Children's Predictions Research Articles

Saliva Urea Nitrogen Improves Mortality Prediction in Children with Malnutrition and AKI

Saliva Urea Nitrogen Improves Mortality Prediction in Children with Malnutrition and AKI

MicroRNA Expression and Target Prediction in Children with Nephrotic Syndrome

Background: Nephrotic syndrome is a common cause of kidney diseases in children. Many studies have examined the association of microRNAs playing potential roles in many pathophysiological functions. We investigated the expression pattern of the microRNAs miR-17-5P, miR-155p, miR-424-5p in children with steroid sensitive nephrotic syndrome (SSNS) and steroid resistance nephrotic syndrome (SRNS), along with the healthy subjects. Materials and Methods: Total RNA was isolated from the urine samples from the three groups (SSNS n = 100, SRNS n = 100, and healthy control group n = 100). Bioinformatics tools such as miRWalk and miR-Tar link were used in predicting targets for the microRNAs. Online database and g profiler software are used to evaluate the targets based on the biological functions. The expression pattern for the candidate microRNAs was carried out using quantitative real time polymerase chain reaction (RT-PCR) equipment. Results:miR-424 and miR-155 were upregulated in SRNS group while miR-17 was downregulated in SRNS group. miR-424-5p and miR-155p was up regulated in SRNS group while miR-17-5p was downregulated. Conclusion: Combined analysis of gene expression along with studied candidate microRNAs can give better understanding of the pathogenesis of childhood nephrotic syndrome.

Promoting scientific understanding and conceptual change in young children using explanations and guidance.

Evaluating evidence and restructuring beliefs based on anomalous evidence are fundamental aspects of scientific reasoning. These skills can be challenging for both children and adults, especially in domains where they possess inaccurate prior beliefs that can interfere with the acquisition of correct scientific information (e.g., heavier objects fall faster than light ones). Across two experiments, we examined the additive benefit of combining explanations with guided activities to promote conceptual change. In Experiment 1 (N = 238), 4- and 5-year-olds were randomly assigned to one of three conditions: guidance with explanations, guidance only, or baseline. The guided conditions varied only in the presence or absence of conceptual information (i.e., explanation about gravity). Pre- and posttest measures showed that children's predictions improved from both guided conditions compared to the baseline condition but did not significantly differ from each other. Experiment 2 (N = 80, 5-year-olds) included a delay test and assessed children's learning through the justification of their predictions. Although children's performance at the immediate posttest improved in both conditions, in the guidance only, children's performance returned to the pretest levels of understanding after the delay. Children in the guidance with explanations condition had greater understanding at posttest, retained this understanding long term, and transferred it to objects with the same weight. These findings highlight the role of explanations in aiding children's long-term learning from anomalous evidence in guided activities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

DKA Prediction in Children Using Artificial Intelligence: Improved Emergency Care

Diabetics Keto Acidosis (DKA) is a serious health problem that requires timely intervention to prevent harmful consequences. This study analyzes various types of patient data, including clinical parameters and historical records such as family history, genetics, age, gender, etc., to identify early indications for developing DKA in children. The proposed AI-based system can support healthcare professionals in the emergency department by providing accurate and timely predictions/decisions. The dataset consisted of various clinical, and laboratory parameters such as "blood sugar level, hco3, pH values, urinary ketones, acidosis, sodium, and potassium values". Furthermore, along with the above parameters the dataset also contained some additional parameters such as "gender, age, and family history". The total size of the dataset was 5000 rows with 978 rows as "children diagnosed with DKA" and 4021 rows as "normal children". For the model selection stage, we selected 08 different machine learning models such as logistic regression, k nearest neighbor, naïve Bayes, decision tree, random forest, gradient boosting, xgboost, and extra trees classifier. Additionally, we also split the above dataset into two sets (i.e., train, and test) with an overall distribution of 70%, and 30% for the train (3500 data points), and test (1500 data points); respectively. Throughout, the study, we strictly followed all the ethical guidelines to ensure patient privacy and data protection rights with relevant regulations. The preliminary results showed that the proposed AI-based prediction model achieved a high accuracy for predicting DKA in children based on the above dataset. Although, the dataset contained more samples of normal children (i.e., 80.5%) than DKA-diagnosed children but still the prediction model performed very well in identifying the DKA in the children. The best-performing model from the models selected in the methods part was found to be the extra trees classifier with overall accuracy, precision, recall, f1-score, and MSE of 81%, 78%, 81%, 73%, and 18%. By integrating, the developed predictive model into the emergency department, the medical officers or healthcare professionals can predict different patients at risk very quickly by providing the input data of the patients on which the proposed AI-based prediction model is trained.

