Predictive Power Research Articles

Understanding Consumption Reduction Through the TPB: Moderating Effects of the Need for Evaluation and Self-Referencing Individual Differences

Limited research has focused on consumption reduction as one potential pathway to meet sustainable development goals. This paper investigates consumers’ intentions to undertake consumption reduction through the lens of an extended theory of planned behavior (TPB), where selected individual differences, namely the need for evaluation (NE) and self-referencing (SR), are given considerable attention. In total, 226 respondents participated in this web-based survey study. The results from structural equation modeling analysis confirm that the extended theory of planned behavior effectively explains consumers’ intentions to undertake consumption reduction. Notably, the individual differences of the NE and SR each uniquely moderate the relationships within the TPB model. This study provides a theoretical contribution by integrating the selected moderators (i.e., the NE and SR) into the TPB framework, increases the TPB’s predictive power, and further provides a novel understanding of the underlying influences of individual differences on consumers’ intentions to undertake consumption reduction for the benefit of the environment. Moreover, the findings offer practical implications for policymakers and social marketers in designing tailor-made interventions and consumption reduction strategies by considering the important role of individual differences.

Damped accumulation-based volatility multivariable grey prediction power model and its application

PurposeIn this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.Design/methodology/approachFirstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.FindingsUnder the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.Originality/valueThe main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

Corneal subepithelial nerve fibers in type 2 diabetes: potential biomarker of diabetic neuropathy

AIM: To observe the changes in corneal subepithelial nerve fibers (CNFs) and Langerhans cells (LCs) in patients with type 2 diabetes using corneal laser confocal microscopy (CLCM). METHODS: A total of 60 patients (64 eyes), including 40 patients with type 2 diabetes (DM group) and 20 subjects without diabetes (control group) were included with CLCM. Neuron J plugin of Image J software were used for quantitative analysis of CNF length (CNFL), CNF density (CNFD), corneal nerve branch fiber density (CNBD), main branch length density, branch length density, corneal nerve fiber tortuosity (NT) score, and LCs density. An independent samples t-test to analyze the variability between the two groups was performed, and Pearson correlation analysis was used to analyze the relationships between CNF and multiple biochemical indicators in the DM group. The predictive power of CNF for type 2 diabetes was assessed using the receiver operating characteristic (ROC) curve. RESULTS: There were significant differences in the CNFL, CNFD, and main branch length density between two groups. The results of Pearson correlation analysis showed a significant negative correlation between CNFD and the duration of diabetes as well as triglyceride levels and total cholesterol, and a significant positive correlation between CNFD and serum albumin. In addition, the NT score showed a positive correlation and urea nitrogen, similar to the positive correlation observed between LC density and glycosylated hemoglobin (HbA1c) levels. CNFD showed the highest area under the curve (AUC of ROC) value, followed by main branch length density and CNFL. The AUC of the ROC curve under the logistic regression model also demonstrated good predictive values. The cut-off values of CNFD, CNFL, and main branch length density for diabetes showed 31.25, 18.85, and 12.56, respectively. CONCLUSION: In patients with type 2 diabetes, there is a notable reduction in both CNFL and CNFD. These measurements can be influenced by various blood biochemical factors. However, the compromised nerve fibers can serve as valuable indicators for predicting the onset of type 2 diabetes and also as biomarkers for detecting diabetic neuropathy and its related complications.

Abstract A026: Assessing the impact of obesity on postoperative outcomes in RCC using machine learning

