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Background/Objectives: Several population pharmacokinetic models of vedolizumab (VDZ) are available for inflammatory bowel disease (IBD) patients. However, their predictive performance in real-world clinical settings remains unknown. This study aims to externally evaluate the published VDZ pharmacokinetic models, focusing on their predictive performance and simulation-based clinical applicability. Methods: A literature search was conducted through PubMed to identify VDZ population pharmacokinetic models. A total of 114 VDZ concentrations from 106 IBD patients treated at the University Medical Center "Zvezdara", Republic of Serbia, served as the external evaluation cohort. The predictive performance of the models was assessed using prediction- and simulation-based diagnostics. Furthermore, the models were utilized for Monte Carlo simulations to generate concentration–time profiles based on 24 covariate combinations specified within the models. Results: Four published pharmacokinetic models of VDZ were included in the evaluation. Using the external dataset, the median prediction error (MDPE) ranged from 13.82% to 25.57%, while the median absolute prediction error (MAPE) varied between 41.64% and 47.56%. None of the models fully met the combined criteria in the prediction-based diagnostics. However, in simulation-based diagnostics, pvcVPC showed satisfactory results, despite wide prediction intervals. Analysis of NPDE revealed that only the models by Rosario et al. and Okamoto et al. fulfilled the evaluation criteria. Simulation analysis further demonstrated that the median VDZ concentration remains above 12 μg/mL at week 22 during maintenance treatment for approximately 45–60% of patients with the best-case covariate combinations and an 8-week dosing frequency. Conclusions: None of the published models satisfied the combined criteria (MDPE, MAPE, percentages of prediction error within ±20% and ±30%), rendering them unsuitable for a priori predictions. However, two models demonstrated better suitability for simulation-based applications. [ABSTRACT FROM AUTHOR]

Purpose: Sensorineural hearing loss (SNHL) is prevalent among older adults in the United States. Recent studies suggest the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio as a potential marker for metabolic and cardiovascular conditions. Our research investigates the association between the TG/HDL-C ratio and SNHL using a comprehensive national dataset. Materials and Methods: This cross-sectional study utilized the National Health and Nutrition Examination Survey (NHANES) data cycle 1999–2000, 2001–2002, 2003–2004, 2011–2012, and 2015–2016. Participants aged 50–69 years with complete audiometry and TG/HDL-C data were included. The outcome was the presence of SNHL, defined as an average hearing threshold >25 dB in the better ear. We employed multivariate logistic regression adjusted for demographics, smoking, noise exposure, and comorbidities to explore the association. Results: A total of 1148 participants constituted the analytic sample, and 31.4% had SNHL. Compared to no SNHL, those with SNHL exhibited higher TG/HDL-C ratios (3.5 vs. 2.7). Elevated TG/HDL-C tertiles correlated with increased SNHL odds [tertile 2: adjusted odds ratio (aOR)=1.50, 95% confidence interval (CI): 0.97–2.32, p=0.069; tertile 3: aOR=1.64, 95% CI: 1.03–2.63, p=0.039]. The link was stronger in participants without diabetes or obesity, with significant predictive values for SNHL presence (area under the ROC curve=0.716 and 0.753, respectively). Conclusion: A higher TG/HDL-C ratio was significantly associated with SNHL in US adults aged 50–69 years, especially in those free from diabetes or obesity. These findings support considering TG/HDL-C as a useful marker for SNHL risk, highlighting the importance of combined metabolic and auditory health assessments. [ABSTRACT FROM AUTHOR]

Background: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models. Methods: Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix. Results: Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy. Conclusions: Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment. [ABSTRACT FROM AUTHOR]

Leveraging advancements in remote data collection and using the Food Insecurity Experience Scale (FIES) as a proxy measure of resilience, we show that machine learning models (such as Gradient Boosting Classifier, eXtreme Gradient Boosting, and Artificial Neural Networks), can predict resilience with relatively high accuracy (up to 81%). Key household‐level predictors include access to financial institutions, asset ownership, the adoption of agricultural mechanization as evidenced by the use of tractors, the number of crops cultivated, and ownership of nonfarm enterprises. Our analysis offers insights to researchers and policymakers interested in the development of targeted interventions to bolster household resilience. [ABSTRACT FROM AUTHOR]

Phishing attacks are becoming more complex and harder to differentiate from legitimate websites. This poses serious risks to users and organizations. This study introduces a phishing detection framework that combines LASSO-based feature selection and a Random Forest classifier enhanced by Weighted Bootstrap Sampling (WBS). The framework addresses two key challenges: optimizing feature selection for high-dimensional data and managing datasets with over 70% outliers. LASSO+ extends the traditional LASSO (Least Absolute Shrinkage and Selection Operator) by integrating Pearson Correlation and Grid Search. This combination improves feature selection by identifying the most relevant features, reducing redundancy, and ensuring efficient processing without compromising accuracy. WBS further enhances Random Forest by prioritizing uncertain samples during training, enabling the model to effectively handle outlier-heavy datasets and improve recall. The proposed framework was evaluated on four diverse datasets with distinct challenges. Results demonstrated high recall rates of 99.59% for Dataset A, 98.76% for Dataset B, 100.00% for Dataset C, and 98.99% for Dataset D. The method also achieved competitive execution times. Compared to existing approaches, the framework delivered better predictive accuracy, robustness, and efficiency. This study highlights the advantages of combining LASSO+ and WBS to improve feature selection and manage outliers in phishing detection. The proposed method provides a reliable solution for addressing cybersecurity challenges in practical applications. [ABSTRACT FROM AUTHOR]

Abstract Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey ‘Korean Longitudinal Study of Aging,’ we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.

