Nonparametric Research Articles

The CT Classification of Multilevel Cervical Ossification of the Posterior Longitudinal Ligament to Guide Hybrid Anterior Controllable Antedisplacement and Fusion vs. Posterior Laminoplasty.

For precise and minimally invasive treatment of ossification of the posterior longitudinal ligament of the cervical spine, the lifting segment is minimized, anterior controllable antedisplacement and fusion (ACAF) was refined and improved. In addition, the development of appropriate surgical procedures for the ossification of each segment was rarely reported. Therefore, this study aimed to compare the efficacy and safety of hybrid anterior controlled antedisplacement fusion (Hybrid ACAF) with laminoplasty for multilevel ossification of the posterior longitudinal ligament (OPLL). Between May 2018 and May 2021, 70 patients with multilevel OPLL were divided into a hybrid ACAF group and a laminoplasty group according to surgical methods. All patients were followed up for at least 1 year. Japanese Orthopaedic Association (JOA) score and recovery rate (JOARR), (VAS, NDI) score and C2-C7 Cobb angle, the sagittal vertical axis of the neck (SVA), and complications (cerebrospinal fluid leakage, C5 paralysis, etc.) were compared between the two groups by t test or non-parametric test. The operation time of hybrid ACAF was longer. C5 paralysis and axial pain were more common in the laminoplasty group, while dysphagia and hoarseness were more common in the hybrid ACAF group. At the last follow-up, the hybrid ACAF group had better recovery and maintenance of cervical lordosis and sagittal plane balance and a higher JOA score and recovery rate than the laminoplasty group. Hybrid ACAF can reduce the number of vertebral bodies and expand the decompression range, which is safe, effective, and tailored to local conditions. Compared with laminoplasty, hybrid ACAF is a precise alternative for patients with OPLL.

Extracellular Vesicle - MDM2-DNA as a Potential Liquid Biopsy Biomarker for Disease Identification in Retroperitoneal Liposarcoma.

We aimed to assess the levels of MDM2-DNA within extracellular vesicles (EVs) isolated from the serum of retroperitoneal liposarcoma (RLS) patients versus healthy donors, as well as within the same patients at the time of surgery versus post-operative surveillance visits. To determine whether EV-MDM2 may serve as a possible first-ever biomarker of liposarcoma recurrence. A hallmark of well-differentiated and de-differentiated (WD/DD) retroperitoneal liposarcoma is elevated MDM2 due to genome amplification, with recurrence rates of >50% even after complete resection. Imaging technologies frequently cannot resolve recurrent WD/DD-RLS versus postoperative scarring. Early detection of recurrent lesions, for which biomarkers are lacking, would guide surveillance and treatment decisions. WD/DD-RLS serum samples were collected both at the time of surgery and during follow-up visits from 42 patients, along with sera from healthy donors (n=14). EVs were isolated, DNA purified and MDM2-DNA levels determined through q-PCR analysis. Non-parametric tests were employed to compare EV-MDM2 DNA levels from patients versus control group, as well as the time of surgery versus post-surgery conditions. EV-MDM2 levels were significantly higher in WD/DD-RLS than controls (P= 0.00085). Moreover, EV-MDM2 levels were remarkably decreased in WD/DD-RLS patients after resection (P=0.00036), reaching values comparable to control group (P=0.124). During post-operative surveillance, significant increases of EV-MDM2 was observed in some patients, correlating with CT scan evidence of recurrent or persistent post-resection disease. Serum EV-MDM2 may serve as a potential biomarker of early recurrent or post-operatively persistent WD/DD-RLS, a disease currently lacking such determinants.

A refined approach for evaluating small datasets via binary classification using machine learning.

Classical statistical analysis of data can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are frequently limited to small datasets, which raises several questions regarding biases and coincidentally positive results. In this study, we present a refined approach for evaluating the performance of a binary classification based on machine learning for small datasets. The approach includes a non-parametric permutation test as a method to quantify the probability of the results generalising to new data. Furthermore, we found that a repeated nested cross-validation is almost free of biases and yields reliable results that are only slightly dependent on chance. Considering the advantages of several evaluation metrics, we suggest a combination of more than one metric to train and evaluate machine learning classifiers. In the specific case that both classes are equally important, the Matthews correlation coefficient exhibits the lowest bias and chance for coincidentally good results. The results indicate that it is essential to avoid several biases when analysing small datasets using machine learning.

