancreatic texture were assessed intraoperatively at the pancreatic resection margin and the pancreatic glands were assigned to one of the four pancreas classes according to the ISGPS (A to D). The primary endpoint was POPF according to the updated ISGPS definition. Secondary endpoints comprised other post-PD morbidity and mortality. Of 271 patients, 264 had available data according to the ISGPS pancreas classification. Of those, 78 were assigned to class A (30%), 53 to class B (20%), 50 to class C (19%) and 83 to class D (31%). POPF occurred in 54 of 271 patients (19.9%). The 30-day mortality was 7/271 (2.6%), with 6/7 having developed POPF (86%). POPF rates within the classes A, B, C and D were 9.0%, 11.3%, 20.0% and 37.4%, respectively (P<0.001). In the univariable regression analysis, only patients in pancreas class D demonstrated a significantly higher risk for POPF when compared to class A (OR 6.05, 95%-CI: 2.6-15.9, P<0.001). In the multivariable regression model, patients in class D had a significantly higher risk for POPF compared to class A (OR 3.45, 95%-CI: 1.15-11.3, P=0.032). The model comprised Body Mass Index, surgery duration, microscopic fibrosis and the ISGPS pancreas classification, demonstrating an AUC-value of approximately 0.82 when tested on the PARIS dataset. This prospective trial shows that the ISGPS pancreas classification is valid. Patients in risk class D are prone to POPF independently of other factors. Therefore, all future publications on pancreatic surgery should report the risk class according to the ISGPS pancreas classification to allow for a better comparison of reported cohorts.

Corporate culture's influence on the transparency of financial reporting in Iran: an in-depth analysis of readability and tone

PurposeThis study investigates the influence of corporate culture on financial reporting transparency within Iranian firms.Design/methodology/approachLeveraging a dataset of 1,480 firm-year observations from the Tehran Stock Exchange spanning from 2013 to 2022, the study employs text mining to quantify linguistic features of corporate culture and transparency, specifically readability and tone, within annual financial statements and Management Discussion and Analysis (MD&amp;A) reports.FindingsOur results confirm a positive and significant relationship between corporate culture and financial reporting transparency. The distinct dimensions of corporate culture — Creativity, Competition, Control, and Collaboration — each uniquely enhance financial transparency. Robustness tests including firm fixed-effects, entropy balancing, Generalized Method of Moments (GMM), and Propensity Score Matching (PSM) validate the profound influence of corporate culture on transparency. Additionally, our analysis shows that corporate culture significantly affects the disclosure of business, operational, and financial risks, with varying impacts across risk categories. Cross-sectional analysis further reveals how the impact of corporate culture on transparency varies significantly across different industries and firm sizes.Research limitations/implicationsThe study’s scope, while focused on Iran, opens avenues for comparative research in different cultural and regulatory environments. Its reliance on text mining could be complemented by qualitative methods to capture more nuanced linguistic subtleties.Practical implicationsFindings underscore the strategic importance of cultivating a transparent corporate culture for enhancing financial reporting practices and stakeholder trust, particularly in emerging economies with similar dynamics to Iran.Originality/valueThis research is pioneering in its quantitative analysis of the textual features of corporate culture and its impact on transparency within Iranian corporate reports, integrating foundational theoretical perspectives with empirical evidence.

POTENTIAL CAUSAL ASSOCIATION BETWEEN HUMAN CYTOMEGALOVIRUS INFECTION AND CHRONIC BACK PAIN: A ONE-SAMPLE MENDELIAN RANDOMIZATION STUDY