Abstract Background Metabolic health is closely related to testosterone levels, and the cardiometabolic index (CMI) is a novel metabolic evaluation metric that encompasses obesity and lipid metabolism. However, there is currently a lack of research on the relationship between CMI and testosterone, which is the objective of this study. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2011 to 2016. Only adult males who completed physical measurements, lipid metabolism assessments, and testosterone measurements were included in the final analysis. The exposure variable CMI was analyzed both as a continuous variable and a categorical variable divided into quartiles. Testosterone was measured using the isotope dilution liquid chromatography-tandem mass spectrometry technique. Linear and logistic regression analyses were used to explore the relationship between CMI and total testosterone (TT) levels, as well as the risk of testosterone deficiency (TD). Smooth curve fittings were employed to visualize their linear relationships. Subgroup analyses were conducted to evaluate the stability of our results across different participant characteristics. Finally, ROC analysis was used to assess the performance of CMI in predicting TD. Results A total of 2,747 participants were included in the analysis, including 552 with TD (20.10%). The average CMI of the sample was 1.59 ± 0.03, with TD participants having a higher CMI of 2.18 ± 0.08 compared to non-TD participants at 1.46 ± 0.03. Corresponding testosterone levels were 223.79 ± 3.69 ng/dL and 508.36 ± 5.73 ng/dL, respectively. After adjusting for all covariates, participants with higher CMI showed lower TT (β = -23.84, 95% CI: -33.94, -13.74, p

Diabetes, a serious condition characterized by elevated blood glucose levels, can be effectively identified, and predicted early using machine learning (ML) algorithms. The research provides a comprehensive assessment of three ensemble ML models-stacking, soft voting, and hard voting-focused on enhancing diabetes diagnosis among Pima Indian women dataset taken from the National Institute of Diabetes and Digestive and Kidney Diseases, this study focuses on Pima Indian women aged 21 and older, with the dataset comprising critical diagnostic measurements. Two ensemble models were developed and evaluated on various evaluation parameters. The stacking model combines predictions from various classifiers using a meta-classifier, leveraging their strengths for final decision-making. In contrast, the voting model aggregates probability estimates from each classifier, providing nuanced predictions. Both models were rigorously evaluated on a validation dataset, emphasizing accuracy, specificity, sensitivity, and the receiver operating characteristic (ROC) area under the curve (AUC). Notably, the voting-based ensemble methods demonstrated superior performance in predicting diabetes for this cohort. However, their effectiveness heavily relies on preprocessing, base model selection, and hyperparameter optimization. This study underscores the potential of ensemble models in medical diagnostics, highlighting the critical role of data preprocessing, and configuration in enhancing predictive accuracy. [ABSTRACT FROM AUTHOR]

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique's response and the target analyte's standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument's response is dependent. The inverse equation is the opposite: the instrument's response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument's response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced. [ABSTRACT FROM AUTHOR]

Background: Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. Methods: This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). Results: CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. Conclusion: HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC. [ABSTRACT FROM AUTHOR]

The insurance market is changing due to new distribution channels, requiring insurers to update their pricing models. We propose a mathematical approach using Bayesian generalized linear models (GLM) to adjust insurance pricing. Our strategy modifies the pricing model by incorporating distribution channels while utilizing the initial model as a baseline. Bayesian GLM enable effective model updates while incorporating existing knowledge. We validated our approach using data from the general insurance sector, comparing it with the traditional approach. Results show that Bayesian GLM outperforms the traditional method in accurately estimating pricing. This superiority highlights its potential as a powerful tool for insurers to remain competitive in a rapidly changing market. Our approach makes a significant mathematical contribution to insurance pricing, allowing insurers to adapt to market conditions and enhance their competitive edge. [ABSTRACT FROM AUTHOR]

