Chi-squared Automatic Interaction Detection Algorithm Research Articles

Determining the Type of Charolais Cows by Chaid Analysis

Factors influencing diagonal body length of Charolais cows (n=311) were analysed by the CHAID (Chi-squared Automatic Interaction Detector) method and are presented in this paper. CHAID algorithm groups the dependent variables (Y) so that the variance within groups is as low as possible and the variance between groups is as high as possible. The hierarchy of explanatory variables in this study was shown by the order of live weight, height at withers and age of the cows. The 1st, 2nd and 3rd nodus were formed by the live weight, according to the following categories: ≤600 kg, 600-645 kg, >645 kg. Diagonal body length increased parallel with the live weight categories. The 158 cows forming the first nodus were divided into two separate groups by the program, based on height at withers: 4th nodus (n=50, ≤ 128 cm); 5th nodus (n=108, >128 cm). Finally, the 5th nodus was separated – according to the age – into two groups in the last phase of the analysis: 6th nodus (n=53, ≤ 5.35 age, 172 cm); 7th nodus (n=55, > 5.35 age, 177 cm). Grouping individuals within the population would help to identify cow types, therefore CHAID analysis may be a possible useful method in beef cattle breeding.

Classification the Characteristics of Traffic Accident Victims in Pariaman Using the Chi-square Automatic Interaction Detection Algorithm

Traffic accidents are incidents that occur when motor vehicles collide on the road, resulting in damage to vehicles and road infrastructure, as well as the potential for material losses, injuries, physical damage, and even death for those involved. Data from the Indonesian National Police show that the number of traffic accident victims between 2010 and 2020 ranged from 147.798 to 197.560 people, with fatalities predominantly occurring among individuals aged 15-34. The high number of traffic accident victims has negative impacts on various aspects of life, ranging from material losses to physical damage to the victims. Classification is a technique used to group objects or data into pre-defined classes or categories based on their attributes or features. One method in the field of classification is Chi-Square Automatic Interaction Detection (CHAID). The results of the classification using this method indicate that the age of the victims and the type of accident are the most significant variables influencing the condition of traffic accident victims. The evaluation of the model using a confusion matrix yielded an accuracy rate of 92%. This indicates that the model performs well in overall data classification.

Main motivations and barriers to pro-environmental behaviour: a study from the employee’s perspective

PurposeThe purpose of this study is to identify groups of employees based on their motivations, detecting the main barriers that may influence their willingness to participate in the pro-environmental initiatives proposed by their employer.Design/methodology/approachTo identify the different groups of employees, an online survey was conducted, and the Chi-square automatic interaction detection algorithm segmentation technique was used with a sample of 483 employees from 9 Latin American universities.FindingsThe results allowed us to identify various segments, in which the main obstacle linked to intrinsic motivation is the university culture and, to a lesser extent, the lack of equipment, while for extrinsic motivation, the lack of infrastructure is the most determining factor. Likewise, the results reflect that, compared to the less motivated employees, those who show greater motivation (both intrinsic and extrinsic) are the ones who encounter the greatest barriers, so that the perceptions of the most motivated, as expert observers, help to identify the main obstacles that organisations must remove to promote pro-environmental behaviours among staff members.Practical implicationsThe results obtained help to guide the representatives or organisational leaders on the actions that generate the greatest impact in the mitigation of climate change from a motivational approach of behavioural prediction.Social implicationsThis study contributes to a more sustainable society by developing an understanding of how employees react to issues related to climate change. Knowing the perceptions of employees can be a turning point so that other members of society can get involved in pro-environmental behaviours.Originality/valueMany studies have analysed the intrinsic and extrinsic motivations of employees to engage in pro-environmental behaviours; however, as far as the authors are aware, this has not been analysed from the perspective of barriers to motivation.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms.

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

Potential of preoperative fluorodeoxyglucose-positron emission tomography/computed tomography to diagnose contralateral lymph node metastases in patients with oral cancer.