A2/A2B Deceased Donor Kidney Transplantation Using A2 Titers Improves Access to Kidney Transplantation: A Single-Center Study

Rationale and ObjectiveThe option for A2/A2B deceased donor kidney transplantation was integrated into the kidney allocation system in 2014 to improve access for B blood group waitlist candidates. Despite excellent reported outcomes, center uptake has remained low across the United States. Here we examined the effect of implementing an A2/A2B protocol using a cut off titer of ≤ 1:8 for IgG and 1:16 for IgM on blood group B kidney transplant recipients at a single center. Study DesignRetrospective observational study. Setting& Participants: Blood group B recipients of deceased donor kidney transplants at a single center from January 1, 2019 to December 2022. ExposureRecipients of deceased donor kidney transplants were analyzed based on donor blood type: A2/A2B vs. blood group compatible. Outcomes1 year patient survival, death censored allograft function, primary non-function, delayed graft function, allograft function as measured by serum creatinine and eGFR at 1 year, biopsy proven rejection, and need for plasmapheresis. Analytical ApproachComparison between the A2/A2B and compatible groups were performed using the Fisher test or the Chi-Square test for categorical variables and the nonparametric Wilcoxon Rank-Sum Test for continuous variables. ResultsA total of 104 blood type B patients received a deceased donor kidney transplant at our center during the study period, 49 (47.1%) of which received an A2/A2B transplant. Waiting time was lower in A2/A2B recipients compared to blood group compatible recipients (57.9 months vs. 74.7 months, p=0.01). A2/A2B recipients were more likely to receive a donor after cardiac death (24.5% vs 1.8%, p<0.05) and experience delayed graft function (65.3% vs. 41.8%). There were no observed differences in the average serum creatinine or eGFR at 1-month, 3-months, and 1-year post kidney transplantation, acute rejection, or primary non-function. LimitationsSingle center study. Small cohort size limiting outcome analysis. ConclusionsImplementation of an A2/A2B protocol increased transplant volumes of blood group B waitlisted patients by 83.6% while decreasing the waiting time for transplantation by 22.5% with similar transplant outcomes.

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments*

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman’s repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.

A Non-parametric Analysis of Gender Differences With the Impacts of Applied Behavior Analysis on Autistic Individuals.

Introduction Considering the scarcity of research that directly investigates the differences between genders in their response to applied behavior analysis (ABA) therapy for individuals diagnosed with autism spectrum disorder (ASD), the objective of this study is twofold.First, it aims to reinforce the male-to-female ratio reported in existing scientific literature, thereby contributing to a broader understanding of gender distribution in ABA therapy for ASD. Second, it seeks to identify gender-based differences in aggregate target behaviors at various time intervals using three distinct datasets. The goal is to determine if gender influences the effectiveness of ABA therapy for ASD, which could inform future therapeutic strategies. Ultimately, this study strives to enhance our understanding of gender disparities in ABA therapy response among ASD individuals and hopes to improve therapeutic outcomes for all, regardless of gender. Materials and methods Three to five behavioral technicians per child collected daily general target mastery data for 263 individuals with autism. This data was gathered using a large N design through retrospective chart reviews within the "Catalyst" tracking software (DataFinch Technologies, Atlanta,USA). Three separate datasets were collected from June 7, 2023to January 7, 2024. Behavior analysts employed a mixed model of discrete trial training, mass trials, and naturalistic environment treatment over seven months. General target mastery data was assembled for 259 children and four adults, with seven data instances missing. Descriptive statistics encompassed central tendency and dispersion measures, including the data distribution's mean, standard deviation, median, and range. Non-parametric inferential analysis was performed with the Mann-Whitney U test. Results Mann-Whitney U computations resulted in non-significant gender differences on all gender comparisons for the three datasets covering the seven-month timeframe. Dataset #1:Time 1-(U=727.5, p=0.846, ή2=0.0002, Time 2-(U=736, p=0.910, ή2=0.00005), Time 3-(U=687.5, p=0.569, ή2=0.001) Dataset #2:Time 1-(U=781, p=0.383, ή2=0.003), Time 2-(U=819.5, p=0.585, ή2=0.001), Time 3-(U=825, p=0.618, ή2=0.001) Dataset #3:Time 1-(U=395, p=0.198, ή2=0.007),Time 2-(U=373.5, p=0.365, ή2 =0.003),Time 3-(U=363, p=0.471, ή2=0.002),Time 4-(U=366.5, p=0.436, ή2 =0.003),Time 5-(U=371, p=0.391, ή2=0.003),Time 6-(U=394, p=0.208, ή2=0.007),Time 7-(U=373, p=0.373, ή2=0.003),Time 8-(U=371.5, p=0.387, ή2=0.003),Time 9-(U=464.5, p=0.512, ή2=0.002),Time 10-(U=356.5, p=0.546, ή2=0.002),Time 11-(U=357.5, p=0.535, ή2=0.002),Time 12-(U=350.5, p=0.346, ή2=0.004) Conclusions This study suggests no significant gender differences in response to ABA therapy among individuals with autism, indicating its potential effectiveness for both genders. However, these findings should be interpreted cautiously due to statistical uncertainties reflected in the broad confidence intervals as they hint at possible substantial gender differences. Further research, including an extension study, must confirm these results and understand potential gender nuances in ABA therapy response. This could help tailor more effective, personalized therapeutic strategies for individuals with autism.