Introduction: Recently, a method was developed to predict the motor Functional Independence Measure (FIM) score at discharge in patients with stroke by stratifying the effects of factors such as age and cognitive function and multiplying those by the influence coefficients of these factors. However, an evaluation of the predictive performance of the method is required for clinical application. The present study aimed to evaluate the predictive performance of this prediction method. Methods: Patients with stroke discharged from a rehabilitation ward between April 2021 and September 2022 were included. Predicted values of the motor FIM score at discharge were calculated after data collection from the hospital's patient database. The concordance between predicted and actual values was evaluated using the interclass correlation coefficient; moreover, the residual values were calculated. Results: In total, 207 patients were included in the analysis. The median age was 79 (69-85) years, and 112 (54.1%) patients were male. The interclass correlation coefficient between predicted and actual values was 0.84 (95% confidence interval 0.75-0.89) for the motor FIM score at discharge. Meanwhile, the median residual value was 5.3 (-2.0-10.3) for the motor FIM score at discharge. Discussion: The prediction method was validated with good performance. However, the residual values indicated that some cases deviated from the prediction. In future studies, it will be necessary to improve the predictive performance of the method by clarifying the characteristics of cases that deviate from the prediction. [ABSTRACT FROM AUTHOR]

In this paper we test and measure the predictive performance of the NYS Establishment Surveys. Using Pesaran and Timmermann test, the null hypothesis of no predictable relationship of each establishment performance measure is rejected. This is also supported graphically by the results of the diffusion index. Goodman-Kruskal gamma coefficient is used to measure how good the forecasts predict their realizations. The coefficients range from 0.412 to 0.729. [ABSTRACT FROM AUTHOR]

Abstract Background Recent studies have suggested that insulin resistance (IR) contributes to the development of cardiovascular diseases (CVD), and the estimated glucose disposal rate (eGDR) is considered to be a reliable surrogate marker of IR. However, most existing evidence stems from studies involving diabetic patients, potentially overstating the effects of eGDR on CVD. Therefore, the primary objective of this study is to examine the relationship of eGDR with incidence of CVD in non-diabetic participants. Method The current analysis included individuals from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD and diabetes mellitus but had complete data on eGDR at baseline. The formula for calculating eGDR was as follows: eGDR (mg/kg/min) = 21.158 − (0.09 × WC) − (3.407 × hypertension) − (0.551 × HbA1c) [WC (cm), hypertension (yes = 1/no = 0), and HbA1c (%)]. The individuals were categorized into four subgroups according to the quartiles (Q) of eGDR. Crude incidence rate and hazard ratios (HRs) with 95% confidence intervals (CIs) were computed to investigate the association between eGDR and incident CVD, with the lowest quartile of eGDR (indicating the highest grade of insulin resistance) serving as the reference. Additionally, the multivariate adjusted restricted cubic spine (RCS) was employed to examine the dose–response relationship. Results We included 5512 participants in this study, with a mean age of 58.2 ± 8.8 years, and 54.1% were female. Over a median follow-up duration of 79.4 months, 1213 incident CVD cases, including 927 heart disease and 391 stroke, were recorded. The RCS curves demonstrated a significant and linear relationship between eGDR and all outcomes (all P for non-linearity > 0.05). After multivariate adjustment, the lower eGDR levels were founded to be significantly associated with a higher risk of CVD. Compared with participants with Q1 of eGDR, the HRs (95% CIs) for those with Q2 − 4 were 0.88 (0.76 − 1.02), 0.69 (0.58 − 0.82), and 0.66 (0.56 − 0.79). When assessed as a continuous variable, per 1.0-SD increase in eGDR was associated a 17% (HR: 0.83, 95% CI: 0.78 − 0.89) lower risk of CVD, with the subgroup analyses indicating that smoking status modified the association (P for interaction = 0.012). Moreover, the mediation analysis revealed that obesity partly mediated the association. Additionally, incorporating eGDR into the basic model considerably improve the predictive ability for CVD. Conclusion A lower level of eGDR was found to be associated with increased risk of incident CVD among non-diabetic participants. This suggests that eGDR may serve as a promising and preferable predictor and intervention target for CVD.

Background The anti-neutrophil cytoplasmic antibody (ANCA) renal risk score (ARRS) for predicting renal survival in ANCA-associated vasculitis (AAV) had not previously been validated in adults over 65 years of age and presenting impairments associated with an aging kidney, a high cardiovascular comorbidity burden and prevalent microscopic polyangiitis. Methods We retrospectively studied a cohort of 192 patients over 65 years of age [median (interquartile range) age: 73 (68–78) years], including 17.2% with renal-limited vasculitis, 49.5% with microscopic polyangiitis and 33.3% with granulomatosis with polyangiitis, at six centres in northern France. The primary study endpoint was the cumulative incidence of end-stage kidney disease (ESKD, maintenance of dialysis for at least 3 months) at 12 months, with death considered as a competing event. Results The median serum creatinine concentration at diagnosis was 300 (202–502) µmol/L, and 48 (25.0%) patients required dialysis at presentation. The ARRS was high in 43 (22.4%) patients, medium in 106 (55.2%) and low in 43 (22.4%). The cumulative incidence of ESKD at 12 months was 0% in the low-risk group, 13.0% (interquartile range 7.6–20.0) in the medium-risk group and 44.0% (29.0–58.0) in the high-risk group (P <.001). In the subgroup of 149 patients presenting a medium or high score, the ARRS had a C-index of 0.66 (0.58–0.74) for the prediction of ESKD at 12 months; this rose to 0.86 (0.80–0.90) when dialysis status at diagnosis was included. Conclusion The ARRS was a poor predictor of kidney survival at 12 months among patients over 65 years of age with renal AAV involvement—especially in the high ARRS group. The addition of dialysis status at diagnosis as an additional clinical parameter might improve the predictive performance of the ARRS. [ABSTRACT FROM AUTHOR]