To establish a decision tree using preoperative PET/computed tomography (CT) parameters for detecting contralateral lymph node metastasis (CLNM) in oral cancer patients. In total, 140 patients with a confirmed histopathological diagnosis of oral carcinoma showed fluorodeoxyglucose accumulation of contralateral lymph nodes in PET images. Of 260 lymph nodes, eight were metastatic. We compared metastatic and non-metastatic nodes using Fisher-Freeman-Halton exact and Fisher's exact and Mann-Whitney U tests. The diagnostic performance was analyzed using receiver operating characteristic curves. We established a decision tree using exhaustive chi-squared automatic interaction detection algorithm. Five PET/CT parameters were significantly different between metastatic and non-metastatic nodes: the longest ( P = 0.015) and shortest ( P = 0.023) diameter, and the maximum standardized uptake values (SUVmax) of the contralateral node ( P = 0.030), primary tumor ( P < 0.001), and ipsilateral node ( P < 0.001). The area under the curves of SUVmax of the primary tumor (0.887), ipsilateral node (0.886), and longest diameter (0.752) were the largest and these three parameters were used as predictive criteria in the decision tree. The accuracy of the decision tree was 97.7% with 100% sensitivity and 97.6% specificity. Preoperative PET/CT imaging with a decision tree has the potential to diagnose CLNM in patients with oral cancer.

Performance Analysis of the CHAID Algorithm for Accuracy

The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it is adaptable to solving any kind of problem at hand. We are keenly aware of the non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine the perfect scope the CHAID algorithm fits into, this paper presented an analysis of the accuracy of the CHAID algorithm. We introduced the causes, applicable conditions, and application scope of the CHAID algorithm, and then highlight the differences in the branching principles between the CHAID algorithm and several other common decision tree algorithms, which is the first step towards performing a basic analysis of CHAID algorithm. We next employed an actual branching case to help us better understand the CHAID algorithm. Specifically, we used vehicle customer satisfaction data to compare multiple decision tree algorithms and cited some factors that affect the accuracy and some corresponding countermeasures that are more conducive to obtaining accurate results. The results showed that CHAID can analyze the data very well and reliably detect significantly correlated factors. This paper presents the information required to understand the CHAID algorithm, thereby enabling better choices when the use of decision tree algorithms is warranted.

Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study.

Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 ± 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.

Risk evaluation of carbapenem-induced liver injury based on machine learning analysis

IntroductionInformation regarding carbapenem-induced liver injury is limited, and the rate of liver injury caused by meropenem (MEPM) and doripenem (DRPM) remains unknown. Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of liver injury. Thus, we aimed to compare the rate of liver injury between MEPM and DRPM and construct a flowchart that can be used to predict carbapenem-induced liver injury. MethodsWe investigated patients treated with MEPM (n = 310) or DRPM (n = 320) and confirmed liver injury as the primary outcome. We used a chi-square automatic interaction detection algorithm to construct DT models. The dependent variable was set as liver injury from a carbapenem (MEPM or DRPM), and factors including alanine aminotransferase (ALT), albumin-bilirubin (ALBI) score, and concomitant use of acetaminophen were used as explanatory variables. ResultsThe rates of liver injury were 22.9% (71/310) and 17.5% (56/320) in the MEPM and DRPM groups, respectively; no significant differences in the rate were observed (95% confidence interval: 0.710–1.017). Although the DT model of MEPM could not be constructed, DT analysis showed that the incidence of introducing DRPM in patients with ALT >22 IU/L and ALBI scores > −1.87 might be high-risk. ConclusionsThe risk of developing liver injury did not differ significantly between the MEPM and DRPM groups. Since ALT and ALBI score are evaluated in clinical settings, this DT model is convenient and potentially useful for medical staff in assessing liver injury before DRPM administration.

Identity profile of young people experiencing a sense of risk on the internet: A data mining application of decision tree with CHAID algorithm

Social media, interactive games, and digital platforms are just some of the ways in which the internet has become part of our lives, and more specifically so among young people. Among other things, this technology is the reason young people are spending increasingly more time online than offline, prompting numerous risks that they are sometimes unaware of. The following quantitative research is based on this premise, and it pursues two objectives, namely, to discover whether or not young people experience a sense of risk linked to their use of the internet and identify the kind of young people that say they do or do not perceive any such risk. A total of 1991 young people answered the Technology Use Studies questionnaire. The classification tree technique based on the Chi-square Automatic Interaction Detector (CHAID) was applied to identify the predictive variables associated with the sense of risk in young people. Two highly significant results have been forthcoming: firstly, we present the factors with the biggest impact on a young person's internet-related sense of risk, involving isolation when they cannot go online, followed by variables such as gender, the use of TikTok, and videogames, the consumption of pornography, and distraction from study time. Secondly, our results indicate that the young people that feel isolated when they cannot go online are precisely the ones that do not experience a sense of online risk. It is therefore of vital importance for education to generate and provide young people with strategies that allow them to establish a healthy and responsible relationship with technologies such as the internet.