Abstract Introduction Obesity is associated with an increased risk of developing renal cell carcinoma (RCC) but paradoxically correlates with improved outcomes in metastatic cases. Moreover, the data on the relationship between obesity and postoperative outcomes following nephrectomy are inconsistent. Accurate prediction of postoperative outcomes, including complications, 30-day readmissions, and mortality, is essential for improving patient outcomes in surgical procedures. In this study, we utilized ML models to predict such outcomes in RCC patients undergoing nephroureterectomy, radical nephrectomy, partial nephrectomy, and other excision procedures on the kidney using data from the National Surgical Quality Improvement Program (NSQIP; 2016- 2021). Methods A gradient-boosted tree (GBT) ML model was developed and trained to predict the primary outcomes of interest: major complications, minor complications, 30-day readmission, and mortality. Patients with a BMI ≥30 kg/m2 were considered obese, and patients with a BMI <30 kg/m2 were used as controls. The model's performance was rigorously evaluated using various measures, such as AUROC, generalized R-square, and misclassification rate. The model's predictions were validated using separate training (70%) and validation (30%) cohorts to ensure generalizability and applicability in diverse patient populations. Results In the analysis, 36,284 cases were included. Bivariate analysis revealed that obesity was associated with an increased rate of minor complications (5.48% vs. 4.41%, p<0.0001) compared to non-obese individuals with similar major complications (3.33 vs. 3.21, p=0.49), mortality (0.54 vs. 0.64, p=0.22) and 30-day readmission rates (5.74 vs. 5.83, p=0.70). The GBT model demonstrated substantial predictive power across all four outcomes. In predicting mortality, the model achieved an AUROC of 0.80 in the validation set, with a misclassification rate of 0.68%. The prediction of major complications was also robust, with an AUROC of 0.91 in the validation set and a misclassification rate of 2.2%. Similarly, the model's performance in predicting minor complications and 30-day readmissions was strong, with AUROC values of 0.70 and 0.66, respectively, in the validation sets. Conclusions Obesity was linked to a higher rate of minor complications but did not affect other postoperative outcomes in RCC patients undergoing nephrectomy. By integrating a wide range of patient-specific variables, ML models are powerful tools for clinicians to identify high-risk patients and tailor perioperative care accordingly. The interaction between RCC and the surrounding adipose tissue microenvironment may have clinical relevance, warranting further research to elucidate the role of BMI in postoperative complications. Our findings advocate for the expanded use of ML techniques in surgical decision-making, which could enhance patient safety, optimize resource use, and improve overall surgical outcomes. Citation Format: Atulya A. Khosla, Manas Pustake, Sufal Chhabra, Yanjia Zhang, Mukesh Roy, Muni Rubens, Venkataraghavan Ramamoorthy, Anshul Saxena, Ishmael A. Jaiyesimi. Assessing the impact of obesity on postoperative outcomes in RCC using machine learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A026.

The Harmony Index: Evaluating, Predicting, and Visualizing Effectiveness in Multi-Agent Team Dynamics

Team-based games are a keystone pillar of the gaming industry. Sadly, the understanding of team dynamics—and the recommendations for both human and AI-based teammates—are based on a rudimentary understanding of human-AI teaming. We propose a superior metric, which provides information about team effectiveness in an efficient and easily-replicable manner. Without an accurate and effective metric for team evaluation, it is nigh-impossible to provide feedback to players and game designers to improve team balance. We provide such a metric. The Harmony Index, a novel algorithm using real-world data, provides simpler and more accurate actionable directives to improve game design across MOBAs and other game genres. We prove its predictive power in a separate analysis and make recommendations for its use in assessing team effectiveness as well as its future use in additional domains.

Peripheral immune signatures associated with the risk of hepatocarcinogenesis in cirrhotic Egyptian HCV patients before and after treatment with direct-acting antivirals.

