BackgroundThe prognosis of hypertensive intracerebral hemorrhage is poor at high altitudes. The objective of this study was to explore whether hyperbaric oxygen can improve the results of computed tomography perfusion imaging and the neurological function of patients with HICH, and influence the hemoglobin concentration.MethodThe patients with HICH were treated with puncture and drainage. Twenty-one patients were treated with HBO after the operation, and the other patients received conventional treatment. CTP was performed twice, and all indices were measured. Scatter plots were (...) used to determine the effect of hemoglobin concentration on CTP imaging. Receiver operating characteristic curves were plotted to analyze the effects of hemoglobin concentration and hematoma volume on recovery results. The patients were followed up for 6 months.ResultsForty-one patients with HICH were treated with puncture and drainage. In total, 21 were treated with HBO after the operation, and 20 received conventional treatment as the control group. No significant differences in the CBV and CBF values of the two groups were noted before treatment. After 10 days, the values of CBV and CBF in the HBO group were significantly higher than those in the control group. A scatter diagram showed there was no significant in the HBO group, but significant correlation for the CBV and CBF values in the control group’s hematoma center and margin. The ROC curves showed that hematoma volume had an influence on prognosis of the control group. The Glasgow Coma Scale scores of the HBO group were significantly higher than those of the control group.ConclusionsHBO therapy can improve the postoperative CBV and CBF values of patients with HICH and ameliorate their prognoses. There was no significant correlation between HBO group and hemoglobin concentration on admission. (shrink)

ObjectiveThis study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI).MethodIn this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were applied to assess (...) the diagnostic accuracy of kinematic parameters in discriminating aMCI from healthy subjects.ResultsMultivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905–0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter’s sensitivity was 0.26–0.95 and specificity was 0.21–0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94).ConclusionWe developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan. (shrink)

COVID-19, the most severe public health problem to occur in the past 10 years, has greatly impacted people's mental health. Colleges in China have reopened, and how to prevent college students from suffering secondary damage due to school reopening remains elusive. This cross-sectional study was aimed to evaluate the psychological impact of COVID-19 after school reopening and explore via machine learning the factors that influence anxiety and depression among students. Among the 478 valid online questionnaires collected between September 14th and (...) September 20th, 74 showed symptoms of anxiety, and 155 showed symptoms of depression. Descriptive analysis of basic personal characteristics indicated that students at a higher grade, having relatives or friends who have been infected, fearing being infected, and having a pessimistic attitude to COVID-19 easily experience anxiety or depression. The Synthetic Minority Oversampling Technique was utilized to counteract the imbalance of retrieved data. The Akaike Information Criterion and multivariate logistic regression were performed to explore significant influence factors. The results indicate that exercise frequency, alcohol use, school reopening, having relatives or friends who have been infected, self-quarantine, quarantine of classmates, taking temperature routinely, wearing masks routinely, sleep quality, retaining holiday, availability of package delivery, take-out availability, lockdown restriction, several areas in school closed due to COVID-19, living conditions in the school, taking the final examinations after school reopening, and the degree to which family economic status is influenced by COVID-19 are the primary influence factors for anxiety or depression. To evaluate the effect of our model, we used 5-fold cross-validation, and the average area under the curve values of the receiver operating characteristic curves of anxiety and depression on the test set reached 0.885 and 0.806, respectively. To conclude, we examined the presence of anxiety and depression symptoms among Chinese college students after school reopening and explored many factors influencing students' mental health, providing reasonable school management suggestions. (shrink)

The coronavirus disease 2019 pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were (...) applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees. (shrink)

This study examined individual, task, and environmental constraints that influence the career progression of youth Brazilian elite basketball players and the probability of reaching Novo Basquete Brasil and to determine if the association of the relative age effect is a key factor in the career progression. The sample consisted of 4,692 male players who were registered to participate in at least one U15, U17, or U22 youth Brazilian basketball championship between 2004 and 2018. Athletes who reached a high-performance level were (...) coded like NBB players. The birthdates, height, body mass, playing position, geographic region, club, competition category, and team performance were retrieved from the official data archive of the Brazilian Basketball Confederation and the National Basketball League. The maturity status was estimated using the predicted age at peak height velocity. A binary logistic regression examined the influence of each characteristic on the probability of a youth Brazilian basketball player to reach the NBB. The receiver operating characteristic curves and the associated area under the curve were used to assess the discriminant ability of the model. The taller and younger players not selected early into national teams, without specialization by playing position, who participated in U22 national championship, migrated to the southeast region, and remained in the formation process over time have a greater chance to reach the NBB. The ROC curve demonstrated an AUC of 93%. A combination of individual, task, and environmental characteristics influences the sport career of a young Brazilian basketball player in reaching the NBB. Further, early-maturing athletes have a greater chance to reach higher performances. RAE influences lower-level categories, but not a “NBB player's” career progression. The coaches, stakeholders, and practitioners should perform a holistic evaluation of sport talent in terms of a constraint-based theoretical model with the aim of avoiding bias produced by the maturational status and RAE in the youth Brazilian elite basketball. (shrink)