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Monetary value of health-a practical decision-making framework combining equity considerations and WTP.

We estimate the first monetary value of a health gain in Croatia to inform the debate about the appropriate "demand-side" cost-effectiveness thresholds in Croatia but also Central and Eastern Europe, where such debates are still uncommon. We test the empirical support for two equity considerations: age and severity operationalized as proportional shortfall (PS), and propose a pragmatic framework for combining equity considerations with the monetary value of health into a single threshold. We used the contingent valuation method to elicit the willingness to pay per Quality-Adjusted Life Year (QALY) in Croatia, using a representative sample of the population (N = 1,500, online survey). 29 EQ-5D health states were valued using payment scales and open-ended question as payment vehicles. To test the hypotheses, we used both parametric tests and non-parametric tests. Multilinear regression was employed to test the theoretical validity of the results. The monetary value of a health gain in Croatia is equivalent to 1.15 of GDP per capita (equaling €17,000). Age of patients seems to be an important equity-related characteristic. The WTP per QALY in the age-neutral risk group (€11,900) was nearly equivalent to the WTP per QALY in the adult (neutral) risk group (€11,700) but lower by 16% compared to the WTP per QALY estimated in children (€14,200; p = 0.00). WTP estimates are theoretically valid and to, a small degree, scale sensitive. There is a positive association between the level of proportional shortfall and willingness to pay. To increase the usefulness of our results for the policy-makers, we combine the elicited preferences into a single decision-making framework and construct several cost-effectiveness thresholds based on willingness to pay and equity-related preferences. Based on empirical results, cost-effectiveness thresholds could range up to €20,308 for the most severe health conditions in children or could be lowered to €16,777 for less severe health conditions. In Central and Eastern Europe, in spite of a growing understanding of the importance of further developing value-based assessment frameworks there has been very little empirical research to guide, inform and promote this development. Countries in this region use mainly GDP-based thresholds without empirical evidence to support such important decisions. This may lead to thresholds that are too high, with detrimental consequence for the pricing and reimbursement systems.

Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer

PurposeTo look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. Materials and methodsA total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. ResultsThe histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = −0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (−) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. ConclusionThe histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.

The Effect of Clavicular Tunnel Position on Reduction Loss in Patients with Acute Acromioclavicular Joint Dislocations Operated with a Single-Bundle Suspensory Loop Device.