Although direct-acting antivirals (DAAs) have revolutionized the management of chronic HCV, the debatable association with hepatocellular carcinoma (HCC) occurrence/recurrence has raised major concerns about their long-term use, especially in cirrhotic cases. The role of epithelial tight junction proteins (TJPs) in hepatocarcinogenesis has been highlighted; however, the association of their expression in peripheral blood mononuclear cells (PBMCs) with HCC has rarely been reported. This study aimed to explore the role of peripheral claudin (Cldn)1 in liver pathogenesis and its crosstalk with soluble immune mediators in HCC prognosis. The study population included six independent subgroups: healthy controls, cirrhotic/non-cirrhotic treatment-naïve HCV patients, DAA-SVR patients, and anticancer treatment-naïve de novo HCC patients. The laboratory tests included serum levels of alpha-fetoprotein (AFP), albumin, liver transaminases, total bilirubin, and CBC profiling. The serum levels of soluble cluster of differentiation (sCD)163, IL-10, and IL-12 were estimated by corresponding ELISA kits, whereas the levels of Cldn1 and transforming growth factor (TGF)-β in PBMCs were quantified using quantitative PCR (qPCR). Serum sCD163, IL-10, and IL-12 levels were significantly higher in the HCC patient group than in the control and non-malignant patient groups (P < 0.0001). No significant difference was detected in the serum levels of the three markers between cirrhotic and non-cirrhotic patients, whereas their levels were significantly different between cirrhotic and non-cirrhotic patients (P < 0.0001). Similarly, the transcriptional levels of peripheral Cldn1 and TGF-β were significantly higher in patients with HCC and non-malignant cirrhosis than in patients without cirrhosis (P = 0.0185-<0.0001 and 0.0089-<0.0001, respectively). Logistic regression analysis revealed a significant association between all the abovementioned markers and HCC (P = 0.0303 to < 0.0001), which was further confirmed by the results of receiver operating characteristic (ROC) analysis, which revealed an area under the curve (AUC) value ranging from 0.883 to 0.996. The calculated cutoff values demonstrated remarkable prognostic capacity, with ranges of 88-99.41% and 82.14-97.92% and positive/negative predictive values ranging from 84.62 to 98.3% and 92-98%, respectively. The proposed HCC predictors are novel non-invasive HCC biomarkers that maintain their predictive power under different pathological conditions and circumvent the drawbacks of conventional prognostic markers in patients with mild cirrhosis and/or normal AFP, albumin, and/or platelet counts.

Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market

Using neural networks in economics time series data is a new unexplored field. Lot of companies and economics research use mostly logistic regression or statistical approach when they try to predict the movement of stock market. Neural networks became a frequent tool for prediction in recent years and this approach has been confirmed to provide more reliable and better solutions when it comes to prediction and accuracy power. Within a wider context of current debate on neural networks employment in stock market predictions, we suggest an innovative methodology based on the combination of neural networks. In our analysis we use Wasserstein Generative Adversarial Network (WGAN) on Germany stock market as an example. We present how the trading strategy could be established on the prediction of the model and how it can be compared with other models in terms of returns. Overall, the WGAN monthly prediction outperformed Random Forest by 36%, benchmark by 32% and LSTM by 26% in the testing period. Our results also suggest that the WGAN model has on average higher returns than pure investment into index. Furthermore, WGAN is less volatile, which is always the preferred option for investors. Using neural networks for stock index prediction and confirming that WGAN investment strategy brings higher returns compared to generally used models is our main contribution to the current debate.

Predictive factors of extremity graft occlusion after endovascular repair of abdominal aortic aneurysm: A 13-year single center experience

Aim: Graft leg thrombosis (GLT) after Endovascular Aortic Repair (EVAR) is an important problem affecting morbidity/mortality. In addition to anatomical factors such as aortic neck angle and diameter in graft leg thrombosis; we aimed to convey the roles of graft extension to the external iliac artery and the effect of graft type selection on the results. Material and Methods: Analysis of 512 patients who underwent EVAR between 2010 and 2023 was performed. The effects of anatomical factors, graft material, underlying diseases, age and gender on the development of graft leg thrombosis were evaluated. Anatomical measurements were evaluated in terms of their predictive power for graft leg thrombosis by Receiver Operating Characteristic (ROC) analysis. Results: The prevalence of graft leg occlusion was significantly associated with some anatomical factors, especially increased aortic neck angle (OR=1.07, p&lt;0.001) and descent to the external iliac artery (OR=13.43, p&lt;0.001). Poliytetrafluoroethylene (PTFE) grafts were associated with a reduced risk of graft leg occlusion (OR=0.26, p=0.002). ROC analysis showed that aortic neck angle had the highest predictive accuracy for GLT (AUC=0.817, p&lt;0.001). There were no significant differences in age, gender, smoking status, diabetes, and hypertension as comorbidities. Peripheral artery disease was shown to increase the risk. Conclusion: Our findings highlight the role of aortic anatomy and graft material selection in the risk of graft leg thrombosis. The use of PTFE grafts appears to be protective against graft leg thrombosis. This information highlights the importance of personalized surgical planning and postoperative management to optimize patient outcomes. Future research should focus on developing models that include these factors.

