Bootstrap Resampling Technique Research Articles

Analysis of risk factors and development of a nomogram prediction model for renal tubular acidosis in primary Sjogren syndrome patients

ObjectiveTo investigate the risk factors of renal tubular acidosis (RTA) in patients with primary Sjögren’s syndrome (pSS) and create a personalized nomogram for predicting pSS-RTA patients.MethodData from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis. In addition, we graded the severity of pSS-RTA patients and used univariate analysis to assess the relationship between pSS-RTA severity and risk factors.ResultsA multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model’s reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value.This study did not find an association between the severity of pSS-RTA and risk factors.DiscussionWe developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients.

Development and Validation of a Risk Prediction Algorithm for Evaluating the Efficacy of Postoperative Adjuvant TACE Therapy for Hepatocellular Carcinoma.

There is a lack of a reliable outcome prediction model for patients evaluating the feasibility of postoperative adjuvant transarterial chemoembolization (PATACE) therapy. Our goal was to develop an easy-to-use tool specifically for these patients. From January 2013 to June 2017, patients with hepatocellular carcinoma from the Liver Center of the First Affiliated Hospital of Chongqing Medical University received postoperative adjuvant Transarterial chemoembolization (TACE) therapy after liver cancer resection. A Cox proportional hazards model was established for these patients, followed by internal validation (enhanced bootstrap resampling technique) to further evaluate the predictive performance and discriminanceevaluate the predictive performance and discriminance, and compare it with other predictive models. The prognostic factors considered included tumour number, maximum tumor diameter, Edmondson-Steiner (ES) grade, Microvascular invasion (MVI) grade, Ki67, age, sex, hepatitis B surface antigen, cirrhosis, Alpha-fetoprotein (AFP), Albumin-bilirubin (ALBI) grade, Childpugh grade, body mass index (BMI), Neutrophil-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR). The endpoint of the study was overall survival. The median overall survival was 36 (95%CI: 34.0-38.0) months, with 1-year, 2-year and 3-year survival rates being 96.3%, 84.0% and 75.3%, respectively. Tumour number, MVI grade, and BMI was incorporated into the model, which had good differentiation and accuracy. Internal validation (enhanced bootstrap) suggested that Harrell's C statistic is 0.72. The model consistently outperforms other currently available models. This model may be an easy-to-use tool for screening patients suitable for PA-TACE treatment and guiding the selection of clinical protocols. But further research and external validation are required.

Inputs-Oriented VRS DEA in dairy farms.

This paper aims to examine the efficiency of Mexico's dairy farms within its four regions of Tlaxcala Stated. The Envelopment Data Analysis (DEA) applied to the variable returns to a scale model (VRS) for the year 2020. Also, Examine the statistical accuracy of efficiency estimation using bootstrap resampling techniques. The results reveal that Tlaxcala's dairy farm efficiency, on the other hand, was adversely influenced by three inputs (costs): cost of investment in livestock, the total annual cost for feeding, reproduction, diseases and treatments, preventive medicine, sanitation, milking, fuel, and total labor. The efficiency distribution among farms using VRS, CRS, and FDH technologies reveals varying patterns. Under VRS and CRS, the majority of farms exhibit high efficiency within the 0 to less than 0.2 range, while FDH displays a broader distribution, with notable efficiency at 1 and across various ranges. These findings highlight the diverse landscape of efficiency levels across different technological approaches within the agricultural sector, offering valuable insights for optimization strategies and resource allocation. The utilization of Bootstrap methodology enhances the reliability of efficiency assessments by providing robust statistical techniques that accommodate non-normal data distributions. By incorporating Bootstrap, decision-makers can obtain more accurate estimates of efficiency levels and confidence intervals, thereby making informed decisions regarding resource allocation and optimization strategies within the agricultural sector. As part of the study, provided The Policy suggestions.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer.