Background and Objectives: The predicted serum concentrations of vancomycin are determined using population pharmacokinetic parameters. However, the accuracy of predicting vancomycin serum concentrations in the older population remains unclear. Therefore, this study aimed to investigate the accuracy of predicting vancomycin serum concentrations and identifying elements that diminish the prediction accuracy in older people. Materials and Methods: A total of 144 patients aged 75 years or older were included. The serum vancomycin concentrations in the patients were predicted based on population pharmacokinetic parameters common in Japan. We examined the accuracy of serum vancomycin concentration prediction in elderly individuals by comparing the predicted and measured serum vancomycin concentrations in each patient. The prediction accuracy was evaluated using the mean prediction error (ME) and mean absolute error of prediction (MAE) calculated from the measured and predicted serum vancomycin concentrations in each patient. Results: The ME for all patients was 0.27, and the 95% CI included 0, indicating that the predicted values were not significantly biased compared to the measured values. However, the predicted serum concentrations in the <50 kg body weight and serum creatinine (Scr) < 0.6 mg/dL groups were significantly biased compared to the measured values. The group with a history of intensive care unit (ICU) admission showed the largest values for the ME and MAE. Conclusions: Our prediction accuracy was satisfactory but tended to be lower in underweight patients, those with low creatinine levels, and patients admitted to the ICU. Patients with multiple of these factors may experience a greater degree of decreased predictive accuracy. [ABSTRACT FROM AUTHOR]

Hierarchical models can express ecological dynamics using a combination of fixed and random effects, and measurement of their complexity (effective degrees of freedom, EDF) requires estimating how much random effects are shrunk toward a shared mean. Estimating EDF is helpful to (1) penalize complexity during model selection and (2) to improve understanding of model behavior. I applied the conditional Akaike Information Criterion (cAIC) to estimate EDF from the finite‐difference approximation to the gradient of model predictions with respect to each datum. I confirmed that this has similar behavior to widely used Bayesian criteria, and I illustrated ecological applications using three case studies. The first compared model parsimony with or without time‐varying parameters when predicting density‐dependent survival, where cAIC favors time‐varying demographic parameters more than conventional Akaike Information Criterion. The second estimates EDF in a phylogenetic structural equation model, and identifies a larger EDF when predicting longevity than mortality rates in fishes. The third compares EDF for a species distribution model fitted for 20 bird species and identifies those species requiring more model complexity. These highlight the ecological and statistical insight from comparing EDF among experimental units, models, and data partitions, using an approach that can be broadly adopted for nonlinear ecological models. [ABSTRACT FROM AUTHOR]

The present work focuses on predicting the steel melt temperature following the vacuum treatment step in a vacuum tank degasser (VTD). The primary objective is to establish a comprehensive methodology for developing and validating machine learning (ML) models within this context. Another objective is to evaluate the model by analyzing the alignment of the SHAP values with metallurgical domain expectations, thereby validating the model's predictions from a metallurgical perspective. The proposed methodology employs a Random Forest model, incorporating a grid search with domain-informed variables grouped into batches, and a robust model-selection criterion that ensures optimal predictive performance, while keeping the model as simple and stable as possible. Furthermore, the Shapley Additive Explanations (SHAP) algorithm is employed to interpret the model's predictions. The selected model achieved a mean adjusted R 2 of 0.631 and a hit ratio of 75.3% for a prediction error within ±5 °C. Despite the moderate predictive performance, SHAP highlighted several aspects consistent with metallurgical domain expertise, emphasizing the importance of domain knowledge in interpreting ML models. Improving data quality and refining the model framework could enhance predictive performance. [ABSTRACT FROM AUTHOR]

Background/Objectives: Several population pharmacokinetic models of vedolizumab (VDZ) are available for inflammatory bowel disease (IBD) patients. However, their predictive performance in real-world clinical settings remains unknown. This study aims to externally evaluate the published VDZ pharmacokinetic models, focusing on their predictive performance and simulation-based clinical applicability. Methods: A literature search was conducted through PubMed to identify VDZ population pharmacokinetic models. A total of 114 VDZ concentrations from 106 IBD patients treated at the University Medical Center “Zvezdara”, Republic of Serbia, served as the external evaluation cohort. The predictive performance of the models was assessed using prediction- and simulation-based diagnostics. Furthermore, the models were utilized for Monte Carlo simulations to generate concentration–time profiles based on 24 covariate combinations specified within the models. Results: Four published pharmacokinetic models of VDZ were included in the evaluation. Using the external dataset, the median prediction error (MDPE) ranged from 13.82% to 25.57%, while the median absolute prediction error (MAPE) varied between 41.64% and 47.56%. None of the models fully met the combined criteria in the prediction-based diagnostics. However, in simulation-based diagnostics, pvcVPC showed satisfactory results, despite wide prediction intervals. Analysis of NPDE revealed that only the models by Rosario et al. and Okamoto et al. fulfilled the evaluation criteria. Simulation analysis further demonstrated that the median VDZ concentration remains above 12 μg/mL at week 22 during maintenance treatment for approximately 45–60% of patients with the best-case covariate combinations and an 8-week dosing frequency. Conclusions: None of the published models satisfied the combined criteria (MDPE, MAPE, percentages of prediction error within ±20% and ±30%), rendering them unsuitable for a priori predictions. However, two models demonstrated better suitability for simulation-based applications.