Enhancing Self-Care Prediction in Children with Impairments: A Novel Framework for Addressing Imbalance and High Dimensionality

Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children’s healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.

What is in an action? Preschool children predict that agents take previous paths and not previous goals.

Developmental science has experienced a vivid debate on whether young children prioritize goals over means in their prediction of others' actions. Influential developmental theories highlight the role of goal objects for action understanding. Yet, recent infant studies report evidence for the opposite. The empirical evidence is therefore inconclusive. The current study advanced this debate by assessing preschool children's verbal predictions of others' actions. In five experiments (N=302), we investigated whether preschool children and adults predict agents to move towards their previous goal (that is, show goal-related predictions) or predict agents to move along the same movement path that they pursued before. While Experiments 1a, 1b and 1c presented young children and adults with animated agents, Experiments 2a and 2b presented participants with human grasping action. An integrative analysis across experiments revealed that children were more likely to predict the agent to move along the same movement path, Z=-4.574, p ≤ 0.0001 (r=0.304). That is, preschool children were more likely to predict that agents would move along the same trajectory even though this action would lead to a new goal object. Thus, our findings suggest that young children's action prediction relies on the detection of spatial and movement information. Overall, we discuss our findings in terms of theoretical frameworks that conceive of action understanding as an umbrella term that comprises different forms and facets in which humans understand others' actions. RESEARCH HIGHLIGHTS: We investigated whether preschool children predict agents to move towards their previous goal or to move along the same movement path that they pursued before. Unlike adults, preschool children predicted that agents would move along the same trajectory even though this action would lead to a new goal. Adults' goal-based predictions were affected from contextual details, whereas children systematically made path-based predictions. Young children's action prediction relies on the detection of spatial and movement information.

Developing a prediction model of children asthma risk using population-based family history health records.

Identifying children at high risk of developing asthma can facilitate prevention and early management strategies. We developed a prediction model of children's asthma risk using objectively collected population-based children and parental histories of comorbidities. We conducted a retrospective population-based cohort study using administrative data from Manitoba, Canada, and included children born from 1974 to 2000 with linkages to ≥1 parent. We identified asthma and prior comorbid condition diagnoses from hospital and outpatient records. We used two machine-learning models: least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and random forest (RF) to identify important predictors. The predictors in the base model included children's demographics, allergic conditions, respiratory infections, and parental asthma. Subsequent models included additional multiple comorbidities for children and parents. The cohort included 195,666 children: 51.3% were males and 17.7% had asthma diagnosis. The base LR model achieved a low predictive performance with sensitivity of 0.47, 95% confidence interval (0.45-0.48), and specificity of 0.67 (0.66-0.67) using a predicted probability threshold of 0.20. Sensitivity significantly improved when children's comorbidities were included using LASSO LR: 0.71 (0.69-0.72). Predictive performance further improved by including parental comorbidities (sensitivity = 0.72 [0.70-0.73], specificity = 0.69 [0.69-0.70]). We observed similar results for the RF models. Children's menstrual disorders and mood and anxiety disorders, parental lipid metabolism disorders and asthma were among the most important variables that predicted asthma risk. Including children and parental comorbidities to children's asthma prediction models improves their accuracy.

Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.

Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes in size, shape, and imaging features of OPGs are associated with the likelihood of vision loss. This paper presents a fully automatic framework for accurate prediction of visual acuity loss using multi-sequence magnetic resonance images (MRIs). Our proposed framework includes a transformer-based segmentation network using transfer learning, statistical analysis of radiomic features, and a machine learning method for predicting vision loss. Our segmentation network was evaluated on multi-sequence MRIs acquired from 75 pediatric subjects with NF1-OPG and obtained an average Dice similarity coefficient of 0.791. The ability to predict vision loss was evaluated on a subset of 25 subjects with ground truth using cross-validation and achieved an average accuracy of 0.8. Analyzing multiple MRI features appear to be good indicators of vision loss, potentially permitting early treatment decisions.Clinical relevance- Accurately determining which children with NF1-OPGs are at risk and hence require preventive treatment before vision loss remains challenging, towards this we present a fully automatic deep learning-based framework for vision outcome prediction, potentially permitting early treatment decisions.

