Ruijue Wang

Background: Pediatric care resources are limited and unevenly distributed in many health systems. Caregivers increasingly turn to large language models (LLMs) for children’s health information, but real-world evidence on LLM use and response quality in general pediatric care is limited. Objective: This study aimed to characterize real-world use, consultation needs, and response quality of Xiaoyi, an LLM-based pediatric health assistant, and to evaluate the impact of expert-guided optimization on response quality. Methods: This study collected all consultation sessions submitted between April 7 and December 31, 2025, to Xiaoyi, a pediatric health assistant co-developed by Children’s Hospital of Chongqing Medical University and Zuoyi Tech. Temporal patterns of consultation activity were characterized; all sessions were annotated for pediatric subspecialty, reason for consultation, and presenting symptoms through LLM-assisted labeling with pediatrician validation. A multidisciplinary panel of 40 pediatricians evaluated Xiaoyi’s response quality over 2 rounds using predefined criteria established by expert consensus: an initial evaluation informed system optimization (prompt revision, knowledge-base and tool expansion, and validation rules) and a follow-up evaluation assessed its impact. In each round, 1000 sessions randomly sampled from the corresponding pre- or postoptimization period (2000 in total) were rated at the session level (overall quality and clinical appropriateness) and the turn level (6 predefined deficiency categories). Categorical ratings were compared using the chi-square test, and turn-level deficiency rates were compared using generalized estimating equations to account for within-session correlation. Results: Caregivers of 8266 children (median age 4.2, IQR 1.3-8 years; 4705/8266, 56.9% male) from 31 of 34 provincial-level divisions in China initiated 16,074 sessions, of which 7046 (43.8%) occurred outside routine clinic hours (08:00-17:30). Queries spanned 31 subspecialties, most frequently child health care (2025/16,074, 12.6%), pulmonology (1903/16,074, 11.8%), and gastroenterology (1833/16,074, 11.4%). The leading reasons for consultation were symptom consultation (7845/16,074, 48.8%), medical report interpretation (2531/16,074, 15.7%), and triage guidance (2515/16,074, 15.6%); among symptom consultation sessions, fever (2719/16,074, 34.7%) and cough (2349/16,074, 29.9%) were most common. After expert-guided optimization, the number of sessions rated good (n=1000) in overall quality (the response fully resolving the caregiver’s question with actionable information) increased from 652 (65.2%) to 743 (74.3%), and the number rated clinically appropriate increased from 614 (61.4%) to 874 (87.4%, both P<.001). The turn-level deficiency rate declined from 19.4% (790/4073 turns) to 6.9% (236/3419 turns; odds ratio 0.30, 95% CI 0.24-0.37; P<.001), with suboptimal clinical recommendations (not optimally tailored to the child’s age or clinical context) remaining the most frequent category. Conclusions: This real-world study suggests that an LLM-based health consultation assistant may complement existing pediatric care, with expert-guided optimization substantially improving response quality. However, the persistence of deficiencies highlights the need for continued monitoring and refinement to ensure clinical reliability.

Epstein-Barr virus (EBV) infection is a common pediatric infectious disease. Infectious mononucleosis (IM) and hemophagocytic lymphohistiocytosis (HLH), two major complications of EBV infection, share similar clinical manifestations in the early stage. While IM is typically self-limiting, HLH is life-threatening and requires immediate intervention. Early differentiation between these two conditions is crucial for clinical decision-making; however, reliable prediction models based on readily available laboratory parameters remain scarce. This study aimed to develop and validate a machine learning prediction model using routine blood parameters obtained within 24 h of hospital admission in children with confirmed acute EBV infection to distinguish EBV-associated IM (EBV-IM) from EBV-associated HLH (EBV-HLH). This retrospective cohort study included 4,871 pediatric patients diagnosed with either EBV-IM or EBV-HLH from two campuses of Children's Hospital of Chongqing Medical University. Demographic information and initial complete blood count (CBC) parameters within 24 h of admission were collected. The cohort was divided into a model development group (Yuzhong Campus, n = 2,848; 70% for training, 30% for internal testing) and an external validation group (Liangjiang Campus, n = 2,023). Thirteen machine learning algorithms were evaluated using random search with 5-fold cross-validation for hyperparameter tuning. Shapley Additive exPlanations (SHAP) analysis was performed to interpret model predictions. EBV-HLH accounted for 12.46% (607/4,871) of the total cohort, with significantly different prevalence between the development and validation cohorts (18.29% vs. 4.25%, p < 0.001). Significant differences were observed between cohorts in age and all CBC parameters except gender (p < 0.05). The Random Forest (RF) model demonstrated optimal performance in the internal validation set (AUC = 0.993, 95% CI: 0.990-0.996). In the external validation cohort, the RF model maintained robust discriminative ability (AUC = 0.971, 95% CI: 0.949-0.992). Calibration curves indicated excellent agreement between predicted probabilities and actual risks. SHAP analysis identified WBC, PLT, LAC, and Hb as the most critical predictors of EBV-HLH. DCA demonstrated substantial clinical net benefit. A free online decision-support tool (https://wangrj1988.shinyapps.io/EBV-HLH-IM/) was developed based on the RF model to facilitate real-time risk assessment. The RF-based model using routine CBC parameters enables admission-based risk assessment of pediatric EBV-HLH with excellent generalizability, offering a cost-effective tool for diverse healthcare settings. Clinical trial number: not applicable.