Lithium-ion batteries are essential for electrochemical energy storage, yet they undergo progressive aging during operational lifespan. Consequently, precise estimation of their state of health (SOH) is crucial for effective and safe operation of energy storage systems. This paper investigates the viability of ultrasound-Based methods for assessing the SOH of prismatic lithium-ion batteries. In the experimental framework, a designated prismatic lithium-ion battery was subjected to numerous charging and discharging cycles using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algorithms: linear regression, support vector machines, Gaussian process regression, and neural networks. The predictive performance of each algorithm was evaluated through extensive experimentation and analysis. The combination of ultrasonic signals with computational models has emerged as a robust technique for precise battery degradation assessment, suggesting its potential as a standard in battery health evaluation methods. [ABSTRACT FROM AUTHOR]

Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimensionality models, nested-dimensionality models could have a greater propensity to fit data that do not represent a specific dimensional structure. However, it is unclear as to what degree model comparison results are biased toward nested-dimensionality IRT models when the data represent specific dimensional structures and when Bayesian estimation and model comparison indices are used. We conducted a simulation study to add clarity to this issue. We examined the accuracy of four Bayesian predictive performance indices at differentiating among non-nested- and nested-dimensionality IRT models. The deviance information criterion (DIC), a commonly used index to compare Bayesian models, was extremely biased toward nested-dimensionality IRT models, favoring them even when non-nested-dimensionality models were the correct models. The Pareto-smoothed importance sampling approximation of the leave-one-out cross-validation was the least biased, with the Watanabe information criterion and the log-predicted marginal likelihood closely following. The findings demonstrate that nested-dimensionality IRT models are not automatically favored when the data represent specific dimensional structures as long as an appropriate predictive performance index is used. [ABSTRACT FROM AUTHOR]

Key summary points: Aim: Aim This study aimed to clarify the predictive performance of different measures of frailty, including the Clinical Frailty Scale (CFS), 11-factor modified Frailty Index (mFI-11), and 5-factor modified Frailty Index (mFI-5), on adverse outcomes. Findings: The CFS, mFI-11, and mFI-5 had a different level of predictive power. Message: According to the specific situation and needs of colorectal cancer patients, appropriate frailty assessment tools can be selected to adjust the treatment plan of high-risk patients timely. Purpose: To clarify the predictive performance of different measures of frailty, including Clinical Frailty Scale (CFS), 11-factor modified Frailty Index (mFI-11), and 5-factor modified Frailty Index (mFI-5), on adverse outcomes. Methods: PubMed, Embase, Web of Science, and other databases were retrieved from the inception of each database to June 2023. The pooled sensitivity, specificity, and the area under the summary receiver operating curve (SROC) values were analyzed to determine the predictive power of CFS, mFI-11, and mFI-5 for adverse outcomes. Results: A total of 25 studies were included in quantitative synthesis. The pooled sensitivity values of CFS for predicting anastomotic leakage, total complications, and major complications were 0.39, 0.57, 0.45; pooled specificity values were 0.70, 0.58, 0.73; the area under SROC values were 0.58, 0.6, 0.66. The pooled sensitivity values of mFI-11 for predicting total complications and delirium were 0.38 and 0.64; pooled specificity values were 0.83 and 0.72; the area under SROC values were 0.64 and 0.74. The pooled sensitivity values of mFI-5 for predicting total complications, 30-day mortality, and major complications were 0.27, 0.54, 0.25; pooled specificity values were 0.82, 0.84, 0.81; the area under SROC values were 0.63, 0.82, 0.5. Conclusion: The results showed that CFS could predict anastomotic leakage, total complications, and major complications; mFI-11 could predict total complications and delirium; mFI-5 could predict total complications and 30-day mortality. More high-quality research is needed to support the conclusions of this study further. [ABSTRACT FROM AUTHOR]