Smartphone tests quantify lower extremities dysfunction in multiple sclerosis

IntroductionIncreasing shortage of neurologists compounded by the global aging of the population have translated into suboptimal care of patients with chronic neurological diseases. While some patients might benefit from expanding telemedicine, monitoring neurological disability via telemedicine is challenging. Smartphone technologies represent an attractive tool for remote, self-administered neurological assessment. To address this need, we have developed a suite of smartphone tests, called neurological functional test suite (NeuFun-TS), designed to replicate traditional neurological examination. The aim of this study was to assess the ability of two NeuFun-TS tests—short walk and foot tapping—to quantify motor functions of lower extremities as assessed by a neurologist.MethodsA cohort of 108 multiple sclerosis (MS) patients received a full neurological examination, imaging of the brain, and completed the NeuFun-TS smartphone tests. The neurological exam was digitalized using the NeurEx™ platform, providing calculation of traditional disability scales, as well as quantification of lower extremities-specific disability. We assessed unilateral correlations of 28 digital biomarkers generated from the NeuFun-TS tests with disability and MRI outcomes and developed machine learning models that predict physical disability. Model performance was tested in an independent validation cohort.ResultsNeuFun-TS-derived digital biomarkers correlated strongly with traditional outcomes related to gait and lower extremities functions (e.g., Spearman ρ &amp;gt; 0.8). As expected, the correlation with global disability outcomes was weaker, but still highly significant (e.g., ρ 0.46–0.65; p &amp;lt; 0.001 for EDSS). Digital biomarkers also correlated with semi-quantitative imaging outcomes capturing locations that can affect lower extremity functions (e.g., ρ ~ 0.4 for atrophy of medulla). Reliable digital outcomes with high test-retest values showed stronger correlation with disability outcomes. Combining strong, reliable digital features using machine learning resulted in models that outperformed predictive power of best individual digital biomarkers in an independent validation cohort.DiscussionNeuFun-TS tests provide reliable digital biomarkers of lower extremity motor functions.

Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era

ABSTRACTThis study investigates the forecasting power of three well‐established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short‐term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out‐of‐sample forecasting analysis specifically targets the period characterized by the short‐term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short‐term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.

Triglyceride-glucose index and hsCRP-to-albumin ratio as predictors of major adverse cardiovascular events in STEMI patients with hypertension.

This study aimed to evaluate the association between the triglyceride-glucose (TyG) index and high-sensitivity C-reactive protein-to-albumin ratio (hsCAR) and the prognosis of patients with STEMI and hypertension. A total of 699 patients diagnosed with STEMI and hypertension were included in this study database. Compared to the low TyG index group (< 7.8), the high TyG index group (≥ 7.8) was associated with an increased risk of MACE (HR 2.09, 95% CI = 1.58-2.77; P < 0.001). Similarly, a higher hsCAR (≥ 0.15) was linked to an increased risk of MACE (HR 1.46, 95% CI = 1.12-1.90; P = 0.005). Subsequently, we categorized the population into four groups based on the defined cutoff points. Compared to the low TyG-low hsCAR subgroup, the other three subgroups demonstrated an elevated risk of MACE. Among patients treated with PCSK9 and SGLT2 inhibitors, the combined effect of the TyG index and hsCAR on MACE was attenuated. Finally, The combined TyG index and hsCAR model exhibited optimal performance (AUC = 0.71, 95% CI = 0.67-0.75; P < 0.001). This study demonstrates that the TyG index and hsCAR provide strong combined predictive power. The synergistic utilization offers a comprehensive approach to cardiovascular risk assessment.