BackgroundChronic back pain (CBP) is a major cause of disability globally and its causes are multifactorial. Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are human herpes viruses usually acquired in early life. About 50% and over 90% of the population worldwide have been infected with CMV and EBV, respectively. This study investigated a potential causal relationship between CMV infection and CBP.MethodUK Biobank participants provided information on CMV seropositivity and CBP status, which were available for both traits in 5,140 participants. We used EBV seropositivity as a negative control to identify confounding and inaccurate causal inference. A one-sample Mendelian randomization (MR) based on independent genetic variants predicting CMV and EBV positivity was conducted in Northern European participants. To validate the association further, the MR study was repeated using a CMV polygenic risk score (PRS).ResultsCMV GWAS revealed 86 independent SNPs having p-value &lt; 2 × 10−4 for the one-sample MR. These SNPs were used to define genetically-predicted categories of CMV infection risk. CMV infection risk categories were significantly associated with CBP (OR = 1.150; 95% CI: 1.005–1.317, p-value = 0.043), findings which were confirmed using the CMV PRS (OR = 1.299; 95% CI: 1.141–1.479, p-value = 0.001). There was no causal association between EBV and CBP (p-value = 0.17).ConclusionOur results provide further evidence for a causal relationship between CMV infection and CBP. These results suggest a stratified approach to CBP may be useful, particularly in clinical trials and they shed light on underlying mechanisms in CBP.Conflicts of interestNo conflicts of interestSources of fundingNo funding obtainedAcknowledgementUKBB data were obtained under the project #18219Some aspects of this work have been previously presented at The Challenge of Chronic Pain: From Genomics to Therapy in UK and first 1st Danish International Conference on Personalised Medicine in Denmark.

Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model.

The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive periodontal care (SPC) for 10 years. Clinical and radiographic parameters were analysed to assign tooth prognosis with a tooth prognostic system (TPS) by two calibrated examiners from different clinical centres (London and Pittsburgh). The prediction model was developed on the London dataset. A logistic regression model (LR) and a neural network model (NN) were developed to analyse the data. These models were externally validated on the Pittsburgh dataset. The primary outcome was 10-year tooth loss in teeth assigned with 'unfavourable' prognosis. A total of 1626 teeth in 69 patients were included in the London cohort (development cohort), while 2792 teeth in 116 patients were included in the Pittsburgh cohort (external validated dataset). While the TPS in the validation cohort exhibited high specificity (99.96%), moderate positive predictive value (PPV = 50.0%) and very low sensitivity (0.85%), the AI-based model showed moderate specificity (NN = 52.26%, LR = 67.59%), high sensitivity (NN = 98.29%, LR = 91.45%), and high PPV (NN = 89.1%, LR = 88.6%). AI-based models showed comparable results with the clinical prediction model, with a better performance in specific prognostic risk categories, confirming AI prediction model as a promising tool for the prediction of tooth loss.

Research on accident early warning of metallurgical enterprises based on grey DEMATEL/ISM and Bayesian network

To clarify the complex relationship between the factors causing safety accidents in metallurgical enterprises and predict the risk of accidents in enterprises, a correlation analysis model of the factors causing safety accidents in metallurgical enterprises based on grey Decision-Making Trial and Evaluation Laboratory/Interpretative Structural Modeling (DEMATEL/ISM) was established, and a Bayesian network early warning model was constructed on this basis. The relationship and action path of accident-causing factors in metallurgical enterprises were clarified. The factors were hierarchically divided and a multi-layer hierarchical structure model was established to obtain the neighboring cause, transitional cause, and essential cause of the accident. The results showed that the employee violation rate, the hazardous substances reserves, the toxic gas and dust pollution control compliance rate, the pass rate for equipment maintenance, and the qualification rate of special equipment were the neighboring causes of the accident. The perfection of the safety production management system was the essential cause. The Bayesian network early warning model was applied to the Fuxin Jiuxing Titanium work site. The expected risk probability of an accident was 17.9%, which was in a comparatively safe state (State2). The results obtained by the Bayesian model are consistent with those obtained by AHP and fuzzy comprehensive evaluation method, which proved the accuracy of the early warning model. The Bayesian model can give the risk probability value of the accident and the risk probability value of the accident cause factors at the same time, and include the causal relationship and conditional correlation relationship among the indicator variables in the reasoning process, which can provide targeted technical support for the construction of the emergency system of risk classification management and control.

Predicting Survival of Metastatic Clear Cell Renal Cell Cancer Treated with VEGFR-TKI-Based Sequential Therapy.