• Longitudinal changes in angiogenic factors could provide prognostic information. • Angiogenic unbalance is detectable from the finding of preeclampsia symptoms. • The daily variation of angiogenic factors is higher the more aggressive the disease is. • The daily variation of angiogenic factors could be predictors of PE-related events. • Women with higher daily variation of angiogenic markers have shorter pregnancies. To investigate whether longitudinal changes of angiogenic factors (AF) sFlt-1, PlGF, and the sFlt-1/PlGF ratio, measured following identification of symptoms of preeclampsia (PE), could provide complementary information to the isolated measurements used in current clinical practice. Retrospective observational study. Sixty women with suspected PE and two AF results measured before gestational week (GW) 34 were included. Daily variation (DV) of AF was calculated from delta values and days elapsed between measurements. Through ROC analysis, the predictive performance of DV for PE-related events was estimated. Kaplan-Meier survival curves resulting from applying cutoff values were assessed. The sFlt-1, PlGF, and sFlt-1/PlGF ratio baseline levels showed significant differences between women without PE and women who developed early-onset PE (P < 0.001). DV of sFlt-1 and sFlt-1/PlGF ratio increased according to the severity of PE, showing significant differences in both pairs of groups compared (p < 0.001), so they were selected as potential predictors. Higher AUC values resulting from ROC analysis were 0.78 for early-onset PE, 0.88 for early-onset severe PE, 0.79 for occurrence of adverse maternal outcomes, and 0.89 for delivery before 37 GW, with sensitivity and specificity values higher than 0.71 and 0.80, respectively. The Kaplan-Meier analysis yielded significantly different curves (log-rank < 0.05), with shorter time-to-delivery as DV increased. Our results support the existence of a correlation between a progressive PlGF and sFlt-1 imbalance and a more aggressive clinical course of PE, detectable from the finding of PE symptoms. Its monitoring could be a useful predictive tool in women with suspected PE. [ABSTRACT FROM AUTHOR]

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Mohsin Khan,1,&ast; Syed Khalid Perwez,2,&ast; Rahul Paul Gaddam,2 Rabuni Aiswarya,2 Mohammed Abrar Basha,3 Abhradeep Malas,4 Shafiul Haque,5– 7 Faraz Ahmad4 1Department of Commerce, School of Social Science and Languages, Vellore Institute of Technology, Vellore, India; 2VIT Business School, Vellore Institute of Technology, Vellore, India; 3School of Life Sciences, B.S Abdur Rahman Crescent Institute of Science & Technology, Chennai, India; 4Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India; 5Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia; 6Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon; 7Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates&ast;These authors contributed equally to this workCorrespondence: Faraz Ahmad, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India, Tel +91 8969 66 8060, Email faraz.ahmad@vit.ac.inPurpose: While previous studies have suggested close association of psychological variables of students withtheir higher-order cognitive abilities, such studies have largely been lacking for third world countries like India, with their unique socio-economic-cultural set of challenges. We aimed to investigate the relationship between psychological variables (depression, anxiety and stress) and cognitive functions among Indian students, and to predict cognitive performance as a function of these variables.Patients and Methods: Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables.Results: Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables.Conclusion: The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.Keywords: depression anxiety stress score, DASS, Montreal Cognitive Assessment, MoCA, college students, artificial neural network, predictive performance, Indian, developing economies, feature reduction, feature weights

Abstract Background In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. Methods Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. Results The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R t curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R t below 1.0, as well as reducing the number of peak cases and final affected population. Conclusions The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Lithium-ion batteries are essential for electrochemical energy storage, yet they undergo progressive aging during operational lifespan. Consequently, precise estimation of their state of health (SOH) is crucial for effective and safe operation of energy storage systems. This paper investigates the viability of ultrasound-based methods for assessing the SOH of prismatic lithium-ion batteries. In the experimental framework, a designated prismatic lithium-ion battery was subjected to numerous charging and discharging cycles using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algorithms: linear regression, support vector machines, Gaussian process regression, and neural networks. The predictive performance of each algorithm was evaluated through extensive experimentation and analysis. The combination of ultrasonic signals with computational models has emerged as a robust technique for precise battery degradation assessment, suggesting its potential as a standard in battery health evaluation methods.

Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field‐based surveys during 2018–2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation‐related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long‐term forces of climate change. [ABSTRACT FROM AUTHOR]

The Ames/quantitative structure–activity relationship (QSAR) International Challenge Projects, held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology. [ABSTRACT FROM AUTHOR]

Background and Objectives: A positive pathological circumferential resection margin is a key prognostic factor in rectal cancer surgery. The point of this prospective study was to see how well different MRI parameters could predict a positive pathological circumferential resection margin (pCRM) in people who had been diagnosed with rectal adenocarcinoma, either on their own or when used together. Materials and Methods: Between November 2019 and February 2023, a total of 112 patients were enrolled in this prospective study and followed up for a 36-month period. MRI predictors such as circumferential resection margin (mCRM), presence of extramural venous invasion (mrEMVI), tumor location, and the distance between the tumor and anal verge, taken individually or combined, were evaluated with univariate and sensitivity analyses. Survival estimates in relation to a pCRM status were also determined using Kaplan–Meier analysis. Results: When individually evaluated, the best MRI predictor for the detection of a pCRM in the postsurgical histopathological examination is mrEMVI, which achieved a sensitivity (Se) of 77.78%, a specificity (Sp) of 87.38%, a negative predictive value (NPV) of 97.83%, and an accuracy of 86.61%. Also, the best predictive performance was achieved by a model that comprised all MRI predictors (mCRM+ mrEMVI+ anterior location+ < 4 cm from the anal verge), with an Se of 66.67%, an Sp of 88.46%, an NPV of 96.84%, and an accuracy of 86.73%. The survival rates were significantly higher in the pCRM-negative group (p < 0.001). Conclusions: The use of selective individual imaging predictors or combined models could be useful for the prediction of positive pCRM and risk stratification for local recurrence or distant metastasis. [ABSTRACT FROM AUTHOR]

