Set Of Values Research Articles

Enhancing deep-seated hepatocellular carcinoma detection: assessing the added value of high mechanical index setting in sonazoid-based contrast-enhanced ultrasound during post-vascular phase.

This retrospective study aimed to investigate the role of an additional high mechanical index (MI) setting scan during the post-vascular phase (PVP) in detecting deep-seated hepatocellular carcinoma (HCC) lesions. A total of 805 confirmed HCCs, which underwent Sonazoid-based contrast-enhanced ultrasound (CEUS) between January 2014 and October 2021, were included. Low MI scan was initially employed for lesion detection during the PVP, followed by high MI scan. Propensity score matching (PSM) was utilized to address confounding variables. Of the 805 study lesions, 668 were detected as perfusion defects at the initial low MI setting, while 137 remained undetected. Among these 137 undetected lesions, 77 were identified at the subsequent high MI setting, whereas 60 remained undetected. Lesions that were larger (18.69±11.27mm vs. 16.55±7.42mm, p = 0.006), more superficial (6.06±2.41cm vs. 7.40±2.74cm, p < 0.001), and hypoechoic (482/668 vs. 62/137, p < 0.001) were detectable at the initial low MI setting. Male patients benefited more from the additional high MI scan (63/97 vs. 14/40, p < 0.001). Lesions identified with additional high MI were larger (18.30±8.76mm vs. 14.30±4.34mm, p < 0.001) and deeper than undetected ones (8.48±2.48cm vs. 6.02±2.43cm, p < 0.001). After PSM, depth was shown to be an independent predictor in multivariate analysis (odds ratio: 1.557, 95% confidence interval: 1.249-1.941). The depth cutoff was 7.75 cm, with a sensitivity of 0.681, specificity of 0.851, and area under the curve of 0.774. The additional high MI setting scan during the PVP of Sonazoid-based CEUS leads to enhanced detection of deep-seated HCCs.

Dynamic screening with liquidity constraints

AbstractWe consider a dynamic screening model with serially independent types where the agent is short-term liquidity constrained. We model a liquidity constraint as a hard constraint that forces the agent to renege whenever he would suffer a loss from fulfilling the contract terms in a given period. In particular, the violation of a liquidity constraint is a verifiable event that future contract terms can condition on. This verifiability leads to less stringent truth-telling constraints than those considered in the existing literature. We show that the weaker constraints do not affect optimal contracting, however. Moreover, we develop a novel method to study private values settings with continuous types and show that a regularity condition that has analogues in the literature on multi-dimensional screening ensures that the optimal contract is deterministic.

Levels of mandatory CSR compliance and the payoff: evidence from India

Purpose This paper aims to examine the diverse levels of corporate social responsibility (CSR) expenditure among Indian companies and its influence on their performance. The study aims to determine whether exceeding the mandatory CSR spending limit provides an edge to companies that outperform in enhancing corporate firm value. Design/methodology/approach A dynamic model using system generalized method of moments (GMM) was used to analyze a balanced panel data set of 191 firms over seven years, spanning from 2016 to 2022. Return on assets was used as a proxy to gauge financial performance. At the same time, the study also examined the robustness of the results by considering return on equity and Tobin’s Q as additional measures. Findings The study results indicate that, in a mandatory CSR setting, all companies are generally perceived as performing and reporting on CSR equally. Hence, it will not make any payoff, although few companies outperform. Therefore, companies should differentiate themselves regarding CSR spending and reporting to claim a competitive advantage in the market. The study also suggests that the payoff of mandatory CSR expenditure for both performing and outperforming companies is reflected more in non-quantifiable firm characteristics than in measurable performance metrics. Research limitations/implications The period of study covers 7 years, i.e. 2015–2016 to 2022–2023. This may limit capturing long-term CSR practices and firm performance trends. Additionally, data from only 191 Indian companies restrict generalizability; future research should include diverse geographic regions with mandated CSR spending to provide a more comprehensive view. In subsequent studies, contextual factors like regulatory changes and macroeconomic conditions could be considered moderating variables. Practical implications The study provides valuable insights to top management, indicating that spending beyond the threshold limit of mandatory CSR spending does not enhance corporate firm value. Instead, this additional investment may yield benefits in the form of goodwill and reputation over the long term. Social implications This study assists corporations in optimizing their CSR strategies to enhance their social and financial performance impact. Moreover, the study suggests ways to improve the CSR payoff and the need for increasing stakeholder satisfaction. Originality/value The study provides original insights into the relationship between mandatory CSR spending and firm performance in the Indian context, revealing that CSR spending does not significantly impact financial metrics, and it highlights the importance of considering a non-quantitative matrix to enhance the firm value in a mandatory CSR setting.