Pedestrian Neglect of Highway Footbridges and Safety Countermeasures: A Case Study from Ghana

In Africa, pedestrian deaths count for the majority of road traffic deaths, mainly resulting from bad crossing habits, including the neglect of footbridges. However, pedestrian behaviour remains understudied in Africa. In this article, we investigate the reasons for the non-use of footbridges along a major highway in Accra, Ghana, and test possible interventions. Using an intercept survey, we sampled 320 pedestrian violators by means of a questionnaire and structured interviews. Two decision trees were analysed using the chi-squared automatic interaction detection algorithm. The results indicated that men and students are more likely to disregard footbridges compared to other pedestrians. The length of the footbridge was cited as the main reason for non-compliance at designated crossings. Nonetheless, 93% of the respondents showed intentions to use the footbridges premised on some interventions. Pedestrian safety would be improved if shorter access points, adequate lighting and visibility are provided for footbridges and other road-crossing facilities. In this study, we propose countermeasures and reinforce the need for engineers and urban planners to carefully consider human behaviour in the design of urban road infrastructure.

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin.

Machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine. We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD). Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models. In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4. Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted.

A novel intubation prediction model for patients hospitalized with COVID-19: the OTO-COVID-19 scoring model

Objective The method for predicting the risk of intubation in patients with coronavirus disease 2019 (COVID-19) is yet to be standardized. This study aimed to introduce a new disease prognosis scoring model that may predict the intubation risk based on the symptoms, signs, and laboratory tests of patients hospitalized with the diagnosis of COVID-19. Method This cross-sectional retrospective study analyzed the intubation status of 733 patients hospitalized with COVID-19 diagnosis between March and December 2020 at Ondokuz Mayıs University Faculty of Medicine, Turkey, based on 33 variables. Binary logistic regression analysis was used to select the variables that significantly affect intubation, which constitute the risk factors. The Chi-square Automatic Interaction Detection algorithm, one of the data mining methods, was used to determine the threshold values of the important variables for intubation classification. Results The following variables found were mostly associated with intubation: C-reactive protein, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, age, lymphocyte count, and malignancy. The logistic function based on these variables correctly predicted 81.13% of intubated (sensitivity), 99.52% of nonintubated (specificity), and 96.86% of both intubated and nonintubated (accurate classification rate) patients. The scoring model revealed the following risk statuses for the intubated patients: very high risk, 75.47%; moderate risk, 20.75%; and very low risk, 3.77%. Conclusions On the basis of certain variables measured at admission, the OTO-COVID-19 scoring model may help clinicians identify patients at the risk of intubation and subsequently provide a prompt and effective treatment at the earliest.

The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies

Based on 130 climate signal indexes provided by National Climate Center of China, this paper established a decision tree diagnostic prediction model for Spring Kuroshio Sea Surface Temperature (SST) from 1961 to 2015 (65 years) by using Chi-Squared Automatic Interaction Detector (CHAID) algorithm in data mining and obtained five rule sets to determine whether Spring Kuroshio SST is high or not. Considering the data of the 44 years from 1961 to 2004 as the training set of the model and the other years as the test set, the training accuracy of the model can reach to 95.45% and the test accuracy can reach to 81.82%. Three types of Spring Kuroshio SST are different in intensity and distribution. The results show that the prediction model of Spring Kuroshio SST based on CHAID algorithm has a high prediction accuracy, with the reasonable and effective model and the well-thought-out decision rules. Moreover, based on the results of decision classification, the SST anomalies correspond to different distribution characteristics of summer daily precipitation anomalies in eastern China, which can provide a new idea and method for climate prediction of regional summer precipitation.

Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms

The purpose of this study was to predict live weight at breeding age (LW) based on weaning morphological traits, which birth weight (BW), weaning weight (WW), withers height (WH), back height (BH), rump height (RH), chest depth (CD), body length (BL), tail length (TL), chest girth (CG), leg girth (LG), front shark circumference (FSC), head length (HL), head width (HW), nose length (NL), ear length (EL), and ear width (EW). For this purpose, measurements were taken from 84 Honamli kids born in 2018. The study also included sex, birth type (BT), and ear type as the nominal predictors. For this purpose, two MARS (Multivariate Adaptive Regression Splines), which are interaction (MARS2) and non-interaction (MARS1), and based-tree algorithms, such as CART (Classification and Regression Tree), CHAID (Chi-Square Automatic Interaction Detector), and Exhaustive CHAID, were used by cross-validation 5 and compared with each other considering the predictive performance by taking into account nine predictive performances criteria. LW has a significantly positive and high linear relationship with WH (0.770), BH (0.770), RH (0.750), BL (0.750), and CG (0.770), respectively (p < 0.01). According to these criteria, second-order interaction MARS2 model had the best performance among all data mining algorithms. Also, the CHAID algorithm was the best predictor of LW among regression tree-based algorithms. The CHAID algorithm predicted that the Honamli goat with 14.426 < WW < 15.575kg and HW > 16.464cm had the heaviest LW at 56.268kg. The MARS2 model showed that the heaviest LW could be produced by WW > 16.10kg, HW > 17cm, Sex-Male × BL > 60cm, WW × BL < 50cm, BT-twin × WW < 15.60kg, BL > 50cm × CG > 62.4cm and male goats. Also, CHAID and MARS2 algorithms explain 92.00% and 94.50% of the variation in LW, respectively. According to the results, it can be concluded that the CHAID and MARS algorithms used in the prediction of LW at breeding age could give an idea to reveal the breed standards examined for breeding purposes. While determining that there are important statistical methods in defining body characteristics at weaning in a complex way, the body characteristics determined by these models can be used as indirect selection criteria.

Prone Apprehension Relocation Test significantly correlates with radiological instability scores of the hip.

ABSTRACTRecently, there was a debate about whether borderline dysplastic hips should be treated surgically with hip arthroscopy or periacetabular osteotomy (PAO). Current studies recommend a classification into stable and unstable hips. Therefore, radiological scores have been described in recent years. Likewise, a new clinical stability test with the Prone Apprehension Relocation Test (PART) has been described. However, there has been no correlation between the modern radiological scores and the PART. We prospectively studied a consecutive group of patients who presented to our clinic. The PART and radiological scores were assessed in these patients. We divided the patients into a PART-positive and a PART-negative group and analyzed the associated clinical and radiological findings. Out of 126 patients (126 hips) included, 36 hips (29%) were evaluated as PART positive. There were significantly more females in the PART positive group (P = 0.005). Comparing the PART groups, significant differences (P < 0.0001) were found for the lateral center edge angle (LCEA), Femoro-Epiphyseal Acetabular Roof (FEAR) index, Gothic arch angle (GAA), anterior wall index (AWI), the occurrence of the upsloping lateral sourcil (ULS) and signs of acetabular retroversion. The correlation analysis showed an association between LCEA, FEAR index, GAA, AWI, ULS and the PART. A chi-square automatic interaction detection algorithm revealed that the strongest predictor of positive PART was the GAA. In conclusion, a high correlation between the PART and known radiological instability parameters was found. Consequently, a combination of clinical instability testing and radiological instability parameters should be applied to detect unstable hips.