Colorectal cancer (CRC) is a serious threat worldwide. Although early screening is suggested to be the most effective method to prevent and control CRC, the current situation of early screening for CRC is still not optimistic. In China, the incidence of CRC in the Yangtze River Delta region is increasing dramatically, but few studies have been conducted. Therefore, it is necessary to develop a simple and efficient early screening model for CRC. To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC. Data of 64448 participants obtained from Ningbo Hospital, China between 2014 and 2017 were retrospectively analyzed. The cohort comprised 64448 individuals, of which, 530 were excluded due to missing or incorrect data. Of 63918, 7607 (11.9%) individuals were considered to be high risk for CRC, and 56311 (88.1%) were not. The participants were randomly allocated to a training set (44743) or validation set (19175). The discriminatory ability, predictive accuracy, and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic (ROC) curves and calibration curves and by decision curve analysis. Finally, the model was validated internally using a bootstrap resampling technique. Seven variables, including demographic, lifestyle, and family history information, were examined. Multifactorial logistic regression analysis revealed that age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.03, P < 0.001], body mass index (BMI) (OR: 1.07, 95%CI: 1.06-1.08, P < 0.001), waist circumference (WC) (OR: 1.03, 95%CI: 1.02-1.03 P < 0.001), lifestyle (OR: 0.45, 95%CI: 0.42-0.48, P < 0.001), and family history (OR: 4.28, 95%CI: 4.04-4.54, P < 0.001) were the most significant predictors of high-risk CRC. Healthy lifestyle was a protective factor, whereas family history was the most significant risk factor. The area under the curve was 0.734 (95%CI: 0.723-0.745) for the final validation set ROC curve and 0.735 (95%CI: 0.728-0.742) for the training set ROC curve. The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population. The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age, BMI, WC, lifestyle, and family history exhibited high accuracy.

Seismic risk assessment of highway bridges in western Canada under crustal, subcrustal, and subduction earthquakes

This study conducts seismic risk assessment of highway bridges in western Canada. The performance-based earthquake engineering (PBEE) framework is enhanced to assess the expected annual repair cost ratio (ARCR) and annual restoration time (ART) of a benchmark bridge class under the region’s three types of earthquakes - shallow crustal earthquakes (CEs), deep subcrustal earthquakes (SCEs), and megathrust Cascadia subduction earthquakes (CSEs). First, event-specific seismic hazard models are considered, whereas event-consistent ground motions are selected for non-linear time history analyses. Compared with those from CEs and SCEs, CSE ground motions feature a much longer duration. This long-duration effect is captured by validating the numerical model of the bridge column against (1) a cyclic pushover test under standard versus long-duration loading protocols and (2) a shaking table test excited by six consecutive ground motions. Besides, the Park and Ang damage index is utilized as the column’s engineering demand parameter (EDP) and updated as a demand-capacity ratio model when reaching four different damage states. A comprehensive list of ground motion intensity measures (IMs) is considered where the spectra acceleration at one second, Sa(1.0), is chosen as the most suitable IM based on its performance in proficiency, efficiency, practicality, and EDP-IM correlation across all three earthquake events. Subsequently, component- and system-level fragility models are derived under each earthquake type using the cloud analysis that convolves the seismic demands with capacity models for multiple bridge components. To further quantify and propagate the epistemic uncertainty associated with the development of probabilistic seismic demand models (PSDMs), the bootstrap resampling technique is utilized to generate numerous seismic demand datasets and develop a stochastic set of seismic fragility curves. Finally, the bootstrapped, event-dependent fragility models are combined with the respective hazard models and probabilistic loss functions to assess the expected ARCR and ART for the benchmark bridge class. This study underscores the significantly higher seismic risk of highway bridges when facing CSEs, followed by CEs and SCEs.

A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem

Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generates optimized weights which are used to build a linear discriminant function. The experimental results obtained by applying this model on simulated and real datasets prove its high efficiency and evaluate its significant value on credit scoring.

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

A prediction tool for mode of delivery in twin pregnancies—a secondary analysis of the Twin Birth Study