Objective: The objective of this study was to evaluate the psychometric properties of the Chinese version of the decision regret scale. Methods: The data of 704 patients who completed the DRSc were used for the analyses. We evaluated the construct, convergent/discriminant, and known-group validity; internal consistency and test–retest reliability; and the item invariance of the DRSc. A receiver operating characteristic curve was employed to confirm the optimal cutoff point of the scale. Results: A confirmatory factor analysis (...) indicated that a one-factor model fits the data. The internal consistency and test–retest reliability [intraclass correlation coefficient = 0.71] of the DRSc were acceptable. The DRSc demonstrated unidimensionality and invariance for use across the sexes. It was confirmed that an optimal cutoff point of 25 could discriminate between patients with high and low decisional regret during clinical practice. Conclusion: The DRSc is a parsimonious instrument that can be used to measure the uncertainty inherent in medical decisions. It can be employed to provide knowledge, offer support, and elicit patient preferences in an attempt to promote shared decision-making. (shrink)

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an (...) automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload. (shrink)

Background: A short version of the Posttraumatic Diagnostic Scale comprising only re-experiencing symptom items has been recently validated on Japanese adults. This short-version-PDS had good psychometric properties among Japanese adults with and without posttraumatic stress disorder. The aim of this study was to translate and culturally validate the short-version-PDS for the Brazilian sociolinguistic context.Methods: A translation of the short-version-PDS was performed based on established guidelines. We enrolled 53 patients with PTSD as a potential comorbidity. The translation and cross-cultural adaptation of (...) the short-version-PDS included forward and back-translation by a Japanese Brazilian researcher and a certified translator; synthesis was achieved by consensus, backward translation, pilot test, and finalization. Content validity coefficient was used to assess quality of adaptation. Internal consistency was calculated using Cronbach's alpha coefficient. Spearman correlations were between the new short-version-PDS and the Brazilian version of the posttraumatic Stress Disorder Checklist for DSM-5, and a receiver operating characteristic curve was used to determine the best cut-off values for the short-version-PDS.Results: The short-version-PDS was well accepted by all subjects, none of the questions were experienced as inappropriate, and all questions of the 3 items were judged important. Item 1 presented CVCt = 0.92; item 2 had a CVCt = 0.87 and item 3 had a CVCt = 0.95. The internal consistency of the final version as measured by Cronbach's alpha was 0.78. The short-version-PDS scale correlated positively with the DSM-5 scale with a Spearman rho of 0.64 (95%CI [0.4-0.8], p 31.0 with sensitivity and specificity are 86.4 and 93.5%, respectively.Conclusions: This Brazilian Portuguese version of the short-version-PDS had good psychometric properties among Brazilian adults with and without PTSD. Transferability and generalizability of the cut-off scores should be further analyzed. (shrink)

BackgroundThe Basal ganglia (BG) played a crucial role in the brain-level mechanisms of chronic pain disorders. However, the functional changes of BG in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) are still poorly understood. This study investigated the BG subregions’ resting-state functional connectivity (rs-FC) in CP/CPPS patients compared with healthy controls.MethodsTwenty eight patients with CP/CPPS and 28 age- and education-matched healthy males underwent clinical measurements and 3T brain MR imaging, including T1-weighted structural images and resting-state functional imaging. The data were analyzed (...) by the seeded-based rs-FC analysis. Then, a machine learning method was applied to assess the feasibility of detecting CP/CPPS patients through the changed rs-FC.ResultsCompared with healthy males, patients presented decreased rs-FC between the BG subregions and right middle cingulate cortex, and correlated with pain (r = 0.51, p-uncorrected = 0.005) and urinary symptoms (r = –0.4, p-uncorrected = 0.034). The left superior temporal gyrus and right supramarginal gyrus showed decreased rs-FC with the BG subregions as well. The area under the receiver operating characteristic curve of 0.943 (accuracy = 80%, F1-score = 80.6%) was achieved for the classification of CP/CPPS patients and healthy males with support vector machine (SVM) based on the changed rs-FC.ConclusionThese findings provide evidence of altered BG subregions’ rs-FC in CP/CPPS, which may contribute to our understanding of the BG’s role in CP/CPPS. (shrink)