Do Time-Dependent Repeated Measures of Anthropometric and Body Composition Indices Improve the Prediction of Incident Diabetes in the Cohort Study? Findings from a Community-Based Korean Genome and Epidemiology Study.

Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell's C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell's C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.

Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.

Improving computational models of deep brain stimulation through experimental calibration.

Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight. However, their predictive power remains constrained by model uncertainties and a lack of validation and calibration. Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy in vitro before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on in vivo impedance spectroscopy measurements. The standard deviation of the volume of tissue activated across the 18 characterized electrodes was approximately 32.93%, underscoring the importance of electrode characterization. Thus, the workflow significantly enhances the model predictions' credibility of neural activation exemplified in a rodent model. Computational models are frequently employed without validation or calibration, relying instead on manufacturers' specifications. Our approach provides an accessible method to obtain a validated and calibrated electrode geometry, which significantly enhances the reliability of the computational model that relies on this electrode. By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.

ENTREPRENEURIAL ORIENTATION AND SME SUCCESS: UNVEILING THE MEDIATING IMPACT OF AMBIDEXTERITY

This research seeks to assess how ambidexterity in exploration and exploitation mediates the relationship between entrepreneurial orientation and the performance of small and medium-sized enterprises (SMEs). Specifically, this study investigates the indirect influence of entrepreneurial orientation on business performance through the mediating roles of ambidexterity exploration and exploitation. A quantitative approach using structural equation modeling (SEM) is employed to examine the relationships among these variables. The research sample comprises 200 SMEs operating primarily in East Java, Indonesia. The findings demonstrate a significant mediating effect (p&lt;0.05) of ambidexterity exploration and exploitation, which enhances the influence of entrepreneurial orientation on business performance. This indirect effect proves to be stronger than the direct impact of entrepreneurial orientation itself. The structural model shows a high R-square value of 68.6%, indicating a strong predictive capability. Additionally, the goodness-of-fit index reflects high model suitability (0.543 &gt; 0.36), and the predictive power of the PLS model is considered medium. The study’s results underscore the importance of fostering both ambidexterity in exploration and exploitation alongside entrepreneurial orientation to optimize SME performance.

Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms

The GATIS score, developed by Zeng et al , represents a significant advancement in predicting the prognosis of patients with rectal neuroendocrine neoplasms (R-NENs). This study, which included 1408 patients from 17 major medical centres in China over 12 years, introduces a novel prognostic model based on the tumour grade, T stage, tumour size, age, and the prognostic nutritional index. Compared with traditional methods such as the World Health Organization classification and TNM staging systems, the GATIS score has superior predictive power for overall survival and progression-free survival. With a C-index of 0.915 in the training set and 0.812 in the external validation set, the GATIS score’s robustness and reliability are evident. The study’s use of a large, multi-centre cohort and rigorous validation processes underscore its significance. The GATIS score offers clinicians a powerful tool to accurately predict patient outcomes, guide treatment decisions, and improve follow-up strategies. This development represents a crucial step forwards in the management of R-NENs, addressing the complexity and variability of these tumours and setting a new benchmark for future research and clinical practice.

Capital mobility, financial development and sustainable trade development in sub-Saharan Africa: a GMM-PVAR based analysis

PurposeThe existing literature on the effects of capital mobility and financial development on sustainable trade development in sub-Saharan African (SSA) countries has been centered on production-based carbon emissions without investigating consumption-based or trade-adjusted carbon emissions. The purpose of this paper is to examine the effects of capital mobility and financial development on sustainable trade development, specifically trade-adjusted carbon emissions in SSA economies.Design/methodology/approachThe study employed the novel GMM-PVAR estimator and the Drisc-Kraay fixed effect panel corrected standard error (PCSE) dynamic ordinary least squares (DOLS) and the fully modified least squares (FMOLS) approaches on panel data from 46 sub-Saharan African (SSA) countries over the period 1992–2021.FindingsThe study established that capital mobility has a significant positive effect on sustainable trade development in SSA in the long run. Further, the empirical results reveal that the link between financial development and sustainable trade development is significantly positive in the long run. Moreover, the results suggest that capital mobility and financial development have predictive power on sustainable trade development.Practical implicationsThe findings of the study imply that policymakers ought to pay equal attention to capital mobility and financial development when developing sustainable trade development policies.Originality/valueThe existing literature has been centered on production-based carbon emissions, without specifically considering sustainable trade development (consumption-based carbon emissions). To the best of our knowledge, this is the first study to examine the effect of capital mobility and financial development on sustainable trade development in SSA countries context.