BACKGROUND Pediatric care resources are limited and unevenly distributed in many health systems. Caregivers increasingly turn to large language models (LLMs) for children’s health information, but real-world evidence on LLM use and response quality in general pediatric care is limited. OBJECTIVE This study aimed to characterize real-world use, consultation needs, and response quality of Xiaoyi, an LLM-based pediatric health assistant, and to evaluate the impact of expert-guided optimization on response quality. METHODS This study collected all consultation sessions submitted between April 7 and December 31, 2025, to Xiaoyi, a pediatric health assistant co-developed by Children’s Hospital of Chongqing Medical University and Zuoyi Tech. Temporal patterns of consultation activity were characterized; all sessions were annotated for pediatric subspecialty, reason for consultation, and presenting symptoms through LLM-assisted labeling with pediatrician validation. A multidisciplinary panel of 40 pediatricians evaluated Xiaoyi’s response quality over 2 rounds using predefined criteria established by expert consensus: an initial evaluation informed system optimization (prompt revision, knowledge-base and tool expansion, and validation rules) and a follow-up evaluation assessed its impact. In each round, 1000 sessions randomly sampled from the corresponding pre- or postoptimization period (2000 in total) were rated at the session level (overall quality and clinical appropriateness) and the turn level (6 predefined deficiency categories). Categorical ratings were compared using the chi-square test, and turn-level deficiency rates were compared using generalized estimating equations to account for within-session correlation. RESULTS Caregivers of 8266 children (median age 4.2, IQR 1.3-8 years; 4705/8266, 56.9% male) from 31 of 34 provincial-level divisions in China initiated 16,074 sessions, of which 7046 (43.8%) occurred outside routine clinic hours (08:00-17:30). Queries spanned 31 subspecialties, most frequently child health care (2025/16,074, 12.6%), pulmonology (1903/16,074, 11.8%), and gastroenterology (1833/16,074, 11.4%). The leading reasons for consultation were symptom consultation (7845/16,074, 48.8%), medical report interpretation (2531/16,074, 15.7%), and triage guidance (2515/16,074, 15.6%); among symptom consultation sessions, fever (2719/16,074, 34.7%) and cough (2349/16,074, 29.9%) were most common. After expert-guided optimization, the number of sessions rated good (n=1000) in overall quality (the response fully resolving the caregiver’s question with actionable information) increased from 652 (65.2%) to 743 (74.3%), and the number rated clinically appropriate increased from 614 (61.4%) to 874 (87.4%, both P<.001). The turn-level deficiency rate declined from 19.4% (790/4073 turns) to 6.9% (236/3419 turns; odds ratio 0.30, 95% CI 0.24-0.37; P<.001), with suboptimal clinical recommendations (not optimally tailored to the child’s age or clinical context) remaining the most frequent category. CONCLUSIONS This real-world study suggests that an LLM-based health consultation assistant may complement existing pediatric care, with expert-guided optimization substantially improving response quality. However, the persistence of deficiencies highlights the need for continued monitoring and refinement to ensure clinical reliability. CLINICALTRIAL