Lower extremity non-contact soft tissue injuries are prevalent in elite futsal. The purpose of this study was to develop robust screening models based on pre-season measures obtained from questionnaires and field-based tests to prospectively predict LE-ST injuries after having applied a range of supervised Machine Learning techniques. One hundred and thirty-nine elite futsal players underwent a pre-season screening evaluation that included individual characteristics; measures related to sleep quality, athlete burnout, psychological characteristics related to sport performance and self-reported perception of chronic (...) ankle instability. A number of neuromuscular performance measures obtained through three field-based tests [isometric hip strength, dynamic postural control and lower extremity joints range of motion ] were also recorded. Injury incidence was monitored over one competitive season. There were 25 LE-ST injuries. Only those groups of measures from two of the field-based tests, as independent data sets, were able to build robust models [area under the receiver operating characteristic curve score ≥0.7] to identify elite futsal players at risk of sustaining a LE-ST injury. Unlike the measures obtained from the five questionnaires selected, the neuromuscular performance measures did build robust prediction models. The inclusion in the same data set of the measures recorded from all the questionnaires and field-based tests did not result in models with significantly higher performance scores. The model generated by the UnderBagging technique with a cost-sensitive SMO as the base classifier and using only four ROM measures reported the best prediction performance scores. The models developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention in futsal. (shrink)

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually (...) used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic curve. In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource. (shrink)

Cardiovascular disease is a major complication of type 2 diabetes mellitus. In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social (...) information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve analysis was used to evaluate the model performance. Of participants, 272 were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. (shrink)

Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise (...) to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions. (shrink)

More and more studies showed that strabismus is not simply an ocular disease, but a neuro-ophthalmology disease. To analyze potential changes in brain activity and their relationship to behavioral performance in comitant strabismus patients and healthy controls. Our study recruited 28 patients with comitant strabismus and 28 people with matched weight, age range, and sex ratio as healthy controls. Using resting-state functional magnetic resonance imaging, we evaluated fALFF to compare spontaneous brain activity between comitant strabismus and healthy controls. We did (...) hospital anxiety and depression scale questionnaires for these patients. We found significantly lower fALFF value in comitant strabismus patients compared with controls in the left frontal superior medial gyrus and the right middle cingulum. In the latter region, fALFF was significantly negatively correlated with the hospital anxiety and depression scale, as well as the duration of disease. Receiver operating characteristic curve analysis indicated that the fALFF method has clear potential for the diagnosis of comitant strabismus patients. These results revealed abnormal spontaneous activity in two brain regions of comitant strabismus patients, which may indicate underlying pathologic mechanisms and may help to advance clinical treatment. (shrink)

Conscious access to fear-relevant information is mediated by threshold The present report proposed a model of access consciousness to fear-relevant information according to which there is a threshold for emotional perception beyond that the subject makes hits with no false alarm. The model was examined by having the participants performed a confidence-ratings masking task with fearful faces. Measures of the thresholds for conscious access were taken by looking at the receiver operating characteristics curves generated from a three-state low- (...) and high-threshold model by Krantz. Indeed, the analysis of the masking data revealed that the ROCs had threshold-like-nature rather continuous challenging in this fashion the classical signal-detection view on perceptual processing. Moreover, the threshold ROC curve exhibited the specific y-intercepts relevant to conscious access performance. The study suggests that the threshold can be an intrinsic property of conscious access, mediating emotional contents between perceptual states and consciousness. (shrink)

The rising demographic of older adults worldwide has led to an increase in dementia cases. In order to ensure the proper allocation of care and resources to this clinical group, it is necessary to correctly distinguish between simulated versus bona-fide cognitive deficits typical of dementia. Performance Validity Tests are specifically designed to assess a lack of effort and the possible simulation of cognitive impairment. Previous research demonstrates that PVTs may be sensitive to dementia, thus inaccurately classifying real memory impairment as (...) simulation. Here, we analyzed the sensitivity of PVTs in discriminating between dementia and simulation using receiver operating characteristic curve analyses. Further, we examined the potential need for adjusting cut-off scores for three stand-alone and one embedded PVT for Portuguese older adults with dementia. The results showed that all measures, except for the Coin in Hand— Extended version, were sensitive to one or more sociodemographic and/or cognitive variables, and it was necessary to adjust cut-off points for all measures. Additionally, the Rey-15 Item Memory Test did not demonstrate sufficient discriminating capacity for dementia. These results present important implications for clinical practice and the daily life of patients, as the use of incorrect cut-off points could impede patients from getting the resources they need. (shrink)