Beyond Competitiveness: Is Productivity Everything?

ABSTRACTThis study examines and compares the predictive power of competitiveness, approximated by the Global Competitiveness Index (GCI), and productivity for economic growth. Using a panel dataset of 62 countries over the period 2007–2019 and the local projections (LP) approach, we find that both higher productivity and higher GCI contribute to higher economic growth over the next 5 years. However, productivity has a stronger predictive power for economic growth than the GCI. We find evidence that this divergence between productivity and GCI could be explained by ‘productivity beyond competitiveness’: As economies develop and converge towards the world technological frontier, the GCI, unlike productivity, no longer captures the key determinants of economic growth. We then examine the determinants of ‘productivity beyond competitiveness’ that can help economies increase their productivity, and by extension growth, even as they approach the technological frontier. We find that higher financial development, institutional quality, as well as some cultural traits explain the divergence between productivity and competitiveness in advanced economies.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

EGS SO13 - Evaluation of Different Surgical Risk Prediction Models in Predicting Morbidity and Mortality in Giant Hiatus and Diaphragmatic Hernia Repairs

Abstract Background Emergency giant hiatus hernia and diaphragmatic hernia repairs are associated with high rates of mortality and morbidity; different studies reported variable outcomes. Accurate risk assessment empowers patients to provide informed consent and also serves as a valuable tool for identifying individuals who may require enhanced postoperative care, such as respiratory support and critical care admission. Various risk prediction tools have been developed to assist clinicians in making decisions to aid in stratifying the risk of surgical management. We aimed to assess the effectiveness of different risk prediction models in predicting mortality and morbidity in this patient cohort. Method Retrospective analysis performed on cases between May, 2010 and January, 2021 in a tertiary adult upper gastro-intestinal centre. The outcomes from the operations were assessed and compared with the predicted morbidity and mortality for different risk prediction models. Including the Surgical Outcome Risk Tool (SORT), the National Emergency Laparotomy Audit (NELA), parsimonious NELA and the American College of Surgeons National Surgical Quality Improvement Programme (ACS-NSQIP). The models were compared using the area under the curve (AUC). Morbidity models included Portsmouth Physiological and Operative Severity Score (P-POSSUM) and the ACS-NSQIP. Morbidity was evaluated by calculating the comprehensive complication index (CCI). Results A total of 108 patients met the criteria and were included in the analysis. Among these 50.9% were females. The median age was 69 (IQR 59-78). The 30-day mortality rate was 6.93% (7 patients), six patients died during the same admission, and one patient died during a re-admission. ACS-NQSIP had the highest predictive power for mortality (AUC = 0.6344), in comparison to NELA (AUC=0.5665), parsimonious NELA (AUC=0.4866) and SORT (AUC = 0.4342). Both ACS-NSQIP and P-POSSUM showed moderate correlation to CCI (rho = 0.489, p&amp;lt;0.001 and 0.446, p&amp;lt;0.001 respectively). Conclusion ACS-NSQIP is a better predictor of both mortality and morbidity in emergency giant hiatus and diaphragmatic hernia repairs when compared to NELA, parsimonious NELA, P-POSSUM and SORT. Interestingly, parsimonious NELA had a lower prediction rate when compared with the older NELA model. ACS-NSQIP may have a role in pre-assessment and consenting of emergency hiatus and diaphragmatic hernia repairs. Multi-centre prospective studies could be used to validate these findings.