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques (PPTs). However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individual's privacy while maintaining the interpretability of the data (i.e., its usefulness). Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing PPTs used in microdata de-identification, privacy measures suitable for several disclosure types, and information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review the taxonomies of PPTs, provide a theoretical analysis of existing comparative studies, and raise multiple open issues. [ABSTRACT FROM AUTHOR]

The accurate evaluation of human epidermal growth factor receptor 2 (HER2) is crucial for successful trastuzumab-based therapy in individuals with gastric cancer (GC). The present study, involving a retrospective cohort (N = 2865) from Wuhan Union Hospital and a prospective cohort (N = 392) from Renmin Hospital of Wuhan University, evaluated the benefits of clinical features using random forest and logistic regression models for the detection of HER2 status in patients with GC. Patients from the Union cohort were randomly assigned to either a training (N = 2005) or an internal validation (N = 860) group. Data processing and feature selection were done in Python, which was also used to build random forest and logistic regression models for the prediction of HER2 overexpression. The Renmin cohort (N = 392) was used as the external validation group. Ten features were closely correlated with HER2 overexpression, including age, albumin/globulin ratio, globulin, activated partial thromboplastin time, tumor stage, node stage, tumor node metastasis stage, tumor size, tumor differentiation, and neuron-specific enolase (NSE). Random forest and logistic regression had areas under the curve (AUC) of 0.9995 and 0.6653 in the training group and 0.923 and 0.667 in the internal validation group, respectively. When the two predictive models were validated using data from the Renmin cohort, random forest and logistic regression had AUCs of 0.9994 and 0.627, respectively. This is the first multicenter study to predict HER2 overexpression in individuals with GC, based on clinical variables. The random forest model significantly outperformed the logistic regression model. [ABSTRACT FROM AUTHOR]

Background: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. Methods: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. Results: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. Conclusions: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus. [ABSTRACT FROM AUTHOR]

The present work aims to answer three essential research questions (RQs) that have previously not been explicitly dealt with in the field of applied machine learning (ML) in steel process engineering. RQ1: How many training data points are needed to create a model with near-upper-bound predictive performance on test data? RQ2: What is the near-upper-bound predictive performance on test data? RQ3: For how long can a model be used before its predictive performance starts to decrease? A methodology to answer these RQs is proposed. The methodology uses a developed sampling algorithm that samples numerous unique training and test datasets. Each sample was used to create one ML model. The predictive performance of the resulting ML models was analyzed using common statistical tools. The proposed methodology was applied to four disparate datasets from the steel industry in order to externally validate the experimental results. It was shown that the proposed methodology can be used to answer each of the three RQs. Furthermore, a few findings that contradict established ML knowledge were also found during the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

Background and Objectives: The predicted serum concentrations of vancomycin are determined using population pharmacokinetic parameters. However, the accuracy of predicting vancomycin serum concentrations in the older population remains unclear. Therefore, this study aimed to investigate the accuracy of predicting vancomycin serum concentrations and identifying elements that diminish the prediction accuracy in older people. Materials and Methods: A total of 144 patients aged 75 years or older were included. The serum vancomycin concentrations in the patients were predicted based on population pharmacokinetic parameters common in Japan. We examined the accuracy of serum vancomycin concentration prediction in elderly individuals by comparing the predicted and measured serum vancomycin concentrations in each patient. The prediction accuracy was evaluated using the mean prediction error (ME) and mean absolute error of prediction (MAE) calculated from the measured and predicted serum vancomycin concentrations in each patient. Results: The ME for all patients was 0.27, and the 95% CI included 0, indicating that the predicted values were not significantly biased compared to the measured values. However, the predicted serum concentrations in the Conclusions: Our prediction accuracy was satisfactory but tended to be lower in underweight patients, those with low creatinine levels, and patients admitted to the ICU. Patients with multiple of these factors may experience a greater degree of decreased predictive accuracy.