ABSTRACT The objective of this study was to determine the accuracy of a brief informed consent quiz to detect consent comprehension in individuals with cognitive impairment and to explore the degree to which cognitive domains and recent substance use, independently, predict comprehension. We performed a secondary analysis of two cross-sectional studies in individuals with substance use disorders. The ICQ total score was used as the index test and the Montreal Cognitive Assessment as reference standard in receiver operating (...) class='Hi'>characteristic curves. Two independent multiple binary logistic regression models were performed using cognitive domains and days of recent substance use as predictors of ICQ outcome. We analyzed data from 215 and 251 participants, respectively. The ICQ showed moderate accuracy for major cognitive impairment and lower accuracy for mild impairment. Optimal cutoff score was set at 10 points or less for detecting comprehension difficulty. Lower scores in Short-Term Memory, Attention, Language, and Orientation increased the probability of failing the ICQ. A procedure including both the ICQ and cognitive screening measure could improve the accuracy of consent comprehension assessments. (shrink)

A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor (...) the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies. (shrink)

PurposeTo investigate spontaneous brain activity in patients with dry eye and healthy control using the fractional amplitude of low frequency fluctuation technique with the aim of elucidating the relationship between the clinical symptoms of DE and changes in brain function.Material and MethodsA total of 28 patients with DE and 28 matched healthy volunteers were enrolled. Resting-state functional magnetic resonance imaging scans were performed in both groups. Then all subjects were required to complete a comprehensive Hospital Anxiety and Depression Scale. (...) class='Hi'>Receiver operating characteristic curve analysis was used to evaluate the differences in fALFF values between the two groups and their diagnostic value. Linear correlations between HADS and fALFF values in different brain regions of DE patients were analyzed using the Pearson correlation coefficient.ResultsPatients with DE had significantly higher fALFF values in the left calcarine sulcus than the HC group, while fALFF values in the bilateral middle frontal gyrus and right MFG/right inferior frontal gyrus were significantly lower in DE patients than in HC group. fALFF values had a high diagnostic value for differentiating patients with DE from the HC group. Right MFG and right MFG/IFG were significantly correlated with HADS values.ConclusionOur study found that DE mainly involved functional disorders in the brain areas of the left CS, bilateral MFG and right MFG/right IFG, which helped us to find possible clinical features of DE disease and reflected the potential pathological mechanism of DE. (shrink)

ObjectiveDespite its prevalence, insomnia disorder remains poorly understood. In this study, we used machine learning to analyze the functional connectivity disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation and pharmacotherapy.Materials and methods51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging, resting-stage functional MRI, and diffusion weighted imaging. Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with (...) or without rTMS. A recently developed machine learning technique, Hollow Tree Super was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.ResultsSubjects were classified into ID and control with an area under the receiver operating characteristic curve of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.ConclusionMachine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets. (shrink)

GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- to (...) middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

Systemic lupus erythematosus is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other risk factors were (...) collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression. The nomogram prediction model showed that the risk rate was from 0.01 to 0.80, and the receiver operating characteristic curve analysis showed that the area under the curve was 0.891. The calibration curve can intuitively show that the probability of depression predicted by the nomogram model is consistent with the actual comparison. The designed nomogram provides a highly predictive assessment of depression in patients with SLE, facilitating more comprehensive depression evaluation in usual clinical care. (shrink)

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work (...) presents that Social Media Analysis can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree, with the help of Term Frequency and Inverse Document Frequency. For experimental analysis, two datasets are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques. (shrink)