Purpose: The aim of this study was to diminish radiation exposure in interventional radiology (IR) imaging while maintaining image quality. This was achieved by decreasing the acquisition frame rate and employing a deep neural network to interpolate the reduced frames. Methods: This retrospective study involved the analysis of 1634 IR sequences from 167 pediatric patients (March 2014 to January 2022). The dataset underwent a random split into training and validation subsets (at a 9:1 ratio) for model training and evaluation. Our approach proficiently synthesized absent frames in simulated low-frame-rate sequences by excluding intermediate frames from the validation subset. Accuracy assessments encompassed both objective experiments and subjective evaluations conducted by nine radiologists. Results: The deep learning model adeptly interpolated the eliminated frames within IR sequences, demonstrating encouraging peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results. The average PSNR values for angiographic, subtraction, and fluoroscopic modes were 44.94 dB, 34.84 dB, and 33.82 dB, respectively, while the corresponding SSIM values were 0.9840, 0.9194, and 0.7752. Subjective experiments conducted with experienced interventional radiologists revealed minimal discernible differences between interpolated and authentic sequences. Conclusion: Our method, which interpolates low-frame-rate IR sequences, has shown the capability to produce high-quality IR images. Additionally, the model exhibits potential for reducing the frame rate during IR image acquisition, consequently mitigating radiation exposure. Critical relevance statement: This study presents a critical advancement in clinical radiology by demonstrating the effectiveness of a deep neural network in reducing radiation exposure during pediatric interventional radiology while maintaining image quality, offering a potential solution to enhance patient safety. Key points: • Reducing radiation: cutting IR image to reduce radiation. • Accurate frame interpolation: our model effectively interpolates missing frames. • High visual quality in terms of PSNR and SSIM, making IR procedures safer without sacrificing quality. Graphical

The relationship between lead exposure and neurological disorders has been extensively studied, but the effects of lead exposure on hepatotoxicity are unknown. Metabolically related fatty liver disease (MAFLD) is an update of previous non-alcoholic fatty liver disease (NAFLD). It redefines the diagnostic conditions and emphasizes metabolic factors while considering non-alcoholic factors. Lead can affect the endocrine system and metabolism, so we believe that lead exposure may contribute to MAFLD. 41,723 individuals who had undergone blood lead testing from 2005 to 2018 in the National Health and Nutrition Examination Survey (NHANES) database were selected for this study. The characteristics of population lead exposure in the last decade or so, the effect of lead exposure on liver function and whether lead exposure can cause MAFLD were analyzed. Co-variates were adjusted according to age, ethnicity, body mass index (BMI), waist circumference, visceral adiposity index (VAI), poverty indices (PIR), diabetes, hypertension, and hyperlipidemia. The results showed that blood lead concentrations stabilized at a low level after a decreasing trend from year to year. The differences in blood lead concentrations were associated with differences in age, sex, race, education level, and PIR. Lead exposure was an independent risk factor for MAFLD, and lead and nine other factors were used as independent risk factors for MAFLD, so a nomogram was established to predict the prevalence probability of MAFLD.

Hereditary spherocytosis is the most common cause of hemolytic anemia due to defects in the blood cell membrane. Partial splenic embolization (PSE) might significantly improve the effects of hemolysis in HS patients while preserving partial splenic function. However, due to the poor controllability of the scope of interventional embolization, PSE in pediatric HS had not been widely studied and applied. The present study is the first to propose that optimized PSE procedure to treat pediatric HS patients, which named super-selective partial splenic embolization (SPSE). We investigated the feasibility and effectiveness of SPSE. This study was conducted by retrospectively reviewing clinical data from HS patients treated by surgical treatments who were diagnosed at children’s hospital of Chongqing medical university from January 2015 to December 2019. Patients were divided into two groups according to treatment preference: SPSE (16 patients) group and total splenectomy (41 patients) group. The data were analyzed such as preoperative and postoperative leukocytes, hemoglobin, platelets, bilirubin, immunological indicators, and postoperative complications. The success rate of two groups were 100%. The improvement of hemolysis in patients after SPSE was not different from that after total splenectomy, and the effect can be maintained for a long time. The average hemoglobin value increased significantly from 6.97g/dL before SPSE to 12.2g/dL after SPSE (P=0.000). SPSE was performed without any serious complications. SPSE is a safe and effective treatment for moderate or severe HS in children.