Hasil untuk "Pediatrics"

Rae-Ellen W. Kavey, D. Simons-Morton, Janet M. de Jesus

; originally published online November 14, 2011; Pediatrics HEALTH AND RISK REDUCTION IN CHILDREN AND ADOLESCENTS EXPERT PANEL ON INTEGRATED GUIDELINES FOR CARDIOVASCULAR Reduction in Children and Adolescents: Summary Report Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk http://pediatrics.aappublications.org/content/early/2011/11/09/peds.2009-2107C.citation located on the World Wide Web at: The online version of this article, along with updated information and services, is of Pediatrics. All rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275. Boulevard, Elk Grove Village, Illinois, 60007. Copyright © 2011 by the American Academy published, and trademarked by the American Academy of Pediatrics, 141 Northwest Point publication, it has been published continuously since 1948. PEDIATRICS is owned, PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly

K. Roberts

OBJECTIVE: To revise the American Academy of Pediatrics practice parameter regarding the diagnosis and management of initial urinary tract infections (UTIs) in febrile infants and young children. METHODS: Analysis of the medical literature published since the last version of the guideline was supplemented by analysis of data provided by authors of recent publications. The strength of evidence supporting each recommendation and the strength of the recommendation were assessed and graded. RESULTS: Diagnosis is made on the basis of the presence of both pyuria and at least 50 000 colonies per mL of a single uropathogenic organism in an appropriately collected specimen of urine. After 7 to 14 days of antimicrobial treatment, close clinical follow-up monitoring should be maintained to permit prompt diagnosis and treatment of recurrent infections. Ultrasonography of the kidneys and bladder should be performed to detect anatomic abnormalities. Data from the most recent 6 studies do not support the use of antimicrobial prophylaxis to prevent febrile recurrent UTI in infants without vesicoureteral reflux (VUR) or with grade I to IV VUR. Therefore, a voiding cystourethrography (VCUG) is not recommended routinely after the first UTI; VCUG is indicated if renal and bladder ultrasonography reveals hydronephrosis, scarring, or other findings that would suggest either high-grade VUR or obstructive uropathy and in other atypical or complex clinical circumstances. VCUG should also be performed if there is a recurrence of a febrile UTI. The recommendations in this guideline do not indicate an exclusive course of treatment or serve as a standard of care; variations may be appropriate. Recommendations about antimicrobial prophylaxis and implications for performance of VCUG are based on currently available evidence. As with all American Academy of Pediatrics clinical guidelines, the recommendations will be reviewed routinely and incorporate new evidence, such as data from the Randomized Intervention for Children With Vesicoureteral Reflux (RIVUR) study. CONCLUSIONS: Changes in this revision include criteria for the diagnosis of UTI and recommendations for imaging.

P.-I. Lee, Ya-Li Hu, Po‐Yen Chen et al.

a Department of Pediatrics, National Taiwan University Children’s Hospital and National Taiwan University College of Medicine, Taipei, Taiwan b Department of Pediatrics, New Taipei City Hospital, New Taipei City, Taiwan c Department of Pediatrics, Section of Infection, Taichung Veterans General Hospital, Taichung, Taiwan d Department of Pediatrics, Chang Gung Memorial Hospital and Chang Gung University College of Medicine at Linkou, Taoyuan, Taiwan e Department of Laboratory Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan f Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan

S. Daniels, F. Greer

I. Madeira

2004;114;555-576 Pediatrics Pressure in Children and Adolescents National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood http://www.pediatrics.org/cgi/content/full/114/2/S2/555 located on the World Wide Web at: The online version of this article, along with updated information and services, is

Mengyan Li, Xiaoou Li, Kenneth D Mandl et al.

Modern data-driven applications increasingly rely on large, heterogeneous datasets collected across multiple sites. Differences in data availability, feature representation, and underlying populations often induce structured missingness, complicating efforts to transfer information from data-rich settings to those with limited data. Many transfer learning methods overlook this structure, limiting their ability to capture meaningful relationships across sites. We propose TransNEST (Transfer learning with Network Embeddings under STructured missingness), a framework that integrates graphical data from source and target sites with prior group structure to construct and refine network embeddings. TransNEST accommodates site-specific features, captures within-group heterogeneity and between-site differences adaptively, and improves embedding estimation under partial feature overlap. We establish the convergence rate for the TransNEST estimator and demonstrate strong finite-sample performance in simulations. We apply TransNEST to a multi-site electronic health record study, transferring feature embeddings from a general hospital system to a pediatric hospital system. Using a hierarchical ontology structure, TransNEST improves pediatric embeddings and supports more accurate pediatric knowledge extraction, achieving the best accuracy for identifying pediatric-specific relational feature pairs compared with benchmark methods.