About one third of patients with epilepsy have seizures refractory to the medical treatment. Electrical stimulation mapping is the gold standard for the identification of “eloquent” areas prior to resection of epileptogenic tissue. However, it is time-consuming and may cause undesired side effects. Broadband gamma activity recorded with extraoperative electrocorticography during cognitive tasks may be an alternative to ESM but until now has not proven of definitive clinical value. Considering their role in cognition, the alpha and beta bands could further (...) improve the identification of eloquent cortex. We compared gamma, alpha and beta activity, and their combinations for the identification of eloquent cortical areas defined by ESM. Ten patients with intractable focal epilepsy participated in a delayed-match-to-sample task, where syllable sounds were compared to visually presented letters. We used a generalized linear model approach to find the optimal weighting of each band for predicting ESM-defined categories and estimated the diagnostic ability by calculating the area under the receiver operating characteristic curve. Gamma activity increased more in eloquent than in non-eloquent areas, whereas alpha and beta power decreased more in eloquent areas. Diagnostic ability of each band was close to 0.7 for all bands but depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patient’s degree of attention to the stimulus. We show that diagnostic ability can be increased by 3–5% by combining gamma and alpha and by 7.5–11% when gamma and beta were combined. We then show how ECoG power modulation from cognitive testing can be used to map the probability of eloquence in individual patients and how this probability map can be used in clinical settings to optimize ESM planning. We conclude that the combination of gamma and beta power modulation during cognitive testing can contribute to the identification of eloquent areas prior to ESM in patients with refractory focal epilepsy. (shrink)

Three experiments examined the influence of argument length on plausibility judgements, in a category-based induction task. The general results were that when arguments were logically invalid they were considered stronger when they were longer, but for logically valid arguments longer arguments were considered weaker. In Experiments 1a and 1b when participants were forewarned to avoid using length as a cue to judging plausibility, they still did so. Indeed, participants given the opposite instructions did not follow those instructions either. In Experiment (...) 2 arguments came from a reliable or unreliable speaker. This manipulation affected accuracy as well as response bias, but the effects of argument length for both reliable and unreliable speakers replicated Experiments 1a and 1b. The results were analysed using receiver operating characteristic (ROC) curves and modelled using multidimensional signal detection theory (SDT). Implications for models of category-based inductive reasoning, and theories of reasoning more generally, are discussed. (shrink)

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other (...) known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts. (shrink)

BackgroundWhereas the majority of bereaved persons recover from their grief without professional assistance, a minority develops pathological grief reactions. Etiological models postulate that dysfunctional cognitions may perpetuate such reactions. The Grief Cognitions Questionnaire assesses thoughts after bereavement in nine interrelated domains. A short form with four domains is often used. However, an evaluation of the psychometric properties of the GCQ-SF and its utility compared to the GCQ is lacking and these instruments have not been validated in German.MethodGerman bereaved persons responded (...) to an online survey containing the GCQ, measures of grief severity, grief rumination, symptoms of depression and anxiety, and optimism and pessimism. 585 participants were included. Item analyses and confirmatory factor analyses were conducted. Correlations between the GCQ and GCQ-SF and grief rumination, optimism and pessimism assessed construct validity. Criterion-related validity was assessed by comparing whether the correlation of the GCQ with grief severity was higher than with anxious and depressive symptoms. Logistic regression and receiver-operator characteristics compared the questionnaires on their ability to predict probable prolonged grief ‘caseness’.ResultsInternal consistencies for both questionnaires were identical and excellent. Confirmatory factor analyses obtained a satisfactory fit for models with nine and four correlated subscales and respective higher-order factor models. The GCQ and the GCQ-SF correlated higher with grief severity than with other measures of psychopathology. The logistic regression showed a significant association between the GCQ-SF and prolonged grief ‘caseness’. Of the remaining subscales of the GCQ, only one subscale contributed to the prediction. The ROC analyses showed nearly identical areas under the curve.ConclusionThe translated GCQ and GCQ-SF demonstrated very good psychometric properties. The correlations with grief severity highlight the questionnaires’ clinical relevance. The questionnaires possessed identical diagnostic specificity and sensitivity. Whenever a timesaving assessment of the most typical grief-specific cognitions is important, the GCQ-SF represents an alternative to the GCQ. The original GCQ may still be superior when a more detailed description of a bereaved person’s cognitions is desirable. (shrink)