On the Finkelberg–Ginzburg Mirabolic Monodromy Conjecture

Abstract We compute the monodromy of the mirabolic $\mathcal{D}$-module for all values of the parameters $(\vartheta ,c)$ in rank 1 and outside an explicit codimension 2 set of values in ranks 2 and higher. This shows in particular that the Finkelberg–Ginzburg conjecture, which is known to hold for generic values of $(\vartheta ,c)$, fails at special values even in rank 1. Our main tools are Opdam’s shift operators and intertwiners for the extended affine Weyl group, which allow for the resolution of resonances outside the codimension two set.

Comparison of EQ-5D-Y-3L Utility Scores Using Nine Country-Specific Value Sets in Chinese Adolescents.

This study aimed to assess and compare the measurement properties of EQ-5D-Y-3L utilities derived from available countries' value sets (Chinese, Japanese, Slovenian, German, Spanish, Hungarian, Netherlandish, Belgian, and Indonesian), among Chinese adolescents. From July to September 2021, a large-scale cross-sectional survey was administered across 16 cities in Shandong, China, with the objective of assessing the health status of junior high school students aged 10-18 years. Supported by the educational authorities, quick response (QR) codes and questionnaire links were disseminated to schools. A total of 97,413 junior high school students completed the questionnaire. Agreement, convergent validity, and known-group validity were determined in the nine country-specific value sets. The Indonesian value set demonstrated the highest mean health utility score (0.970), followed by the Japanese (0.961), Chinese (0.960), Netherlandish (0.948), Hungarian (0.942), German (0.938), Belgian (0.932), Slovenian (0.926), and Spanish (0.926) value sets, respectively. The utility scores derived from Asian value sets were higher than those from Europe. Good or excellent agreements (intraclass correlation coefficients >0.7) were found between each paired value set. In Bland-Altman plots, the 95% limits of agreement for any two value sets were 0.046-0.348. A strong relationship (Spearman's correlation coefficients >0.99) between any two value sets was found. The EQ-5D-Y-3L utility scores discriminated equally well for the nine value sets across three known groups. The effect size and the relative efficiency statistics showed the Chinese value sets were more sensitive in general. Referring to the Chinese value set, all the relative efficiency values in each value set were similar across three known groups, ranging from 0.9 to 1.0. A total of nine country-specific EQ-5D-Y-3L value sets showed an overall high level of agreement, strong correlation, and good known-group validity. However, the utility scores derived from nine EQ-5D-Y-3L value sets were different and the country-specific value sets were not interchangeable.

Simulation and Analysis of the Loading, Relaxation, and Recovery Behavior of Polyethylene and Its Pipes

Spring–dashpot models have long been used to simulate the mechanical behavior of polymers, but their usefulness is limited because multiple model parameter values can reproduce the experimental data. In view of this limitation, this study explores the possibility of improving uniqueness of parameter values so that the parameters can be used to establish the relationship between deformation and microstructural changes. An approach was developed based on stress during the loading, relaxation, and recovery of polyethylene. In total, 1000 sets of parameter values were determined for fitting the data from the relaxation stages with a discrepancy within 0.08 MPa. Despite a small discrepancy, the 1000 sets showed a wide range of variation, but one model parameter, σv,L0, followed two distinct paths rather than random distribution. The five selected sets of parameter values with discrepancies below 0.04 MPa were found to be highly consistent, except for the characteristic relaxation time. Therefore, this study concludes that the uniqueness of model parameter values can be improved to characterize the mechanical behavior of polyethylene. This approach then determined the quasi-static stress of four polyethylene pipes, which showed that these pipes had very close quasi-static stress. This indicates that the uniqueness of the parameter values can be improved for the spring–dashpot model, enabling further study using spring–dashpot models to characterize polyethylene’s microstructural changes during deformation.

Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.

This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models-VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2-were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy.

IMPACT OF OECD MEMBERSHIP ON ROMANIA'S FINANCIAL SERVICES AND CORPORATE GOVERNANCE

significant influence would be the one on the financial services and corporate governance of the companies. Romania will see an increased market efficiency, more foreign investment, and greater customer protection in the key sectors including non-banking financial sectors. Therefore, with OECD membership, a more predictable economic environment would eventually render Romania an attractive destination for investment. Reciprocally, it will help in economic growth, reduced market volatility, and trust in the corporate sector. In short, higher standards of corporate governance and financial services will have Romania stand in a better position in the global chain and integrate into global markets in a much better way, thereby increasing its economic resilience. Romania already confirmed several times the attachment to the set of values and principles of the OECD. Romania also promotes its profile as a candidate involved in the debates at the OECD level but also active in the projects developed and implemented by the OECD through the added value it can bring to the Organization at the regional level.

Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data

BackgroundMost epithelial ovarian cancer (EOC) eventually develops recurrence. Identification of high-risk patients can prompt earlier intervention and improve long-term outcomes. We used laboratory and clinical data to create models based on machine learning for EOC platinum resistance recurrence identification.MethodsThis study was designed as a retrospective cohort analysis. Initially, we identified 1,392 patients diagnosed with epithelial ovarian cancer who underwent platinum-based chemotherapy at Yunnan Cancer Hospital between January 1, 2012, and June 30, 2022. We collected data on the patients’ clinicopathologic characteristics, routine laboratory results, surgical information, details of chemotherapy regimens, and survival outcomes. Subsequently, to identify relevant variables influencing the recurrence of platinum resistance, we screened thirty potential factors using two distinct variable selection methods: Lasso regression and multiple logistic regression analysis. Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. These included decision tree analysis (DTA), K-Nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost). The performance of these models was compared against that of traditional logistic regression. To ensure robust internal validation and facilitate comparison among model performance metrics, a five-fold cross-validation method was implemented. Key performance indicators for the models included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and average accuracy. Finally, we will visualize these models through nomograms, decision tree diagrams, variable importance plots, etc., to assist clinicians in their practice.ResultsMultiple logistic regression analysis identified eight variables associated with platinum resistance recurrence. In the lasso regression, seven variables were selected. Based on the findings from both Lasso regression and multiple logistic regression analysis, models were developed using these 7 and 8 factors. Among these, the XGBoost model derived from multiple logistic regression exhibited superior performance and demonstrated good discrimination during internal validation, achieving an AUC of 0.784, a sensitivity of 0.735, a specificity of 0.713, an average accuracy of 80.4%, with a cut-off value set at 0.240. Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development.ConclusionsWe have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model—derived from variables selected through multiple logistic regression—demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation.

NIR spectroscopy prediction model for capsaicin content estimation in chilli: A rapid mining tool for trait-specific germplasm screening

Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQexternal, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQexternal value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample t-test p-value of 0.757 (p > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.

Extending the Scope of Instrumental Variable Methods

Abstract Since Philip Wright’s (1928) proposal of the linear instrumental variables (IV) estimator the scope of application of IV models has greatly expanded. There are now nonlinear IV models in use with both additive and nonadditive latent variables, employing both semiparametric and nonparametric specifications. A notable feature of Wright’s linear IV estimator is that it is applicable to incomplete models that leave the genesis of endogenous explanatory variables unspecified. On the other hand, inversion of the linear model enables the value of unobservable heterogeneity to be expressed as a function of observable variables. Only recently has the application of incomplete IV models been extended to cases in which this inversion produces a non-singleton set of values of unobservable variables. This situation arises in many models such as those featuring discrete choice, counts, or censored outcomes, or when there are multiple sources of heterogeneity as in random coefficient models, or when economic considerations result in inequality restrictions on values taken by observed and unobserved variables. This paper provides an introduction to the methods set out for analyzing such generalized instrumental variable (GIV) models in Chesher and Rosen (2017). An illustrative example is provided in which an outcome is determined by a random coefficients linear model with an endogenous explanatory variable and instrumental variable exclusion and independence restrictions.

Optimized Machine Learning Algorithms for Predicting the Punching Shear Resistance of Flat Slabs with Transverse Reinforcement

In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one of the most critical factors. It is true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating the punching shear resistance. Four machine learning (ML) algorithms (SVR, DT, RF, and XGBoost) with Bayesian optimization (BO) are presented in this paper to provide accurate predictions for flat slabs. The adoptability and optimization of the models are achieved through the analysis of a database of 337 test specimens with nine design parameters. Machine learning (ML) techniques are used to estimate punching shear resistance, which is compared with design provisions and equations relating to critical shear crack theory (CSCT). According to this study, Bayesian optimization is still capable of improving the performance of conventional machine learning algorithms, while the XGBoost-based model offers advanced capabilities. Predictions based on BO-XGBoost are in good agreement with actual values (MAE, RMSE, and R2 are 0.09 MN, 0.14 MN, and 0.92, respectively) in test set. Following a detailed explanation using Shapley additive explanation (SHAP), a high-performance ML approach is used to investigate the predictive results. With the proposed optimized algorithms, it is possible to determine the punching shear resistance of flat slabs with transverse reinforcement during the preliminary stages of the construction.