One of the controversies regarding the management of twin gestations relates to the mode of delivery. Currently, counseling regarding the mode of delivery and the chance of successful vaginal twin delivery is based on the average risk for intrapartum cesarean delivery in the general population of twin pregnancies. Decision support tools that provide an individualized risk for intrapartum cesarean delivery based on the unique characteristics of each patient can improve counseling and decision-making regarding the choice of mode of delivery in twin pregnancies. This study aimed to develop and validate a prediction model to determine the risk for intrapartum cesarean delivery in twin pregnancies. In this secondary analysis of the Twin Birth Study, a multicenter randomized controlled trial, we considered the subgroup of individuals who underwent a trial of vaginal delivery. Candidate predictors included maternal age, parity, previous cesarean delivery, conception method, chorionicity, diabetes and hypertension in pregnancy, gestational age at birth, the onset of labor, presentation of the second twin, sonographic fetal weight estimation, and fetal sex. The co-primary outcomes were overall intrapartum cesarean delivery and cesarean delivery of the second twin. Multivariable logistic regression models were used to estimate the probability of the study outcomes. Model performance was evaluated using measures of discrimination (the area under the receiver operating characteristic curve), calibration, and predictive accuracy. Internal validation was performed using the bootstrap resampling technique. A total of 1221 individuals met the study criteria. The rate of overall intrapartum cesarean delivery and cesarean delivery for the second twin was 25.4% and 5.7%, respectively. The most contributory predictor variables were nulliparity, term birth (≥37 weeks), a noncephalic presentation of the second twin, previous cesarean delivery, and labor induction. The models for overall intrapartum cesarean delivery and cesarean delivery of the second twin had good overall discriminatory accuracy (area under the receiver operating characteristic curve, 0.720; 95% confidence interval, 0.688-0.752 and 0.736; 95% confidence interval, 0.669-0.803, respectively) and calibration (as illustrated by the calibration plot and Brier scores of 0.168; 95% confidence interval, 0.156-0.180 and 0.051; 95% confidence interval, 0.040-0.061, respectively). The models achieved good specificity (66.7% and 81.6%, respectively), high negative predictive value (86.0% and 96.9%, respectively), and moderate sensitivity (68.1% and 57.1%, respectively). The prediction models developed in this study may assist care providers in counseling individuals regarding the optimal timing and mode of delivery in twin pregnancies by providing individualized estimates of the risk for intrapartum cesarean delivery.

Do Not Cut Off Your Tail: A Mega-Analysis of Responses to Auditory Perturbation Experiments.

The practice of removing "following" responses from speech perturbation analyses is increasingly common, despite no clear evidence as to whether these responses represent a unique response type. This study aimed to determine if the distribution of responses to auditory perturbation paradigms represents a bimodal distribution, consisting of two distinct response types, or a unimodal distribution. This mega-analysis pooled data from 22 previous studies to examine the distribution and magnitude of responses to auditory perturbations across four tasks: adaptive pitch, adaptive formant, reflexive pitch, and reflexive formant. Data included at least 150 unique participants for each task, with studies comprising younger adult, older adult, and Parkinson's disease populations. A Silverman's unimodality test followed by a smoothed bootstrap resampling technique was performed for each task to evaluate the number of modes in each distribution. Wilcoxon signed-ranks tests were also performed for each distribution to confirm significant compensation in response to the perturbation. Modality analyses were not significant (p > .05) for any group or task, indicating unimodal distributions. Our analyses also confirmed compensatory reflexive responses to pitch and formant perturbations across all groups, as well as adaptive responses to sustained formant perturbations. However, analyses of sustained pitch perturbations only revealed evidence of adaptation in studies with younger adults. The demonstration of a clear unimodal distribution across all tasks suggests that following responses do not represent a distinct response pattern, but rather the tail of a unimodal distribution. https://doi.org/10.23641/asha.24282676.

Surveillance and treatment of primary hepatocellular carcinoma (aka. STOP HCC): protocol for a prospective cohort study of high-risk patients for HCC using GALAD-score