ChildPredictor: A Child Face Prediction Framework With Disentangled Learning

The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by "style", i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at https://github.com/zhaoyuzhi/ChildPredictor.

Automated prediction of children's age from voice acoustics

The emergence of a variety of applications aimed at video gaming, parental control, education, specific language impairment, child development assessment, and speech therapy create demands for age-targeted approaches. Yet, there is a lack of methods providing robust and easily interpretable age estimation of speakers from early childhood to post-pubertal stage. This study aims to provide a fully-automated approach for children's age prediction based on voice acoustics. Sustained phonation of vowels /a/, /e/, /i/, /o/, and /u/ recorded from 255 speakers (132 girls and 123 boys) ranging between 4 and 15 years of age were analysed. The first three formant frequencies and fundamental frequency across each vowel were automatically evaluated and used as features for linear and nonlinear regressors to estimate the prediction model. We demonstrate rapid, accurate age estimation with reasonable accuracy of an average 1.3-year difference from actual children's chronological age. The lower age prediction error of 1.2 years was achieved for boys compared to 1.5 years for girls. The early childhood age from 4 to 5 years was less accurate for prediction. No effect of utterance duration on estimated results was observed. Our results present a robust technology with clinically interpretable outcomes insusceptible to overfitting that enables to predict children's age in a wide range of ages. Better prediction accuracy for boys than girls appears to reflect the faster vocal tract growth for men. The lower prediction accuracy in early childhood can be attributed to rapid nonlinear development and greater variability in the level of motor control maturation.

FAMILY DECISION-MAKING IN SERIOUS ILLNESS SCENARIOS: ACCURACY AND PERCEIVED ACCURACY OF ADULT CHILD SURROGATES

Abstract Adult children often help older adults make important decisions in serious illness situations, but previous research suggests adult children are not uniformly accurate in predicting parents' preferences. In this project we examined the accuracy with which adult children (N= 68; M age= 45; 69.1% female) were able to predict medical preferences of their parents (N= 34; M age= 75; 94.1% female). Parents expressed their preferences in four serious illness scenarios, and children predicted parents' preferences. Parents and children also estimated the accuracy of children's predictions and the frequency of communication about medical care. On average across all scenarios, children predicted parents' preferences accurately 42.3% of the time. Parents were willing to take more risk with treatment for more severe disease, as their children predicted, although children generally overestimated parents' willingness to live with increasing disability (M difference = 9.77%). Overall, children were poor at predicting their own accuracy (Rs = -.017, p= .893). Parents were slightly more insightful about their children's accuracy (Rs= .261, p=.034), but there was no association between accuracy and parent or child estimate of frequency of conversations about medical preferences (Parent Rs= .067, p=.586 ; Child Rs= .164, p= .182). These findings suggest that neither are adult children highly accurate at predicting parents' medical preferences, nor do they or their parents have a precise sense of their accuracy. Results from this study may inform interventions to help families communicate about advance care planning.

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.

Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children's TMC and detect its determinants since it can present the relative influence of variables on children's TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of "hyperparameters". To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and "Hyperopt". Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children's TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children's TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, "walkability" and "bikeability" of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

A Novel Method for Adult Height Prediction in Children With Idiopathic Short Stature Derived From a German-Dutch Cohort.

ContextPrediction of adult height (AH) is important in clinical management of short children. The conventional methods of Bayley-Pinneau (BP) or Roche-Wainer-Thissen (RWT) have limitations.ObjectiveWe aimed to develop a set of algorithms for AH prediction in patients with idiopathic short stature (ISS) which are specific for combinations of predicting variables.MethodsDemographic and auxologic data were collected in childhood (1980s) and at AH (1990s). Data were collected by Dutch and German referral centers for pediatric endocrinology. A total of 292 subjects with ISS (219 male, 73 female) were enrolled. The population was randomly split into modeling (n = 235) and validation (n = 57) cohorts. Linear multi-regression analysis was performed with predicted AH (PAH) as response variable and combinations of chronological age (CA), baseline height, parental heights, relative bone age (BA/CA), birth weight, and sex as exploratory variables.ResultsTen models including different exploratory variables were selected with adjusted R² ranging from 0.84 to 0.78 and prediction errors from 3.16 to 3.68 cm. Applied to the validation cohort, mean residuals (PAH minus observed AH) ranged from −0.29 to −0.82 cm, while the conventional methods showed some overprediction (BP: +0.53 cm; RWT: +1.33 cm; projected AH: +3.81 cm). There was no significant trend of residuals with PAH or any exploratory variables, in contrast to BP and projected AH.ConclusionThis set of 10 multi-regression algorithms, developed specifically for children with ISS, provides a flexible tool for AH prediction with better accuracy than the conventional methods.