Connor Ludwig, Khashayar Namdar, Farzad Khalvati

Medical image segmentation has greatly aided medical diagnosis, with U-Net based architectures and nnU-Net providing state-of-the-art performance. There have been numerous general promptable models and medical variations introduced in recent years, but there is currently a lack of evaluation and comparison of these models across a variety of prompt qualities on a common medical dataset. This research uses Segment Anything Model (SAM), Segment Anything Model 2 (SAM 2), MedSAM, SAM-Med-3D, and nnU-Net to obtain zero-shot inference on the BraTS 2023 adult glioma and pediatrics dataset across multiple prompt qualities for both points and bounding boxes. Several of these models exhibit promising Dice scores, particularly SAM and SAM 2 achieving scores of up to 0.894 and 0.893, respectively when given extremely accurate bounding box prompts which exceeds nnU-Net's segmentation performance. However, nnU-Net remains the dominant medical image segmentation network due to the impracticality of providing highly accurate prompts to the models. The model and prompt evaluation, as well as the comparison, are extended through fine-tuning SAM, SAM 2, MedSAM, and SAM-Med-3D on the pediatrics dataset. The improvements in point prompt performance after fine-tuning are substantial and show promise for future investigation, but are unable to achieve better segmentation than bounding boxes or nnU-Net.

Henning Höfener, Farina Kock, Martina Pontones et al.

Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. While artificial intelligence (AI) solutions have been proposed, most utilize private datasets and only cover parts of the diagnostic pipeline. Therefore, we present a large, high-quality, publicly available leukemia bone marrow dataset spanning the entire diagnostic process, from cell detection to diagnosis. Using this dataset, we further propose methods for cell detection, cell classification, and diagnosis prediction. The dataset comprises 246 pediatric patients with diagnostic, clinical and laboratory information, over 40 000 cells with bounding box annotations and more than 28 000 of these with high-quality class labels, making it the most comprehensive dataset publicly available. Evaluation of the AI models yielded an average precision of 0.96 for the cell detection, an area under the curve of 0.98, and an F1-score of 0.61 for the 33-class cell classification, and a mean F1-score of 0.90 for the diagnosis prediction using predicted cell counts. While the proposed approaches demonstrate their usefulness for AI-assisted diagnostics, the dataset will foster further research and development in the field, ultimately contributing to more precise diagnoses and improved patient outcomes.

Siyu Zhu, Mouxiao Bian, Yue Xie et al.

With the rapid rise of large language models (LLMs) in medicine, a key question is whether they can function as competent pediatricians in real-world clinical settings. We developed PEDIASBench, a systematic evaluation framework centered on a knowledge-system framework and tailored to realistic clinical environments. PEDIASBench assesses LLMs across three dimensions: application of basic knowledge, dynamic diagnosis and treatment capability, and pediatric medical safety and medical ethics. We evaluated 12 representative models released over the past two years, including GPT-4o, Qwen3-235B-A22B, and DeepSeek-V3, covering 19 pediatric subspecialties and 211 prototypical diseases. State-of-the-art models performed well on foundational knowledge, with Qwen3-235B-A22B achieving over 90% accuracy on licensing-level questions, but performance declined ~15% as task complexity increased, revealing limitations in complex reasoning. Multiple-choice assessments highlighted weaknesses in integrative reasoning and knowledge recall. In dynamic diagnosis and treatment scenarios, DeepSeek-R1 scored highest in case reasoning (mean 0.58), yet most models struggled to adapt to real-time patient changes. On pediatric medical ethics and safety tasks, Qwen2.5-72B performed best (accuracy 92.05%), though humanistic sensitivity remained limited. These findings indicate that pediatric LLMs are constrained by limited dynamic decision-making and underdeveloped humanistic care. Future development should focus on multimodal integration and a clinical feedback-model iteration loop to enhance safety, interpretability, and human-AI collaboration. While current LLMs cannot independently perform pediatric care, they hold promise for decision support, medical education, and patient communication, laying the groundwork for a safe, trustworthy, and collaborative intelligent pediatric healthcare system.