Effects of genotype and age on eggshell cuticle coverage and color profile in modern laying hen strains

The aim of this research was to investigate the effects of laying hen genotype and age on eggshell cuticle deposition. A total of 4,320 brown eggs were obtained from 3 modern hen strains (A, B, and C), currently used worldwide for commercial egg production, at different intervals of age (20–30, 40–50, and 60–70 wk). Four samplings of 120 randomly collected eggs were carried out for each genotype/interval of age. Eggs were individually weighed and cuticle blue staining was used to assess quality and degree of cuticle coverage. On each egg, the eggshell color profile was assessed before and after staining using the CIE L*a*b* system and these values were used to calculate ΔE*ab. A 4-point scale visual score (VS) system was also applied to estimate the degree of cuticle coverage after staining (0 = no coverage, 1 = partial coverage, 2 = total coverage - low degree, 3 = total coverage - high degree). The effects of genotype and age and their interaction on eggshell color attributes were assessed by means of factorial ANOVA, while omnibus Chi-Square and Chi-squared Automatic Interaction Detector algorithm were applied for the analysis of VS data. Overall, both genotype and age affected the eggshell color profile as well as the degree of cuticle coverage. Hen strain A showed better cuticle deposition in comparison with B and particularly C one, being ΔE*ab values significantly higher. The VS evaluation revealed that eggs with impaired cuticle coverage degree increased with the hen age (23, 34, and 37%, respectively for 20–30, 40–50, and 60–70 wk; P < 0.05). However, a significant interaction between genotype and age was observed: transition from early to late hen age resulted in a significantly different pattern of ΔE*ab changes in each genotype. The classification tree analysis confirmed that the hen genotype has a greater effect than the age on cuticle deposition. In conclusion, considering the importance of the cuticle in table egg production, these results highlight the crucial role exerted by the genotype on eggshell cuticle coverage.

Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms

This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL &gt; 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.

Dose Adjustment Model of Paliperidone in Patients With Acute Schizophrenia: A post hoc Analysis of an Open-Label, Single-Arm Multicenter Study.

This study aimed to investigate the factors that influenced the clinicians to adjust the paliperidone dose in the acute phase of schizophrenia. This was a post hoc study of an 8-week, open-label, single-arm multicenter trial which evaluated the efficacy, safety, and tolerability of flexible doses of paliperidone ER (3–12 mg/day) in patients with acutely exacerbated schizophrenia. Patients were divided into groups according to the dose at week 8 (3, 6, and 9–12 mg). The responder was defined as the reduction percentage in the Positive and Negative Syndrome Scale (PANSS) total score of ≥30%. According to the chi-squared automatic interaction detection algorithm, decision tree models predicting an increase in the dose of paliperidone ER were established. A decision tree, based on 4-week Marder positive factor, Clinical Global Impression (CGI), and BMI, was established to guide the dose adjustments of paliperidone ER in the acute phase of schizophrenia. The multivariable logistic regression analysis showed that lower age at onset, higher baseline PANSS positive subscale score, and lower baseline Personal and Social Performance Scale (PSP) score were significant predictors of increased dose in responders. Patients with young-onset age, severe baseline symptoms, and poor function are more likely to benefit from high dosage.

Systemic Lupus Erythematosus Disease Activity Score (SLE-DAS) enables accurate and user-friendly definitions of clinical remission and categories of disease activity

ObjectivesThere is an unmet need for accurate and user-friendly definitions of systemic lupus erythematosus (SLE) disease activity and remission. We aimed to derive and validate the SLE Disease Activity Score...

A hierarchy of correlates impacting adults’ sensor-based physical activity and sedentary time

ABSTRACT Investigation into correlates across all levels of the socio-ecological model predictive of objectively measured physical activity has rarely been assessed in adults. While considering a diversity of correlates, we determined which correlates best predict sensor-based moderate-to-vigorous physical activity (MVPA) and sedentary-time (ST) in adults. A Chi-squared Automatic Interaction Detection algorithm was used to hierarchize the correlates associated with high ST (≥66.6thpercentile) and sufficient MVPA (≥150 min/week) in 865 adults. The main correlate predictive of being active was currently partaking in sport/exercise. The following relevant correlates were being male for the exercisers and having trees in the neighbourhood for the non-exercisers. The final correlate to boost male exercisers’ MVPA was having lots of shops in the neighbourhood and not having television in the bedroom for women. The primary correlate for high ST was job activity level, with individuals having highly active jobs being less likely to exhibit high levels of ST; being single, male, and a former athlete also increased the chances of being highly sedentary. To increase adults’ MVPA, promotion of sport participation, neighbourhood landscape planning, shop availability, as well as limiting television in the bedroom must be prioritized. For counteracting ST, increasing workplace activity level is warranted.