Lexicalization in the developing parser

We use children's noun learning as a probe into the nature of their syntactic prediction mechanism and the statistical knowledge on which that prediction mechanism is based. We focus on verb-based predictions, considering two possibilities: children's syntactic predictions might rely on distributional knowledge about specific verbs–i.e. they might be lexicalized–or they might rely on distributional knowledge that is general to all verbs. In an intermodal preferential looking experiment, we establish that verb-based predictions are lexicalized: children encode the syntactic distributions of specific verbs and use those distributions to make predictions, but they do not assume that these can be assumed of verbs in general.

Longitudinal Observational Study of Cardiac Outcome Risk Factor Prediction in Children, Adolescents, and Adults with Barth Syndrome.

Barth Syndrome (BTHS) is an X-linked mitochondrial cardioskeletal myopathy caused by defects in TAFAZZIN, a gene responsible for cardiolipin remodeling. Altered mitochondrial levels of cardiolipin lead to cardiomyopathy (CM), muscle weakness, exercise intolerance, and mortality. Cardiac risk factors predicting outcome are unknown. Therefore, we conducted a longitudinal observational study to determine risk factors for outcome in BTHS. Subjects with minimum two evaluations (or one followed by death or transplant) were included. Cardiac size, function, and QTc data were measured by echocardiography and electrocardiography at 7 time points from 2002 to 2018. Analysis included baseline, continuous, and categorical variables. Categorical risk factors included prolonged QTc, abnormal right ventricle fractional area change (RV FAC), left ventricle (LV) or RV non-compaction, and restrictive CM phenotype. The association between variables and cardiac death or transplant (CD/TX) was assessed. Median enrollment age was 7years (range 0.5-22; n = 44). Transplant-free survival (TFS) was 74.4% at 15years from first evaluation. The cohort demonstrated longitudinal declines in LV size and stroke volume z-scores (end-diastolic volume, p = 0.0002; stroke volume p < 0.0001), worsening RV FAC (p = 0.0405), and global longitudinal strain (GLS) (p = 0.0001) with stable ejection (EF) and shortening (FS) fraction. CD/TX subjects (n = 9) displayed worsening LV dilation (p = 0.0066), EF (p ≤ 0.0001), FS (p = 0.0028), and RV FAC (p = .0032) versus stability in TFS. Having ≥ 2 categorical risk factors predicted CD/TX (p = 0.0073). Over 15years, 25% of BTHS subjects progressed to CD/TX. Those with progressive LV enlargement, dysfunction, and multiple cardiac risk factors warrant increased surveillance and intense therapy.

Evidence for goal- and mixed evidence for false belief-based action prediction in 2- to 4-year-old children: A large-scale longitudinal anticipatory looking replication study.

Unsuccessful replication attempts of paradigms assessing children's implicit tracking of false beliefs have instigated the debate on whether or not children have an implicit understanding of false beliefs before the age of four. A novel multi-trial anticipatory looking false belief paradigm yielded evidence of implicit false belief reasoning in 3- to 4-year-old children using a combined score of two false belief conditions (Grosse Wiesmann, C., Friederici, A. D., Singer, T., & Steinbeis, N. [2017]. Developmental Science, 20(5), e12445). The present study is a large-scale replication attempt of this paradigm. The task was administered three times to the same sample of N=185 children at 2, 3, and 4years of age. Using the original stimuli, we did not replicate the original finding of above-chance belief-congruent looking in a combined score of two false belief conditions in either of the three age groups. Interestingly, the overall pattern of results was comparable to the original study. Post-hoc analyses revealed, however, that children performed above chance in one false belief condition (FB1) and below chance in the other false belief condition (FB2), thus yielding mixed evidence of children's false belief-based action predictions. Similar to the original study, participants' performance did not change with age and was not related to children's general language skills. This study demonstrates the importance of large-scaled replications and adds to the growing number of research questioning the validity and reliability of anticipatory looking false belief paradigms as a robust measure of children's implicit tracking of beliefs.