Unsupervised Decision Trees for Axis Unimodal Clustering

The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a unimodality test. The proposed method follows the typical top-down splitting paradigm for building axis-aligned decision trees and aims to partition the initial dataset into axis unimodal clusters by applying thresholding on multimodal features. To determine the decision rule at each node, we propose a criterion that combines unimodality and separation. The method automatically terminates when all clusters are axis unimodal. Unlike typical decision tree methods, DTAUC does not require user-defined hyperparameters, such as maximum tree depth or the minimum number of points per leaf, except for the significance level of the unimodality test. Comparative experimental results on various synthetic and real datasets indicate the effectiveness of our method.

Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.

This study aimed to develop utility weights for the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument, tailored to the Norwegian and Swedish populations. The utility weights are intended for use in the specific welfare contexts of Norway and Sweden to support more precise healthcare decision-making in cancer treatment and care. This cross-sectional study included 1019 Norwegian and 1048 Swedish participants representative in age and gender of the two general populations. Participants completed a discrete choice experiment involving 960 choice sets, each consisting of two EORTC QLU-C10D health states described by the instrument's domains and the duration of each state. Utility weights were calculated using generalized estimation equation models, and non-monotonic levels were merged to ensure consistent valuation. In the Norwegian participants, the largest utility decrements were seen for the domain of physical functioning (decrement of -0.263 for highest level "very much"), followed by pain (decrement -0.205 for level "very much") and role functioning (-0.139). Among the cancer-specific domains, nausea had the largest utility decrement (-0.124). In the Swedish participants, the largest utility decrements were also observed for physical functioning (-0.207 for the response "very much"), followed by pain (-0.139), role functioning (-0.133), and nausea (-0.119). Emotional functioning also exhibited a sizable utility decrement (-0.115). This study provides the first set of utility weights for the EORTC QLU-C10D specific to Norway and Sweden, reflecting the unique health preferences of these populations. The generated utility decrements can inform cost-utility analyses and optimize resource allocation in cancer care within the Norwegian and Swedish healthcare systems.

Flower scent, pollinators, and effects of proximity between plants on fruit set of the rare, threatened, and endemic Daphne rodriguezii

AbstractThis study focuses on Daphne rodriguezii, a threatened plant species that is endemic to Menorca in the Balearic Islands of the Western Mediterranean. The main objective was to investigate the volatile organic compounds (VOCs) in the floral aroma, as well as the timing and chemical variations in scent production. The primary pollinators were identified, and the relationship between their behavior and the chemical profile of the floral scent was investigated. Furthermore, the impact of plant density on fruit set rates was evaluated to obtain valuable insights for conservation and habitat‐management initiatives. The findings reveal that D. rodriguezii emits a distinct sweet floral aroma that primarily consists of benzenoids and linalool derivatives. Scent emission occurs during the evening and night, coinciding with the activity period of moths (Autographa gamma), which were identified as the primary pollen vectors for this self‐incompatible species. The fruit set values exhibit a positive correlation with the proximity between plants. These results emphasize the importance of moths and plant proximity as crucial factors to consider in the conservation management of this species.

In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients.

Hospital re-admissions in heart failure (HF) patients are mostly caused by an acute exacerbation of their chronic congestion. Bioimpedance analysis (BIA) has emerged as a promising non-invasive method to assess the volume status in HF. However, its correlation with clinically assessed volume status and its prognostic value in the acute intra-hospital setting remains uncertain. In this single-center observational study, patients (n = 49) admitted to the cardiology ward for acute decompensated HF (ADHF) underwent a daily BIA-derived volume status assessment. Median hospital stay was 7 (4-10) days. Twenty patients (40%) reached the composite endpoint of cardiovascular mortality or re-hospitalization for HF over 6months. Patients at discharge displayed improved NYHA class, lower body weight, plasma and blood volume, as well as lower NT-proBNP levels compared to the admission. Compared to patients with total body water (TBW) less than or equal to that predicted by body weight, those with higher relative TBW levels had elevated NT-proBNP and E/e´ (both p < 0.05) at discharge. In the Cox multivariate regression analysis, the BIA-derived delta TBW between admission and discharge showed a 23% risk reduction for each unit increase (HR = 0.776; CI 0.67-0.89; p = 0.0006). In line with this finding, TBW at admission had the highest prediction importance of the combined endpoint for a subgroup of high-risk HF patients (n = 35) in a neural network analysis. In ADHF patients, BIA-derived TBW is associated with the increased risk of HF hospitalization or cardiovascular death over 6months. The role of BIA for prognostic stratification merits further investigation.