The treatment of acromioclavicular joint (ACJ) dislocations offers numerous options, and ongoing debates persist regarding their comparative effectiveness. Among these options, the suspensory loop device (SLD) is one of the most favored treatment modalities. Despite the observed high reduction loss rate associated with SLD, the treatment yields favorable clinical outcomes. This study aimed to investigate the clinical outcomes of patients with acute type 3 and 5 ACJ dislocations who underwent open and arthroscopic procedures using a single-bundle SLD, and to evaluate the effect of clavicular tunnel position on reduction loss. Thirty-seven eligible patients diagnosed with acute type 3 and type 5 ACJ dislocation who underwent open and arthroscopic surgery with a single-bundle SLD between January 2015 and March 2022 were evaluated retrospectively. Demographic data and radiological measurements including coracoclavicular (CC) interval, clavicle length (CL), and implant distance (ID) were recorded. The ID/CL ratio was calculated and a value between 0.17 and 0.24 was considered as "acceptable implant position". Reduction loss and other complications were noted. Patients were divided into two groups: open (Group 1) and arthroscopic (Group 2). Constant Murray Score (CMS) and Visual Analog Scale (VAS) were used for clinical and functional outcomes. Non-parametric tests were used for statistical analysis of variables. The study included six females (16.2%) and 31 males (83.8%) with a mean age of 40.2 ± 14.7 years (range: 20-75). The mean follow-up period was 22.3 ± 16.7 months (range: 6-72). The average time from trauma to surgery was 6.3 ± 5.3 days (range: 1-18). At the last follow-up, the CMS was 89.3 ± 8.8 and the VAS score was 2.1 ± 0.9. The mean ID/CL ratio was 0.19 ± 0.1 and 19 patients (51.4%) were between 0.17 and 0.24. Reduction loss was observed in nine patients (24.3%). There were no significant differences between Group 1 and Group 2 regarding operation time (p = 0.998), ID/CL ratio (p = 0.442), reduction loss (p = 0.458), CMS (p = 0.325), and VAS score (p = 0.699). Of the 28 patients without reduction loss, 16 had an ID/CL ratio between 0.17 and 0.24 (p = 0.43). Furthermore, within the 0.17-0.24 interval, CMS was higher with an average of 91.8 ± 5.1 compared to the other intervals (p = 0.559). The clinical and functional outcomes of acute type 3 and type 5 ACJ dislocation operated open and arthroscopically with single-bundle SLD are similar and satisfactory. A clavicular tunnel position in the range of 0.17-0.24 (ID/CL ratio) is recommended to maintain postoperative reduction.

Screening for Autism Spectrum Disorder in Children and Adolescents With Leber's Congenital Amaurosis

PurposeTo investigate autism spectrum disorder (ASD) indicators in children with Leber congenital amaurosis (LCA). MethodsCross-sectional, correlational, and prospective study. Participants included patients aged 2 to 16 years with LCA confirmed by genetic testing. There were 20 individuals with ciliopathies (LCA cilio) and 26 with other gene mutations (LCA other). The instrument used for ASD screening was the Autism Behavior Checklist (ABC). Marginal descriptive analyses, non-parametric tests, and a linear regression model were conducted. ResultsOf the 46 participants, 6 had ASD scores. There was no statistically significant correlation between the different groups (LCA cilio and LCA other) (p=0.438). There was no statistically significant correlation between age and ASD (p=0.308). However, there was a statistically significant correlation between visual acuity and ASD (p=0.008) and between male gender and ASD (p=0.025). ConclusionsThis study suggests that there is no correlation between LCA cilio, LCA other and ASD. These findings bring new insights to the existing literature, which previously lacked robust data on the relationship between LCA and ASD. These data demonstrate that visual acuity plays a crucial role in the development of children with visual impairment as poorer visual acuity is associated with a higher incidence of ASD. Based on this study, early interventions can be designed, especially for individuals without light perception, with the aim of maximizing their developmental outcomes. Furthermore, such data indicates that any improvement in visual acuity outcomes in treatment clinical trials become relevant for child development.

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Ex vivo biomechanical evaluation and comparison of lateral femoro-fabella ligament suture and lateral suture with bone anchor for cranial cruciate ligament repair in cats.