Decisions of regulatory government bodies and courts affect many aspects of citizens’ lives. These organizations and courts are expected to provide timely and coherent decisions, although they struggle to keep up with the increasing demand. The ability of machine learning (ML) models to predict such decisions based on past cases under similar circumstances was assessed in some recent works. The dominant conclusion is that the prediction goal is achievable with high accuracy. Nevertheless, most of those works do not consider important (...) aspects for ML models that can impact performance and affect real-world usefulness, such as consistency, out-of-sample applicability, generality, and explainability preservation. To our knowledge, none considered all those aspects, and no previous study addressed the joint use of metadata and text-extracted variables to predict administrative decisions. We propose a predictive model that addresses the abovementioned concerns based on a two-stage cascade classifier. The model employs a first-stage prediction based on textual features extracted from the original documents and a second-stage classifier that includes proceedings’ metadata. The study was conducted using time-based cross-validation, built on data available before the predicted judgment. It provides predictions as soon as the decision date is scheduled and only considers the first document in each proceeding, along with the metadata recorded when the infringement is first registered. Finally, the proposed model provides local explainability by preserving visibility on the textual features and employing the SHapley Additive exPlanations (SHAP). Our findings suggest that this cascade approach surpasses the standalone stages and achieves relatively high Precision and Recall when both text and metadata are available while preserving real-world usefulness. With a weighted F1 score of 0.900, the results outperform the text-only baseline by 1.24% and the metadata-only baseline by 5.63%, with better discriminative properties evaluated by the receiver operating characteristic and precision-recall curves. (shrink)

In this study, the well-known CES-D depression scale was considered as a prototypical tool for assessing an individual’s inclination to respond at random. It was postulated that the set of the responses obtained from the participants of a questionnaire survey could be divided into three classes: the honest responses class; the pure random class, characterized by the same prob- ability of each admissible score; the smart random class, characterized by the same distribution of the probability of scores as in the (...) honest responses class. A two-step classifying procedure was recommended. According to this procedure, the first step should be to separate the pure random responses from the others. As needed, at the second step the smart random responses should be separated from the honest responses. The CES-D scale consists of sixteen direct questions and four reverse questions, each question with four admissible score values, 0, 1, 2, or 3. The material for the analyses includes all of the 4 294 967 296 possible arrangements of the four scores for the sixteen direct questions, and all of the 256 arrangements of the four scores for the four reverse questions, and all of the possible 49 · 13 = 637 pairs of the possible values of the mean scores from the sixteen direct questions and from the four reverse questions, respectively. It was assumed, without loss of generality, that the honest responses to the sixteen direct questions lead to the resulting distribution: P = (8/16; 5/16; 2/16; 1/16), for a score equal to 0, 1, 2, or 3, respectively. The original iterative procedure was used to compute the exact distributions of the mean scores in the pure random class, and in the smart random class. The discriminant ability to dis- tinguish between the two classes was characterized with the Receiver Operating Characteristic (ROC) curve. In result, the excellent discriminant properties of the CES-D were confirmed. The area under the ROC curve was estimated equal to AUC = 0.98 with standard error SE = 0.004 for the screening of the pure random responses from all of the others, and equal to AUC = 0.87 with standard error SE = 0.006 for the purpose of distinguishing the smart random responses from the honest ones. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the (...) analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

The Test Your Memory is a brief self-administered, cognitive screening test, currently used in several settings. It requires minimal administrator supervision and the computation of the final test score takes approximately 2 min. We assessed the discrimination ability of the Italian version of the TYM in detecting Mild Cognitive Impairment in clinical setting. TYM-I was administered to 94 MCI patients and 134 healthy controls. The clinical diagnosis of MCI was considered as the gold standard. An extended formal neuropsychological test battery (...) was used to define MCI subtypes. Receiver Operating Characteristic analyses were conducted to find the optimal cut-off and measure discrimination ability of TYM-I in detecting MCI. TYM-I had a similar area under the curve point estimate as Mini Mental State Examination. A TYM-I score lower or equal to 36 was found to be optimal cut off to detect MCI. The TYM-I showed the highest discrimination ability among individuals aged more than 70 and high educational level. The amnestic MCI subtype patients, compared to non-amnestic MCI patients, had worse performance in recall, orientation and visuospatial abilities TYM-I subscores. The TYM-I is a valid screening test in detecting cognitive dysfunction, easily carried out in clinical practice. The TYM-I subscores may allow to identify amnestic and non-amnestic MCI subtypes. (shrink)

Background and PurposeTranscranial direct current stimulation is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were (...) compared employing graph theoretical analysis based on the functional magnetic resonance imaging data. A support vector machine prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS. Our prediction model achieved the mean prediction accuracy of 68.3% after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome. (shrink)