BackgroundVietnam and Saudi Arabia have high disease burden of primary hepatocellular carcinoma (HCC). Early detection in asymptomatic patients at risk for HCC is a strategy to improve survival outcomes in HCC management. GALAD score, a serum-based panel, has demonstrated promising clinical utility in HCC management. However, in order to ascertain its potential role in the surveillance of the early detection of HCC, GALAD needs to be validated prospectively for clinical surveillance of HCC (i.e., phase IV biomarker validation study). Thus, we propose to conduct a phase IV biomarker validation study to prospectively survey a cohort of patients with advanced fibrosis or compensated cirrhosis, irrespective of etiologies, using semi-annual abdominal ultrasound and GALAD score for five years.MethodsWe plan to recruit a cohort of 1,600 patients, male or female, with advanced fibrosis or cirrhosis (i.e., F3 or F4) and MELD ≤ 15, in Vietnam and Saudi Arabia (n = 800 each). Individuals with a liver mass ≥ 1 cm in diameter, elevated alpha-fetoprotein (AFP) (≥ 9 ng/mL), and/or elevated GALAD score (≥ -0.63) will be scanned with dynamic contrast-enhanced magnetic resonance imaging (MRI), and a diagnosis of HCC will be made by Liver Imaging Reporting and Data System (LiRADS) assessment (LiRADS-5). Additionally, those who do not exhibit abnormal imaging findings, elevated AFP titer, and/or elevated GALAD score will obtain a dynamic contrast-enhanced MRI annually for five years to assess for HCC. Only MRI nearest to the time of GALAD score measurement, ultrasound and/or AFP evaluation will be included in the diagnostic validation analysis. MRI will be replaced with an abdominal computed tomography scan when MRI results are poor due to patient conditions such as movement etc. Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MRI will not be carried out in study sites in both countries. Bootstrap resampling technique will be used to account for repeated measures to estimate standard errors and confidence intervals. Additionally, we will use the Cox proportional hazards regression model with covariates tailored to the hypothesis under investigation for time-to-HCC data as predicted by time-varying biomarker data.DiscussionThe present work will evaluate the performance of GALAD score in early detection of liver cancer. Furthermore, by leveraging the prospective cohort, we will establish a biorepository of longitudinally collected biospecimens from patients with advanced fibrosis or cirrhosis to be used as a reference set for future research in early detection of HCC in the two countries.Trial registrationName of the registry: ClinicalTrials.govRegistration date: 22 April 2022Trial registration number: NCT05342350URL of trial registry record

Development and Validation of a Prediction Score for Low-Cardiac-Output Syndrome After Adult Cardiac Surgery

The authors aimed to develop a simple prediction score to help identify patients at high risk of low-cardiac-output syndrome after adult cardiac surgery. A single-center, retrospective, observational study. At a tertiary hospital. Adult patients who underwent on-pump cardiac surgery between April 2016 and March 2021. None. Among the 2,806 patients retained for final analyses, 355 (12.7%) developed low-cardiac-output syndrome. Using a stepwise backward variable selection procedure applied to a multivariate logistic regression, a prediction model, including 8 risk factors, could be identified-preoperative left ventricular ejection fraction, glomerular filtration rate <60 mL/min according to the Cockcroft formula or preoperative dialysis, combined surgery, nonelective surgery, mitral valve surgery for mitral valve regurgitation, history of extracardiac arteriopathy, preoperative hemoglobin <13 g/dL, and New York Heart Association functional class III or IV. A clinical prediction score was derived from the regression coefficients. The model had a good discriminative ability, with an area under the receiver operating characteristics curve of 0.8 (95% CI: 077-0.84). Using a threshold value of 5, the score had a 68% sensitivity, 79% specificity, a positive-predictive value of 33%, and a negative-predictive value of 94%. These results were validated on a validation sample using the bootstrap resampling technique. The authors developed a clinical score to facilitate the prediction of low- cardiac-output syndrome after adult cardiac surgery. This could help tailor patient management by contributing to the early identification of those at high risk of postoperative low cardiac output.

Prediction of birthweight and risk of macrosomia in pregnancies complicated by diabetes

Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia. This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus. This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g), large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique. A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively). The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia, large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.

Multimodality US versus Thyroid Imaging Reporting and Data System Criteria in Recommending Fine-Needle Aspiration of Thyroid Nodules.

Background Current guidelines recommend the use of conventional US for risk stratification and management of thyroid nodules. However, fine-needle aspiration (FNA) is often recommended in benign nodules. Purpose To compare the diagnostic performance of multimodality US (including conventional US, strain elastography, and contrast-enhanced US [CEUS]) with the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) in the recommendation of FNA for thyroid nodules to reduce unnecessary biopsies. Materials and Methods In this prospective study, 445 consecutive participants with thyroid nodules from nine tertiary referral hospitals were recruited between October 2020 and May 2021. With univariable and multivariable logistic regression, the prediction models incorporating sonographic features, evaluated with interobserver agreement, were constructed and internally validated with bootstrap resampling technique. In addition, discrimination, calibration, and decision curve analysis were performed. Results A total of 434 thyroid nodules confirmed at pathologic analysis (259 malignant thyroid nodules) in 434 participants (mean age, 45 years ± 12 [SD]; 307 female participants) were included. Four multivariable models incorporated participant age, nodule features at US (proportion of cystic components, echogenicity, margin, shape, punctate echogenic foci), elastography features (stiffness), and CEUS features (blood volume). In recommending FNA in thyroid nodules, the highest area under the receiver operating characteristic curve (AUC) was 0.85 (95% CI: 0.81, 0.89) for the multimodality US model, and the lowest AUC was 0.63 (95% CI: 0.59, 0.68) for TI-RADS (P < .001). At the 50% risk threshold, 31% (95% CI: 26, 38) of FNA procedures could be avoided with multimodality US compared with 15% (95% CI: 12, 19) with TI-RADS (P < .001). Conclusion Multimodality US had better performance in recommending FNA to avoid unnecessary biopsies than the TI-RADS. Clinical trial registration no. NCT04574258 © RSNA, 2023 Supplemental material is available for this article.

Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

Remnants of magma underplating by the Emeishan mantle plume within the lithosphere beneath southeastern margin of the Tibetan Plateau

The Emeishan Large Igneous Province (ELIP) is located along southeastern margin of the Tibetan Plateau, where long-wavelength crustal thickening and surface uplift have been attributed to the influx of weak crustal material from central Tibet into the lower crust beneath the Yangtze Craton. We have developed a new shear-wave velocity model for the lithosphere beneath eastern Tibet, based on a two-step linearized joint inversion technique of P-wave receiver functions (PRFs) with Rayleigh wave dispersion (10–60 s period) and Love wave dispersions (8–30 s period) for three dense seismic arrays. We applied bootstrap resampling technique to calculate the results of inversion. The resulting shear velocity model revealed two major low-velocity features in the middle–lower crust beneath southeastern Tibet. The horizontally extensive low-velocity zone (LVZ) beneath the northern Sichuan–Yunnan diamond-shaped block (SYDSB) can be interpreted as possible lower crustal flow from central Tibet, whereas the low-velocity anomaly observed beneath the Xiaojiang Fault is consistent with the presence of a partially molten layer enhanced by shear deformation along large faults. These two LVZs are separated by a lithospheric-scale high-velocity zone (HVZ) that is situated beneath the geologically inferred core of the ELIP. Based on other geophysical and geological evidence, we interpret this high-velocity anomaly body as remnants of the Emeishan plume-strengthened lithosphere that originally formed during the Permian volcanism, and we speculate that it has acted as a barrier to the hypothesized flow of lower crustal material from central Tibet since the late Cenozoic.

Development and internal validation of a prediction model to evaluate the risk of severe hemorrhage following mini-percutaneous nephrolithotomy.

To develop a new prediction model for assessing the severe hemorrhage events in post mini-percutaneous nephrolithotomy (mini-PCNL) patients and internally validate it, thus to guide decision making in clinical practice. The patients who underwent mini-PCNL were retrospectively reviewed. Potential risk factors were included as prediction variables for multivariate logistic regression analysis to identify independent risk factors, and prediction model was constructed. The predictive ability of the model was evaluated using the Concordance index (C-index) and Brier score. Bootstrapping resampling technique was used to perform internal validation. The related packages in R were used to generate the web application based on the prediction model. Multiple-tract was the strongest predictor of severe hemorrhage following mini-PCNL. Other risk factors were none or mild hydronephrosis, congenital anomalies of urinary system, urinary tract infection, operation time and stone peak Hounsfield unit. A prediction model was constructed to assess the probability of severe hemorrhage after mini-PCNL. The C-index and Brier score were 0.731 and 0.093, respectively after correcting for optimism, which signified the excellent discrimination and calibration. A new prediction model was developed to estimate risk of severe hemorrhage after mini-PCNL. It had been internally validated with good discrimination and calibration. The prediction model might be beneficial for endourologists in surgical decision-making and risk aversion.

Development of a Clinical Score to Stratify the Risk for Carbapenem-Resistant Enterobacterales Bacteremia in Patients with Cancer and Hematopoietic Stem Cell Transplantation