Mahshad Lotfinia, Arash Tayebiarasteh, Samaneh Samiei et al.

Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and privacy-preserving approach to training but struggles in highly non-independent and identically distributed (non-IID) settings, where institutions with more representative data may experience degraded performance. Moreover, existing large-scale FL studies have been limited to adult datasets, neglecting the unique challenges posed by pediatric data, which introduces additional non-IID variability. To address these limitations, we analyzed n=398,523 adult chest radiographs from diverse institutions across multiple countries and n=9,125 pediatric images, leveraging transfer learning from general-purpose self-supervised image representations to classify pneumonia and cases with no abnormality. Using state-of-the-art vision transformers, we found that FL improved performance only for smaller adult datasets (P<0.001) but degraded performance for larger datasets (P<0.064) and pediatric cases (P=0.242). However, equipping FL with self-supervised weights significantly enhanced outcomes across pediatric cases (P=0.031) and most adult datasets (P<0.008), except the largest dataset (P=0.052). These findings underscore the potential of easily deployable general-purpose self-supervised image representations to address non-IID challenges in clinical FL applications and highlight their promise for enhancing patient outcomes and advancing pediatric healthcare, where data scarcity and variability remain persistent obstacles.

Hassan Alhuzali, Walid Al-Eisawi, Muhammad Abdul-Mageed et al.

We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 (co-located with EMNLP 2025). This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q&A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA.

Lorenzo Di Sarno, Serena Ferretti, Lavinia Capossela et al.

<b>Background:</b> Traumatic brain injury (TBI) and hypoxic–ischemic encephalopathy (HIE) are major causes of long-term neurological disability in children, with limited options for effective neuronal recovery. Recent research has highlighted the therapeutic potential of nerve growth factor (NGF) in promoting neural repair through mechanisms such as neuroprotection, neurogenesis, and the modulation of neuroinflammation. This review evaluates the current evidence on NGF as a treatment strategy for pediatric brain injury, emphasizing its mechanisms of action and translational clinical applications. <b>Methods:</b> A comprehensive review was conducted using the PubMed, Scopus, and Cochrane CENTRAL databases to identify studies published between 1 January 1978 and 1 March 2025, investigating NGF in the context of brain injury. The inclusion criteria comprised studies assessing neurological outcomes through clinical scales, biochemical markers, neuroimaging, or electrophysiological examinations. <b>Results:</b> Seventeen studies met the inclusion criteria, encompassing both preclinical and clinical research. Preclinical models consistently demonstrated that NGF administration reduces neuroinflammation, enhances neurogenesis, and supports neuronal survival following TBI and HIE. Clinical studies, including case reports of pediatric patients treated with intranasal NGF, reported improvements in motor and cognitive function, neuroimaging findings, and electrophysiological parameters, with no significant adverse effects observed. <b>Conclusions:</b> NGF demonstrates significant promise as a neuroprotective and neuroregenerative agent in pediatric brain injury, with both experimental and early clinical evidence supporting its safety and efficacy. Large-scale controlled clinical trials are warranted to validate these preliminary findings and to determine the optimal dosage regimens and administration schedules for NGF in the treatment of TBI and HIE.

Виталий Борисович Цхай, Павел Анатольевич Шестерня

Systemic juvenile idiopathic arthritis and adult Still's disease are currently considered as a single autoinflammatory disease with inherited polygenic pattern – Still's disease (SD). The emerge of joint clinical guidelines of the European Anti-Rheumatism League (European Alliance of Associations for Rheumatology, EULAR) and Pediatric Rheumatology European Society (PreS) outlines a fundamental change in the existing doctrine, designed to eliminate the existing gap between the pediatric and adult services. These guidelines introduce the first consolidated opinion on the diagnosis and management of children and adults with SD. However, issues of reproduction with SD remain extremely poorly covered. Trying to clarify the link between BS and pregnancy, we researched publications indexed in PubMed and eLIBRARY databases for the period of 2010-2024 using the keywords “Still’s disease”, “juvenile idiopathic arthritis”, “pregnancy”, “obstetric complications” and “perinatal outcomes”.