Finite-time synchronization of complex dynamic networks with impulsive control and actuator saturation: An LMI approach

This paper investigates the finite-time synchronization (FTS) of complex dynamic networks (CDNs) with impulsive control subject to actuator saturation. We develop a novel method that integrates the impulsive comparison principle and improved convex hull representation lemma of saturated function to derive LMI-based sufficient conditions for FTS, which are easy to verify. We also estimate the admissible set of initial values to ensure the validity of the lemma. Unlike previous work, we relax the monotonicity assumption on the Lyapunov function, which broadens our results’ applicability. Moreover, we formulate and solve an optimization problem to enlarge the admissible set under the LMI constraints, using YALMIP toolbox in MATLAB software. Finally, several numerical examples are proposed to demonstrate our results’ effectiveness and superiority.

Global and local identifiability analysis of a nonlinear biphasic constitutive model in confined compression.

Application of biomechanical models relies on model parameters estimated from experimental data. Parameter non-identifiability, when the same model output can be produced by many sets of parameter values, introduces severe errors yet has received relatively little attention in biomechanics and is subtle enough to remain unnoticed in the absence of deliberate verification. The present work develops a global identifiability analysis method in which cluster analysis and singular value decomposition are applied to vectors of parameter-output variable correlation coefficients. This method provides a visual representation of which specific experimental design elements are beneficial or harmful in terms of parameter identifiability, supporting the correction of deficiencies in the test protocol prior to testing physical specimens. The method was applied to a representative nonlinear biphasic model for cartilaginous tissue, demonstrating that confined compression data does not provide identifiability for the biphasic model parameters. This result was confirmed by two independent analyses: local analysis of the Hessian of a sum-of-squares error cost function and observation of the behaviour of two optimization algorithms. Therefore, confined compression data are insufficient for the calibration of general-purpose biphasic models. Identifiability analysis by these or other methods is strongly recommended when planning future experiments.

Out of the Hospital, Out of the Box: Utilizing the CNS Role in Home Healthcare Settings.

The United States has poorer population health outcomes compared to similar developed nations despite higher healthcare spending. Multiple factors contribute to this problem, including high healthcare costs, insurance coverage limitations, and a lack of focus on preventive care and wellness. Clinical Nurse Specialists (CNSs), primarily known for their impact in acute care settings, increasingly demonstrate their value in outpatient settings like home healthcare. CNSs possess graduate-level training, equipping them with the skills and expertise to develop and implement strategies that improve population health outcomes in home health settings. CNSs provide clinical leadership and support to staff while remaining at the forefront of ensuring the organization delivers high-quality patient care. Understanding the specific skills and competencies of the CNS to support the patient, family, staff, and organization is necessary to improve outcomes for patients in outpatient settings. The CNS delivers care through direct patient care, supporting nurses and nursing practice, and improving organizational systems. This unique skill set allows CNSs to influence home healthcare outcomes across various levels of care positively. This article sheds light on how CNSs enhance patient outcomes in outpatient settings like home healthcare.

Risk Factors Analysis and Nomogram Conduction for Major Adverse Events After Lumbar Fusion Surgery in Older Patients: A Prospective Cohort Study

This study aimed to identify risk factors for major adverse events (AEs) after lumbar fusion surgery in patients aged 75 and over and evaluate the role of several common geriatric comprehensive assessment items in predicting postoperative major AEs. This is a prospective cohort study of patients aged ≥75 years who underwent open lumbar fusion for degenerative spine disease from August 2019 to August 2022. The primary outcome measure was the incidence of major postoperative AEs within 90 days after surgery. Clavien-Dindo III-IV complications and unplanned readmission were defined as major AEs. Patients' characteristics, laboratory tests, assessment results, and surgery-related variables were compared between the major AEs and the non-major AEs groups. Multivariable logistic regression analysis was used to identify independent risk factors for major AEs. The logistic regression model was evaluated in another prospective cohort of patients from October 2022 to October 2023. A total of 301 patients (mean [SD] age, 79.7 [3.5] years; 60.5% male) were included in the study. Five features, including female (OR 1.99, p = 0.040), higher body mass index (BMI) (OR 1.090, p = 0.024), frailty (OR 2.043, p = 0.032), hypoalbuminemia (OR 2.489, p = 0.040), and higher Charlson comorbidity index (CCI) (OR 1.397, p = 0.024), were independently associated with major AEs and were selected to develop a predictive nomogram of major AEs. The area under the curve values for the development set and validation set were 0.75 and 0.71, respectively. Preoperative frailty, hypoalbuminemia, female sex, greater BMI, and higher CCI grade were risk factors for 90-day major AEs after lumbar fusion surgery in older patients. The predictive nomogram that we developed using this data can enhance preoperative risk counseling and optimization for older patients.