Hydromechanical continuously variable transmission is capable of bearing large torque and has wide transmission range, which is suitable for high-power tractors. Dynamic characteristics could influence the tractor life, especially in a high-power tractor. Wet clutch is the crucial component in the HMCVT, which could smooth and soft power transmission. Therefore, it is important to study the dynamic characteristics and implement the wet clutch test of HMCVT. In this paper, AMESim is used to establish virtual models of gearbox, pump-controlled hydraulic motor (...) system, and shifting hydraulic system. Then, a simulation study of tractor operation under working condition is carried out. The internal and external meshing forces of the planetary row are analyzed. Finally, the wet clutch engagement process of HMCVT in the high-power tractor is tested to verify the oil pressure. The simulation results show that the values of internal and external meshing force become larger as the throttle opening increases. At the moment of shifting change, the meshing forces of the planetary gear have great impact. The clutch test shows that the trend of the oil filling curve obtained from the bench test is similar to that obtained from the theoretical curve, which verifies the simulation results. (shrink)

Background During the first weeks of March 2020 in Spain, the cases of severe respiratory failure progressively increased, generating an imbalance between the clinical needs for advanced life support (ALS) measures and the effective availability of ALS resources. To address this problem, the creation of triage committees (TC) was proposed, whose main function is to select the best candidates to receive ALS. The main objective of our study is to describe the clinical characteristics of the patients evaluated by the TC (...) of the Alcorcón Foundation University Hospital (AFUH) during the first wave of SARS CoV-2. Other objectives are to determine if there are differences between the patients considered candidates / not candidates for ALS and to analyze the functioning of the TC. Methods Retrospective observational study of all patients assessed by the AFUH TC. Results There were 19 meetings, in which 181 patients were evaluated, 65.4% male and with a mean age of 70.1 years. 31% had some degree of functional dependence, the Barthel median was 100 and Charlson 4. 58.5% were not considered a candidate for ALS at that time. The patients considered candidates to receive ALS were younger (72 vs 66; _p_ Conclusions The patients evaluated by the TC had a mean age of 70 years, high comorbidity and almost a third had some degree of functional dependence. More than half were not considered candidates for ALS at that time, these patients being older, with more comorbidity and a worse previous functional situation. TC decisions, based on objective clinical criteria, were almost always respected. Public institutions must get involved in triage procedures, which should and in our opinion must include the creation of TC in health centers. The implementation of Anticipated Decision programs (ADP) would help enable patients affected by triage decisions to participate in them. (shrink)

We address the question of whether it is possible to operate a time machine by manipulating matter and energy so as to manufacture closed timelike curves. This question has received a great deal of attention in the physics literature, with attempts to prove no- go theorems based on classical general relativity and various hybrid theories serving as steps along the way towards quantum gravity. Despite the effort put into these no-go theorems, there is no widely accepted definition of a time (...) machine. We explain the conundrum that must be faced in providing a satisfactory definition and propose a resolution. Roughly, we require that all extensions of the time machine region contain closed timelike curves; the actions of the time machine operator are then sufficiently "potent" to guarantee that closed timelike curves appear. We then review no-go theorems based on classical general relativity, semi-classical quantum gravity, quantum field theory on curved spacetime, and Euclidean quantum gravity. Our verdict on the question of our title is that no result of sufficient generality to underwrite a confident "yes" has been proven. Our review of the no-go results does, however, highlight several foundational problems at the intersection of general relativity and quantum physics that lend substance to the search for an answer. (shrink)

We address the question of whether it is possible to operate a time machine by manipulating matter and energy so as to manufacture closed timelike curves. This question has received a great deal of attention in the physics literature, with attempts to prove no-go theorems based on classical general relativity and various hybrid theories serving as steps along the way towards quantum gravity. Despite the effort put into these no-go theorems, there is no widely accepted definition of a time machine. (...) We explain the conundrum that must be faced in providing a satisfactory definition and propose a resolution. Roughly, we require that all extensions of the time machine region contain closed timelike curves; the actions of the time machine operator are then sufficiently “potent” to guarantee that closed timelike curves appear. We then review no-go theorems based on classical general relativity, semi-classical quantum gravity, quantum field theory on curved spacetime, and Euclidean quantum gravity. Our verdict on the question of our title is that no result of sufficient generality to underwrite a confident “yes” has been proven. Our review of the no-go results does, however, highlight several foundational problems at the intersection of general relativity and quantum physics that lend substance to the search for an answer. (shrink)

