Several studies have investigated the potential effects of hyponatremia on recurrent febrile seizures (RFS) during febrile illness. Because findings were inconsistent across studies, we aimed to evaluate the serum sodium levels in febrile seizures (FS) of children with or without RFS during the same episode. We conducted electronic searches in three databases (PubMed, EMBASE, Cochrane Library) and one scholarly search engine (Google Scholar) up to June 2021 for studies on FS. Screening was done based on the titles and abstracts of primary studies. Then, eligibility was reviewed based on the abstracts. Finally, in order to match the inclusion and exclusion criteria, full-text articles were evaluated by two authors and inconsistencies were discussed. Data extraction was carried out by two independent authors. The extracted variables were author's name, article title, journal name, year of publication, study location, study design, sample size, and mean and standard deviation of blood Na concentration in FS. We performed a risk of bias assessment of included studies using the Newcastle-Ottawa Scale (NOS). The effect size was calculated using the standardized mean difference (SMD), and random-effects models were used for the analysis. A total of 12 articles were included with a single outlier. This analysis suggested that serum sodium level was lower in patients with RFS during the same febrile episode than in those with single FS, with SMD of -0.70, (n=1784; 95% CI: -1.03, -0.36; Z=-4.10, p<0.01; I² 86.67%, p<0.01). In the sensitivity analysis, no significant change was observed in pooled SMD. The optimal cutoff value of serum sodium level was 134.72 mmol/L with an area under the receiver operating characteristic curve of 0.81 (95% CI: 0.61, 1.00), with sensitivity of 80.0% and specificity of 70.0%. This result indicated a significant association between hyponatremia and RFS during the same febrile episode. Decreased serum sodium levels may be involved in seizure recurrence and may play a role in FS pathogenesis.

Introduction: Differentiating between bacterial and viral gastroenteritis is crucial in pediatric enteritis practice. Our objective was to use machine learning (ML) to identify acute gastroenteritis (AG) caused by bacteria based on blood cell counts and interview findings. Methods: ML was performed using a decision tree classifier based on data from previously published papers. We included 164 children between one and 108 months diagnosed with gastroenteritis, with 112 having bacterial AG and 52 having viral AG as subjects and controls. Feature selection was performed using least absolute shrinkage and selection operator (LASSO), and the classifier's performance was evaluated by five-fold cross-validation. Additionally, we presented a tree diagram of the decision tree classifier as a flowchart for practical applications. Results: The area under curve (AUC) was 0.80, indicating a moderate model. Three important features in this model were platelet-lymphocyte ratio, eosinophil count, and leukocyte count. Conclusions: In conclusion, this study demonstrates that bacterial AG can be estimated from blood cell counts with moderate accuracy. These findings may be valuable in narrowing down bacterial AG in children with gastrointestinal symptoms.

Background Group A Streptococcus (GAS) is the most common bacterial cause of pharyngitis in children. GAS pharyngitis requires antimicrobial agents, and rapid antigen detection tests (RADTs) are currently considered useful for diagnosis. However, the decision to perform the test is based on the pediatrician's examination findings, but the indicators are not clear. Therefore, we used machine learning (ML) to create a model to identify GAS pharyngitis from clinical findings and to explore important features. Methods ML with Python programming language was used for this study. Data from the included study involved 676 children aged 3 to 15 years diagnosed with pharyngitis, with positive results on the RADT serving as exposures, and negative results serving as controls. The ML performances served as the outcome. We utilized six types of ML classifiers, namely, logistic regression, support vector machine, k-nearest neighbor algorithm, random forest, an ensemble of them, Voting Classifier, and the eXtreme Gradient Boosting (XGBoost) algorithm. Additionally, we used SHapley Additive exPlanations (SHAP) values to identify important features. Results Moderately performing models were generated for all six ML classifiers. XGBoost produced the best model, with an area under the receiver operating characteristics curve of 0.75 ± 0.01. The order of important features in the model was palatal petechiae, followed by scarlatiniform rash, tender cervical lymph nodes, and age. Conclusion Through this study, we have demonstrated that ML models can predict childhood GAS pharyngitis with moderate accuracy using only commonly recorded clinical variables in children diagnosed with pharyngitis. We have also identified four important clinical variables. These findings may serve as a reference for considering indicators under the current guidelines recommended for selective RADTs.

Background: Respiratory syncytial virus (RSV) is the leading cause of lower respiratory infections in children. However, current diagnosis currently relies heavily on the clinicians’ subjective judgment, potentially delaying appropriate intervention. Identifying the primary clinical features of RSV in hospitalized pediatric patients can aid triage. Therefore, we analyzed a publicly available dataset comprising 768 pediatric patients, 135 of whom had confirmed RSV infection. Methods: A binary classification model was developed using the CatBoostClassifier algorithm and evaluated through five-fold cross-validation. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values, and the threshold values for the top features were derived from the dependency plots. Results: The model achieved an average area under the receiver operating characteristic curve (AUC) of 0.770, indicating a moderate predictive performance. The three most important features are weight, respiratory rate, and oxygen saturation (SpO₂). A simplified scoring system based on thresholds for weight (≤ 10 kg), respiratory rate (≥ 50 breaths/min), and SpO₂ (≤ 97%) achieved an AUC of 0.726. Conclusions: These findings suggest that machine learning can identify clinically meaningful features for RSV prediction in hospitalized children with severe pneumonia. The proposed scoring system may complement conventional, existing scoring approaches. However, prospective validation is required to confirm its generalizability and clinical utility.