Identifying the risk factors for carbapenem-resistant Enterobacterales (CRE) bacteremia in cancer and hematopoietic stem cell transplantation (HSCT) patients would allow earlier initiation of an appropriate empirical antibiotic treatment. This is a prospective multicenter observational study in patients from 12 centers in Argentina, who presented with cancer or hematopoietic stem-cell transplant and developed Enterobacterales bacteremia. A multiple logistic regression model identified risk factors for CRE bacteremia, and a score was developed according to the regression coefficient. This was validated by the bootstrap resampling technique. Four hundred and forty-three patients with Enterobacterales bacteremia were included: 59 with CRE and 384 with carbapenem-susceptible Enterobacterales (CSE). The risk factors that were identified and the points assigned to each of them were: ≥10 days of hospitalization until bacteremia: OR 4.03, 95% CI 1.88-8.66 (2 points); previous antibiotics > 7 days: OR 4.65, 95% CI 2.29-9.46 (2 points); current colonization with KPC-carbapenemase-producing Enterobacterales: 33.08, 95% CI 11.74-93.25 (5 points). With a cut-off of 7 points, a sensitivity of 35.59%, specificity of 98.43%, PPV of 77.7%, and NPV of 90.9% were obtained. The overall performance of the score was satisfactory (AUROC of 0.85, 95% CI 0.80-0.91). Finally, the post-test probability of CRE occurrence in patients with none of the risk factors was 1.9%, which would virtually rule out the presence of CRE bacteremia.

Diagnostic nomogram for closed-loop small bowel obstruction requiring emergency surgery

PurposeThis study aimed to build a diagnostic model of closed-loop small bowel obstruction (CL-SBO) using clinical information, blood test results, and computed tomography (CT) findings. MethodsAll patients who were diagnosed with small bowel obstruction (SBO) and underwent surgery between January 1, 2018, and October 31, 2021, in the affiliated hospital of Qingdao university were reviewed, and their relevant preoperative information was collected. All variables were selected using univariate analysis and backward stepwise regression to build a diagnostic nomogram model. K-fold cross-validation and bootstrap resampling techniques were used for internal validation, and data from Qingdao Central Hospital were used for external validation. We also evaluated the diagnostic performance of each CT finding and performed subgroup analysis according to bowel ischemia in the closed-loop small bowel obstruction (CL-SBO) group. ResultsA total of 219 patients (95 in the CL-SBO group and 124 in the open-loop small bowel obstruction [OL-SBO] group) were included in our research. D-dimers (median 1085 vs. 690, P = 0.019), tenderness (77.9% vs. 59.7%, P = 0.004), more than one beak sign (65.3% vs. 30.6%, P < 0.001), radial distribution (18.9% vs. 6.5%, P = 0.005), whirl sign (35.8% vs. 8.9%, P < 0.001), and ascites (71.6% vs. 53.2%, P = 0.006) were selected as the predictive variables of the nomogram. This model's Harrell's C statistic was 0.786 (95% confidence interval (CI), 0.724–0.848), and the Brier score was 0.182. The Harrell's C statistic of external validation was 0.784 (95%CI, 0.664–0.905); the Brier score was 0.190. Regarding the CT findings, radial distribution, U/C-shaped loop, and whirl sign had high specificity (93.5%, 96.0%, and 91.1%, respectively), but low sensitivity (18.9%, 8.4%, and 35.8%, respectively). D-dimer levels and tenderness were also associated with bowel ischemia. ConclusionThe nomogram accurately predicted CL-SBO in patients with SBO, and surgery should be considered when patients have a high risk for developing CL-SBO.

The potential effects of COVID-19 lockdown and the following restrictions on the status of eight target stocks in the Adriatic Sea

The COVID-19 pandemic had major impacts on the seafood supply chain, also reducing fishing activity. It is worth asking if the fish stocks in the Mediterranean Sea, which in most cases have been in overfishing conditions for many years, may have benefitted from the reduction in the fishing pressure. The present work is the first attempt to make a quantitative evaluation of the fishing effort reduction due to the COVID-19 pandemic and, consequently, its impact on Mediterranean fish stocks, focusing on Adriatic Sea subareas. Eight commercially exploited target stocks (common sole, common cuttlefish, spottail mantis shrimp, European hake, red mullet, anchovy, sardine, and deepwater pink shrimp) were evaluated with a surplus production model, separately fitting the data for each stock until 2019 and until 2020. Results for the 2019 and 2020 models in terms of biomass and fishing mortality were statistically compared with a bootstrap resampling technique to assess their statistical difference. Most of the stocks showed a small but significant improvement in terms of both biomass at sea and reduction in fishing mortality, except cuttlefish and pink shrimp, which showed a reduction in biomass at sea and an increase in fishing mortality (only for common cuttlefish). After reviewing the potential co-occurrence of environmental and management-related factors, we concluded that only in the case of the common sole can an effective biomass improvement related to the pandemic restrictions be detected, because it is the target of the only fishing fleet whose activity remained far lower than expectations for the entire 2020.

Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration

Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The purpose this study is to apply the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period). Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data.