To compare the biomechanical properties of lateral femoro-fabella ligament suture (FFLS) and lateral suture with a bone anchor suture (BAS) for management of feline cranial cruciate ligament disease. 12 femurs from 6 mature cat cadavers. The samples were collected from April to June 2023. The specimens had an FFLS and, subsequently, BAS placed and were positioned into a biomechanical testing machine, preloaded from 5 N to 15 N for 100 cycles, and subsequently, a load to suture failure was applied. The displacement at 5 N and 15 N, the total precycle displacement (millimeters), the force at 3 mm displacement and at failure (newtons), the displacement at failure (millimeters), and the stiffness to failure (Newton:millimiter) were recorded. Nonparametric Wilcoxon signed-rank tests were used to compare data between the 2 groups. The displacement at 5 N and 15 N and the total precycle displacement were significantly higher in the FFLS group compared to the BAS group. Additionally, the FFLS group results showed less consistent displacement and marked variability. The force (newtons) at 3 mm displacement was higher in the BAS group. There was no significant difference in force and no significant difference in displacement at failure between the 2 groups. However, the stiffness to failure (N/mm) was significantly higher in the BAS group. BAS represented a more stable and reliable femur attachment for extracapsular suture in cats. To demonstrate the stability and reliability between BAS and FFLS and influence implant selection in the treatment of cranial cruciate ligament rupture in cats with evaluation of biomechanical properties.

Nonparametric high-dimensional multi-sample tests based on graph theory

High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introduce two key properties desirable for graphs in testing homogeneity. Roughly speaking, these properties may be described as: unboundedness of edge counts under the same distribution and boundedness of edge counts under different distributions. It turns out that the minimum spanning tree violates these properties but the shortest Hamiltonian path posses them. Based on the shortest Hamiltonian path, we propose two combinations of edge counts in multiple samples to test for homogeneity. We give the permutation null distributions of proposed statistics when sample sizes go to infinity. The power is analyzed by assuming both sample sizes and dimensionality tend to infinity. Simulations show that our new tests behave very well overall in comparison with various competitors. Real data analysis of tumors and images further convince the value of our proposed tests. Software implementing the test is available in the R package GRelevance. Supplemental materials are available online.

Impact of Neoadjuvant Chemoimmunotherapy on Surgical Outcomes and Time to Radiation in Triple-Negative Breast Cancer.

We examined the association between immunotherapy-containing and standard chemotherapy regimens with treatment delays and postoperative complications in stage II-III triple-negative breast cancer. The effect of immune-related adverse events (irAEs) was compared. We compared 139 women treated with neoadjuvant pembrolizumab plus chemotherapy (KEYNOTE-522 regimen) from August 2021 to September 2022 with 287 consecutive patients who received neoadjuvant chemotherapy alone prior to July 2021 and underwent surgery. Baseline characteristics, time to treatments, and surgical complications were compared using two-sample non-parametric tests. Linear regression evaluated association of irAEs with time to surgery and radiation. Logistic regression identified factors associated with surgical complications. Age, body mass index, race, American Society of Anesthesiologists (ASA) class, and mastectomy rates were similar among cohorts. No clinically relevant difference in time from end of neoadjuvant treatment to surgery was observed [KEYNOTE-522: median 32 (IQR 27, 43) days; non-KEYNOTE-522: median 31 (IQR 26, 37) days; P = 0.048]. Time to radiation did not differ (P = 0.7). A total of 26 patients (9%; non-KEYNOTE-522) versus 11 (8%; KEYNOTE-522) experienced postoperative complications (P = 0.6). In the KEYNOTE-522 cohort, 59 (43%) of 137 patients experienced 82 irAEs; 40 (68%) required treatment. Older age (P = 0.018) and ASA class 4 (P = 0.007) were associated with delays to surgery after adjusting for clinical factors. Experiencing ≥ 1 irAE was associated with delay to radiation (P = 0.029). IrAEs were not associated with surgical complications (P = 0.4). We observed no clinically meaningful difference between times to surgery/adjuvant radiation or postoperative complications and type of preoperative chemotherapy. IrAEs were associated with delay to adjuvant radiation but not with postoperative complications or delay to surgery.

The influence of gender and sport on popliteal angle and dorsiflexion in junior high school students.