(1) Objective: To develop a clinically useful nomogram that may provide a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) metastatic renal cell carcinoma (mRCC) treated with nephrectomy and vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI)-based sequential therapy. (2) Methods: A prospectively maintained database of 145 patients with mRCC treated between 2008 and 2018 was analyzed to predict the CSS of patients receiving sunitinib and second- and third-line therapies according to current standards of practice. A nomogram based on four independent clinical predictors (Eastern Cooperative Oncology Group status, International Metastatic RCC Database Consortium score, the Morphology, Attenuation, Size and Structure criteria and Response Evaluation Criteria in Solid Tumors response criteria) was calculated. The corresponding 1- to 10-year CSS probabilities were then determined from the nomogram. (3) Results: The median age was 60 years (95% CI 57.9-61.4). The disease was metastatic at diagnosis in 59 (40.7%), and 86 (59.3%) developed metastasis during follow-up. Patients were followed for a median 48 (IQR 72; 95% CI 56-75.7) months after first-line VEGFR-TKI initiation. The concordance probability estimator value for the nomogram is 0.778 ± 0.02 (mean ± SE). (4) Conclusions: A nomogram to predict CSS in patients with CC mRCC that incorporates patient status, clinical risk classification and response criteria to first-line VEGFR-TKI at 3 months is presented. This new tool may be useful to clinicians assessing the risk and prognosis of patients with mRCC.

Are protected characteristics associated with mental health care inequalities in the adult UK general population? a cross-sectional study.

This study investigates the association between protected characteristics and inequalities in mental health care in the UK. Multinomial regression was used to model the association between protected characteristics and self-reported distress. Data was extracted from waves 6-10 (2014-2019) of the UK Household Longitudinal Study. Two risk categories were constructed: "undiagnosed distress" referred to a General Health Questionnaire-12 (GHQ-12) score above "caseness" along with no history of mental health diagnosis; "diagnosis without self-report symptoms" referred to a GHQ-12 score consistently below "caseness" within the study time frame but having received a mental health diagnosis. Compared to people without a disability, people with a disability are at considerably greater risk of both undiagnosed distress (Relative risk ratios (RRR) 2.76; Confidence Interval (CI): 2.55, 2.99) and diagnosis without self-reported symptoms (RRR 3.61; CI: 2.80, 4.66). Likewise, women were more likely than men to report undiagnosed distress (RRR = 1.49; CI: 1.38,1.61) or a diagnosis without self-reported symptoms (RRR = 1.38; CI: 1.08, 1.76. Lesbian, gay, and bisexual people are at greater risk of undiagnosed distress compared with heterosexual people (RRR 1.42; CI: 1.19, 1.70). Adults aged 16-24 years were at greatest risk compared to all other age groups. People from a minority ethnic background had a reduced risk of diagnosis without self-report symptoms compared with people from a White ethnic background (RRR 0.34; CI: 0.20, 0.61). Education, employment and income variables moderated some of these associations. This is the first study to examine diagnosis without self-report symptoms alongside undiagnosed distress. Findings suggest that addressing inequality in mental health care requires increased understanding of the needs and strengths within different groups and to provide appropriate forms of social, medical or psychosocial intervention rather than a singular focus on increasing detection, diagnosis and treatment. People with a disability appear to be at greatest disadvantage, requiring greater attention in policy and practice.

Evaluation of social risks in large-scale infrastructure projects in India – a comparative study using relational and traditional approaches

Large-scale infrastructure projects (LSIPs) are characterized by huge costs, high complexity, a high propensity for risks, and a long-lasting impact on the environment and society. While past researchers have extensively studied project risk and associated issues, much less is explored about social risks, a particular category of risks rooted in the social contexts of the LSIPs. The gap is starker in the context of developing countries. To address this gap in knowledge, a network analysis approach is used to explore various social risks and their interdependencies in LSIPs in India. Data was collected from 23 risk management experts working in Indian LSIPs through a matrix-based questionnaire and subsequent interviews. A Social network analysis (SNA) of the social risk interactions identified labor unrest, environmental pollution, and public resistance as the most critical in LSIPs. The SNA-based risk criticality scores were also compared with the traditional probability-impact-based risk perception scores, and divergence of the results was observed. The study concludes by suggesting an actionable social-risk mitigation framework. The findings provide insights on the social risk interaction patterns and emphasize the importance of taking a relational perspective in conjunction with the traditional probability-impact-based view while assessing such risks in LSIPs.