Using a comprehensive sample of U.S. non-profit hospitals from 2011 to 2015, we examine the effects of state community benefits regulation on the amount of charity care provided by and the operational efficiency of U.S. non-profit hospitals. First, we document that, under such regulations, non-profit hospitals provide more charity care and less compensated care as a proportion of net revenue. We infer from these findings that CBR has the potential to increase both non-profit hospitals’ amount of charity care and their (...) efficiency of operations. Second, by examining variation in CBR types, we find no differences between having provision or having reporting requirements on the amount of charity care offered. Moreover, when we consider CBR with both provision and reporting requirements, the combination of these two requirements does not incrementally enhance charity care offerings, suggesting that the requirements may serve as substitutes with comparable effect. Lastly, we show that several state-level characteristics influence the relationship between CBR and charity care: CBRs in states with a higher gross domestic product and percentage of revenue received in taxes have incrementally lower associations with charity care, while hospitals in states where populations have, on average, higher household incomes have greater associations between CBR and charity care. Our findings highlight the effects that state-enacted regulation can have on socially beneficial behavior by non-profits. (shrink)

Nowadays, many countries are promoting entrepreneurial education or the “innovation, entrepreneurship, and creativity” education. Entrepreneurial education can enhance a nation’s economic competitiveness and give rise to new business. At the moment, entrepreneurial courses are mostly designed by school teachers; however, while school teachers may possess business experience, they lack in entrepreneurial experience. Hence, entrepreneurial education courses call for experts with entrepreneurial experience to contribute to course designs and assist with course teachings. Entrepreneurial education not only improves a student’s entrepreneurial skills, (...) but also enables each student to explore their personal characteristics in order to advance the collaboration efficacy of the team as a whole. This study asked six experts with entrepreneurial experience in the information industry to work with school teachers in course design as well as teaching collaboration. The course design starts with three talk sessions given by professionals who share with students their thoughts and experiences in entrepreneurial products, team organization, fund raising, and profit calculation. Following that, each student is asked to share their own thoughts on entrepreneurial products and start searching for team members and planning their project. During the course, each team receives six individual advising sessions from the professionals, with topics ranging from product modeling, feasibility, product market estimation, fundraising methods, and profit calculation. The experts also provide each team member with personal trait analysis. Last but not least, the course invites five management-level industry professionals to play the role of venture capital investor and evaluate each team’s product modeling based on their presentation. This study reviews the grades given by the experts as well as the evaluations given by the three industry managers to assess whether the entrepreneurial education course’s student entrepreneur teams satisfy the industry’s expectations. (shrink)

Threshold- and signal-detection-based models have dominated theorizing about recognition memory. Building upon these theoretical frameworks, we have argued for a dual-process model in which conscious recollection and familiarity contribute to memory performance. In the current paper we assessed several memory models by examining the effects of levels of processing and the number of presentations on recognition memory receiver operating characteristics . In general, when the ROCs were plotted in probability space they exhibited an inverted U shape; however, when (...) they were plotted inzspace they exhibited a U shape. An examination of the ROCs showed that the dual-process model could account for the observed ROCs, but that models based solely on either threshold or signal-detection processes failed to provide a sufficient account of the data. Furthermore, an examination of subjects' introspective reports using the remember/know procedure showed that subjects were aware of recollection and familiarity and were able to consistently report on their occurrence. The remember/know data were used to accurately predict the shapes of the ROCs, and estimates of recollection and familiarity derived from the ROC data mirrored the subjective reports of these processes. (shrink)

BackgroundThe aim of this study was to explore the construct validity and diagnostic properties of the Reading the Mind in the Eyes Test in non-demented patients with amyotrophic lateral sclerosis.MaterialsA total of 61 consecutive patients and 50 healthy controls were administered the 36-item RMET. Additionally, patients underwent a comprehensive assessment of social cognition via the Story-Based Empathy Task, which encompasses three subtests targeting Causal Inference, Emotion Attribution, and Intention Attribution, as well as global cognitive [the Edinburgh Cognitive and Behavioral ALS (...) Screen ] and behavioral screening [the Frontal Behavioral Inventory ; the Dimensional Apathy Scale ; the Beck Depression Inventory ; and the State and Trait Anxiety Inventory-Y]. The construct validity of the RMET was tested by regressing it within a stepwise model that encompassed as predictors the abovementioned cognitive and behavioral measures, covarying for demographic and motor confounders. Receiver-operating characteristics analyses allowed exploring intrinsic and post-test properties of the RMET both in discriminating patients from HCs and in identifying patients with a defective SET-EA performance.ResultsThe RMET was solely predicted by the SET-EA and SET-IA. RMET scores showed high accuracy both in discriminating patients from HCs and in identifying patients with a defective SET-EA score, with adequate-to-optimal both intrinsic and post-test properties.DiscussionThe RMET is a convergently and divergently valid measure of affective social cognition in non-demented ALS patients, also featuring optimal intrinsic and post-test diagnostic properties in both case-control and case-finding scenarios. (shrink)