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes. [ABSTRACT FROM AUTHOR]

Precipitation isoscapes have provided supporting data for numerous studies of water stable isotopes, alleviating the lack of observation data. However, the applicability of simulation data from global models to specific regional contexts remains a subject requiring further investigation, particularly concerning d-excess—an aspect often overlooked by prediction models. To bridge this gap, this study evaluates the performance of three mainstream precipitation isoscapes (OIPC3.2, RCWIP1, and RCWIP2) for the prediction of average annual δ2H, δ18O, and d-excess based on observations from the CHNIP database. The results show that while all three models can accurately reproduce δ2H and δ18O values, none are able to accurately match d-excess values. This disparity can be attributed to the absence of water-vapor source information in the models' input variables, a key determinant influencing d-excess outcomes. Additionally, it is noteworthy that OIPC3.2 stands out as the optimal choice for δ2H and δ18O estimations, while RCWIP2 exhibits progressive enhancements over RCWIP1 in d-excess estimations. This highlights the significance of selecting highly pluralistic information variables and recognizing the impact of error propagation in such models. As a result, the advancement of isoscapes in accurately and precisely depicting precipitation isotopes, particularly d-excess, necessitates further refinement. Future avenues for improvement might involve the incorporation of water-vapor source-clustering methodologies, the selection of information-rich variables, and the autonomous construction of a dedicated d-excess simulation. This research provides valuable insights for the further refining of isoscape modeling in the future. [ABSTRACT FROM AUTHOR]

This study aimed to: (1) identify all relevant studies reporting on the diagnostic accuracy of maternal circulating placental growth factor) alone or as a ratio with soluble fms-like tyrosine kinase-1), and of placental growth factor-based models (placental growth factor combined with maternal factors±other biomarkers) in the second or third trimester to predict subsequent development of preeclampsia in asymptomatic women; (2) estimate a hierarchical summary receiver-operating characteristic curve for studies reporting on the same test but different thresholds, gestational ages, and populations; and (3) select the best method to screen for preeclampsia in asymptomatic women during the second and third trimester of pregnancy by comparing the diagnostic accuracy of each method. A systematic search was performed through MEDLINE, Embase, CENTRAL, ClinicalTrials.gov , and the World Health Organization International Clinical Trials Registry Platform databases from January 1, 1985 to April 15, 2021. Studies including asymptomatic singleton pregnant women at >18 weeks' gestation with risk of developing preeclampsia were evaluated. We included only cohort or cross-sectional test accuracy studies reporting on preeclampsia outcome, allowing tabulation of 2×2 tables, with follow-up available for >85%, and evaluating performance of placental growth factor alone, soluble fms-like tyrosine kinase-1– placental growth factor ratio, or placental growth factor-based models. The study protocol was registered on the International Prospective Register Of Systematic Reviews (CRD 42020162460). Because of considerable intra- and interstudy heterogeneity, we computed the hierarchical summary receiver-operating characteristic plots and derived diagnostic odds ratios, β, θ i , and Λ for each method to compare performances. The quality of the included studies was evaluated by the QUADAS-2 tool. The search identified 2028 citations, from which we selected 474 studies for detailed assessment of the full texts. Finally, 100 published studies met the eligibility criteria for qualitative and 32 for quantitative syntheses. Twenty-three studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the second trimester, including 16 (with 27 entries) that reported on placental growth factor test alone, 9 (with 19 entries) that reported on the soluble fms-like tyrosine kinase-1–placental growth factor ratio, and 6 (16 entries) that reported on placental growth factor-based models. Fourteen studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the third trimester, including 10 (with 18 entries) that reported on placental growth factor test alone, 8 (with 12 entries) that reported on soluble fms-like tyrosine kinase-1–placental growth factor ratio, and 7 (with 12 entries) that reported on placental growth factor-based models. For the second trimester, Placental growth factor-based models achieved the highest diagnostic odds ratio for the prediction of early preeclampsia in the total population compared with placental growth factor alone and soluble fms-like tyrosine kinase-1–placental growth factor ratio (placental growth factor-based models, 63.20; 95% confidence interval, 37.62–106.16 vs soluble fms-like tyrosine kinase-1–placental growth factor ratio, 6.96; 95% confidence interval, 1.76–27.61 vs placental growth factor alone, 5.62; 95% confidence interval, 3.04–10.38); placental growth factor-based models had higher diagnostic odds ratio than placental growth factor alone for the identification of any-onset preeclampsia in the unselected population (28.45; 95% confidence interval, 13.52–59.85 vs 7.09; 95% confidence interval, 3.74–13.41). For the third trimester, Placental growth factor-based models achieved prediction for any-onset preeclampsia that was significantly better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1–placental growth factor ratio (placental growth factor-based models, 27.12; 95% confidence interval, 21.67–33.94 vs placental growth factor alone, 10.31; 95% confidence interval, 7.41–14.35 vs soluble fms-like tyrosine kinase-1–placental growth factor ratio, 14.94; 95% confidence interval, 9.42–23.70). Placental growth factor with maternal factors ± other biomarkers determined in the second trimester achieved the best predictive performance for early preeclampsia in the total population. However, in the third trimester, placental growth factor-based models had predictive performance for any-onset preeclampsia that was better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1–placental growth factor ratio. Through this meta-analysis, we have identified a large number of very heterogeneous studies. Therefore, there is an urgent need to develop standardized research using the same models that combine serum placental growth factor with maternal factors ± other biomarkers to accurately predict preeclampsia. Identification of patients at risk might be beneficial for intensive monitoring and timing delivery. [ABSTRACT FROM AUTHOR]