Combined Echocardiography and Lung Ultrasound for Extubation Outcome Prediction in Children after Cardiac Surgery

Background: Children are at risk of extubation failure after congenital heart disease surgery. Such cases should be identified to avoid possible adverse consequences of failed extubation. This study aimed to identify ultrasound predictors of successful extubation in children who underwent cardiac surgery. Methods: Children aged 3 months to 6 years who underwent cardiac surgery (if they were intubated for >6 h and underwent a spontaneous breathing trial) were included in this study. Results: We included 83 children who underwent surgery for congenital heart disease. Transthoracic echocardiography and lung ultrasound were performed immediately before spontaneous breathing trials. Upon spontaneous breathing trial completion, respiratory parameters, including arterial blood gas analysis and frequency-to-tidal volume ratio, were similarly recorded. For outcome assessment, all children were followed up for 48 h after extubation. We successfully extubated 57 children (68.7%). These children were significantly older and weighed more but had shorter aortic cross-clamp and cardiopulmonary bypass times. Children who could not be weaned or extubated had prolonged total mechanical ventilation and pediatric intensive care unit stay. In the multivariate regression analysis, a lung ultrasound score ≥12 and ejection fraction ≥40% immediately before spontaneous breathing trials were the only independent predictors of successful extubation. When combined, the lung ultrasound score and an ejection fraction ≥40% showed a better diagnostic performance than every other isolated variable (lung ultrasound, N-terminal-pro-B-type natriuretic peptide, and frequency-to-tidal volume ratio). Conclusions: The combination of lung ultrasound and transthoracic echocardiography immediately before the spontaneous breathing trial effectively predicts extubation outcomes in children after cardiac surgery.

Young children's ability to make predictions about novel illnesses.

Understanding disease transmission is a complex problem highlighted by the COVID-19 pandemic. These studies test whether 3- to 6-year-old children in the United States use information about social interactions to predict disease transmission. Before and during COVID-19, children predicted illness would spread through close interactions. Older children outperformed younger children with no associations between task performance and pandemic experience. Children did not predict that being hungry or tired would similarly spread through close interactions. Participants include 196 three- to six-year-olds (53% girls, 47% boys; 68% White, 9% Black, 7% Asian, 6% Hispanic or Latinx), with medium-sized effects (d=.6, =.3). These findings suggest that thinking about social interaction supports young children's predictions about illness, with noted limitations regarding children's real-world avoidance of disease-spreading behaviors.

Giving priority to race or wealth in peer group contexts involving social inclusion.

This study investigated children's and adolescents' predictions regarding intergroup inclusion in contexts where peers differed on two dimensions of group membership: race and wealth. African American and European American participants (N = 153; age range: 8-14 years, Mage = 11.46 years) made predictions about whether afterschool clubs would prefer to include a peer based on race or wealth and reported what they personally thought should happen. Between late childhood and early adolescence, European American participants increasingly expected that afterschool clubs would include a same-wealth peer (even when this peer was of a different race) whereas African American participants increasingly expected that the afterschool clubs would include a same-race peer (even when this peer was of a different level of wealth). Both European American and African American participants themselves thought that the clubs should include a same-wealth peer over a same-race peer, and with age, were increasingly likely to reference perceived comfort when explaining their decision. Future studies on the development of racial preferences will benefit from including wealth status information given that, with age, perceived comfort was associated with same-wealth rather than same-race status. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Maternal Depression, Parental Practices and Child Sex: Prediction of Children’s Behavior

Abstract Exposure to maternal depressive symptoms and negative parenting practices during childhood configures a vulnerability factor associated with behavior problems among children. This study aims to verify the predictive effects of maternal depression (main factor) on behavioral outcomes and maternal parenting practices, considering the sex of the children (covariate factor); and the predictive effect of maternal depression, parenting practices, and child’s sex on behavioral outcomes. The sample was composed of a community sample of 101 mother-child pairs. Mothers answered to: Structured Clinical Interview for DSM Disorders, Parenting Styles Inventory, and Strengths and Difficulties Questionnaire. The covariance and multiple linear regressions analysis showed that being a boy, and specific practices of neglect, physical abuse, and permissive discipline were the main predictors of behavioral problems among children exposed to negative practices and maternal depression. These findings underline the role of the child’s sex in the interactions between variables, which could contribute to interventions in mental health.