The aim of the study was to assess factors affecting the popliteal angle and foot dorsiflexion, in particular gender. The subjects were 142 students from the 2nd and 3rd year of Poznań junior high schools. The participants included 57 girls and 87 boys. Three raters examined each subject: a specialist in orthopaedics, a resident doctor and a physical therapy student. Foot dorsal flexion was tested in a supine position with lower limbs extended. Next, dorsal flexion was evaluated with the knee and hip in 90 degrees of flexion. Finally, a passive knee extension (PKE) test was carried out. The significance of the PKE test is that the lower the angle the more flexible the hamstrings. This is because the PKE measurement is the distance to the right angle, that is a full knee extension with the hip flexed. The non-parametric test (Mann-Whitney) and the Student's t-test showed differences between the female and male gender in the measurements of the popliteal angle (p < .05000). The correlation was negative, which means that the hamstrings are more flexible in girls. No differences were found between gender and passive foot dorsiflexion and dorsiflexion with a flexed hip and knee. No differences were found between the group with the extended PE curriculum and the group with the standard number of PE classes in the range of motion of foot dorsiflexion and the value of the popliteal angle. Girls between 13 and 15years old have a significantly larger hamstring flexibility, which is confirmed by the tests of the popliteal angle. No differences were found in dorsiflexion between girls and boys who have not been trained using a training model.

Contrast-enhanced ultrasound for differentiating benign from malignant focal solid renal lesions in pediatric patients

The contrast-enhanced ultrasound (CEUS) has been mainly applied to adults to differentiate benign and malignant renal lesions, however, the characteristics of CEUS in pediatric has not been as well studied as in adults. In the present work, the eligible pediatric patients who underwent renal CEUS between March 2016 and February 2023 were retrospectively analyzed. It included 20 lesions (median diameter, 8.4 cm; range, 1.8–18.0 cm) from 20 patients (median age, 28.0 months; range, 3.0–212.0 months; 9 boys) in malignant group and 5 lesions (median diameter, 3.8 cm; range, 1.3–7.5 cm) from 5 patients (median age, 25.0 months; range, 0.7–216.0 months; 2 boys) in benign group. The diagnostic performance was assessed. Nonparametric and Chi-square tests were performed. With hyperenhancement plus wash-out, CEUS showed a sensitivity of 95.0% [95% confidence interval (CI): 75.1%, 99.9%], a specificity of 80.0% (CI: 28.4%, 99.5%), a positive predictive value of 95.0% (CI: 75.1%, 99.9%) and a negative predictive value of 80.0% (CI: 28.4%, 99.5%). It suggested that CEUS is a valuable technique for identifying between malignant and benign renal lesions in children.

Wavelet Feature Screening

An initial screening of which covariates are relevant is a common practice in high-dimensional regression models. The classic feature screening selects only a subset of covariates correlated with the response variable. However, many important features might have a relevant albeit highly nonlinear relation with the response. One screening approach that handles nonlinearity is to compute the correlation between the response and nonparametric functions of each covariate. Wavelets are powerful tools for nonparametric and functional data analysis but are still seldom used in the feature screening literature. We propose a wavelet feature screening method that can be easily implemented, and we prove that, under suitable conditions, it captures the true covariates with high probability. Simulation results also show that our approach outperforms other screening methods in highly nonlinear models. We apply feature screening to two datasets about ozone concentration and epilepsy. In both applications, the proposed method selects features that match findings in the literature of their respective research fields, illustrating the applicability of feature screening. Supplementary material for this article is available online.

The prevalence of neck pain, back pain and low back pain among third-year medical students at universities in the metropolitan region of Porto Alegre in times of Covid-19

Objective: To determine the prevalence of cervical, dorsal and lumbar pain caused by switching to remote classes during the Covid-19 pandemic. Methods: This is an original article based on a cross-sectional study among men and women over the age of 18 who are third-year medical students to assess the incidence of neck pain, back pain and low back pain, using online forms with questions about physical and mental health. Results: Around 60% of the participants said they had adapted their study environment because of the remote classes, with a further 70% saying they were attending classes in the office, with their backs poorly supported. In addition, there was a low number of overweight students who performed daily stretching. Conclusion: The data was analyzed using tables, descriptive statistics and the statistical test: Mann-Whitney Non-parametric Test and Krsukal-Wallis Non-parametric Test and the importance of further research was highlighted, given that this research topic is essential for the prevention of possible comorbidities.

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.