Data-Driven Framework for Classification and Management of Start-Up Risk for High Investment Returns

This research explores the classification of startup risks to achieve high investment returns using Random Forest Regression. The study aims to identify and predict potential risks faced by startups, thereby aiding investors in making informed decisions. We analyzed a dataset comprising various features such as funding levels, market size, expenses, team experience, product development stage, customer satisfaction scores, and revenue streams. We employed a Random Forest Regression model to evaluate the predictive power of these features. The model's performance was assessed using several metrics: Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), Mean Squared Logarithmic Error (MSLE), and Explained Variance Score.The model demonstrated robust predictive capabilities, with an MSE of 0.255, R-squared of 0.9515, MAE of 0.782, MSLE of 0.219, and an Explained Variance Score of 0.915. These results indicate that the model effectively captures the variance in startup risks and predicts them with high accuracy. Feature importance analysis revealed that expenses and funding levels were the most critical factors influencing startup risk classification. The distribution of risks identified 12.4% Strategic Risks, 12.6% Financial Risks, 13.1% Operational Risks, 13.7% Market Risks, and 48.2% of activities with no significant risks.Based on our findings, we recommend that investors focus on key features as outlined in this research when assessing startup risks. By employing the insights provided by our model, investors can better identify high-potential startups, optimize resource allocation, and improve their investment strategies.The Random Forest Regression model offers a reliable tool for predicting and classifying startup risks, providing valuable insights that can enhance investment decision-making and ultimately lead to higher returns.

Analisis Manajemen Risiko Pada Proyek Konstruksi Rehabilitasi Daerah Irigasi Bungkal Kota Sungai Penuh

In implementing construction projects, various obstacles arise from risks which cause the expected quality of work results to not be achieved, therefore it is necessary to implement risk management before implementing the Bungkal Irrigation Area construction project. The risk management used in this research is according to Australia/New Zealand. Standards (1999), risk management is part of a systematic process of identifying, analyzing, evaluating, controlling, supervising and communicating risks related to all activities, functions or processes with the aim of the company being able to minimize losses and maximize opportunities. The implementation of this risk management helps companies identify risks from the start and helps make decisions to overcome these risks. The aim of this thesis research is to identify risks in the rehabilitation of the Bungkal Irrigation Area in Sungai Full City before project implementation, so that they can find out how to control risks. This is to reduce losses that may arise from the risks of this rehabilitation construction activity. This research uses quantitative descriptive methods with interview methods and distributing questionnaires to respondents. The results of this research show that there are 3 risks that fall into the Extreme Risk category out of 32 existing risk indicators, namely: Flooding, first place, lack of depth of normalized soil excavation in irrigation drainage channels, second place, lack of supervision over work implementation, third place. Extreme risk is a risk that requires immediate treatment to reduce the risk, activities must not be carried out or continued. If risks cannot be reduced with limited resources, then the work cannot be completed.

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States.

Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports

BackgroundWe aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports. MethodsWe analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Nonstructured reports were narrative, while structured reports followed standardized formats. We developed ThyroPath, a rule-based Natural Language Processing pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstructured) for evaluation. The pipeline's performance was assessed using both strict and lenient criteria for accuracy, precision, recall, and F1-score; a metric that combines precision and recall evaluation. ResultsIn extraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for structured reports and 90% for unstructured reports, covering 18 thyroid cancer pathology features. In classification tasks, ThyroPath-extracted information demonstrated an overall accuracy of 93% in categorizing reports based on their corresponding guideline-based risk of recurrence: 76.9% for high-risk, 86.8% for intermediate risk, and 100% for both low and very low-risk cases. However, ThyroPath achieved 100% accuracy across all risk categories with human extracted pathology information. ConclusionsThyroPath shows promise in automating the extraction and risk recurrence classification of thyroid pathology reports at large scale. It offers a solution to laborious manual reviews and advancing virtual registries. However, it requires further validation before implementation.