R. Black

Serum hexosaminidase activity (HEX) is elevated with ischemic gut injury. To determine if subsequent decreases in HEX correlate with gut healing, 97 weanling rats were subjected to laparotomy at which alternate vascular bundles were ligated along the base of the entire anterior mesenteric artery arcade. Fifteen rats served as pre-operative controls. After recovery, rats were allowed ad lib food and water. Groups were then killed at intervals, blood was drawn for HEX determination, and samples of small bowel were taken for histological evaluation. DATA:Microscopically, focal ischemic necrosis began at 6 hours. Between 12 and 48 hrs, cellular changes were consistent with progressive ischemic injury. Evidence of healing was apparent beginning at 5 days and these histological changes correlated with changes in HEX. Thus HEX proves useful as a marker for healing gut in this model.

Sumit Pandey, Satyasaran Changdar, Mathias Perslev et al.

Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors. This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregions across three challenging datasets: Pediatrics Tumor Challenge (PED), Brain Metastasis Challenge (MET), and Sub-Sahara-Africa Adult Glioma (SSA). These datasets represent diverse scenarios and anatomical variations, making them suitable for assessing the robustness and generalization capabilities of the MPUnet model. By utilizing multi-planar information, the MPUnet architecture aims to enhance segmentation accuracy. Our results show varying performance levels across the evaluated challenges, with the tumor core (TC) class demonstrating relatively higher segmentation accuracy. However, variability is observed in the segmentation of other classes, such as the edema and enhancing tumor (ET) regions. These findings emphasize the complexity of brain tumor segmentation and highlight the potential for further refinement of the MPUnet approach and inclusion of MRI more data and preprocessing.

Carlo Lipizzi

Large Language Models (LLM) have taken the front seat in most of the news since November 2022, when ChatGPT was introduced. After more than one year, one of the major reasons companies are resistant to adopting them is the limited confidence they have in the trustworthiness of those systems. In a study by (Baymard, 2023), ChatGPT-4 showed an 80.1% false-positive error rate in identifying usability issues on websites. A Jan. '24 study by JAMA Pediatrics found that ChatGPT has an accuracy rate of 17% percent when diagnosing pediatric medical cases (Barile et al., 2024). But then, what is "trust"? Trust is a relative, subject condition that can change based on culture, domain, individuals. And then, given a domain, how can the trustworthiness of a system be measured? In this paper, I present a systematic approach to measure trustworthiness based on a predefined ground truth, represented as a knowledge graph of the domain. The approach is a process with humans in the loop to validate the representation of the domain and to fine-tune the system. Measuring the trustworthiness would be essential for all the entities operating in critical environments, such as healthcare, defense, finance, but it would be very relevant for all the users of LLMs.

Victoria Hedley, Rebecca Leary, Anando Sen et al.

Over the past 50 years, the advancements in medical and health research have radically changed the epidemiology of health conditions in neonates, children, and adolescents; and clinical research has on the whole, moved forward. However, large sections of the pediatric community remain vulnerable and underserved, by clinical research. One reason for this is the fact that most pediatric diseases are also rare diseases (i.e., they fit the EU definition of a rare condition, by affecting no more than 5 in 10,000 individuals), and indeed the majority of conditions under this umbrella heading are in fact much rarer, affecting fewer than 1 in 100,000. Rare pediatric diseases incur particular challenges, both in terms of actually conducting clinical trials but also planning trials (and indeed, stimulating the preclinical research and knowledge generation necessary to embark on clinical trials in the first place). The pediatric regulation and orphan regulation (covering rare diseases) were introduced to address the complexities in research and development of medicines specifically for children and for people living with a rare disease, respectively. The regulations have been reasonably effective, particularly in areas where adult and pediatric diseases overlap, driving the development of more pediatric medicines; however, challenges still remain, often exacerbated by the rarity of the diseases. These include issues around trial planning, the need for more innovative methodologies in smaller populations, significant delays in trial start up and recruitment, recruitment issues (due to small populations and the nature of the conditions), lack of endpoints, and scarce data. This chapter will discuss some of the major challenges in delivering trials in pediatric rare diseases while also assessing current and future solutions to address these.

Fadillah Maani, Anees Ur Rehman Hashmi, Numan Saeed et al.

Brain tumor segmentation is a fundamental step in assessing a patient's cancer progression. However, manual segmentation demands significant expert time to identify tumors in 3D multimodal brain MRI scans accurately. This reliance on manual segmentation makes the process prone to intra- and inter-observer variability. This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge. The task is to segment tumors in brain MRI scans automatically from various populations, such as adults, pediatrics, and underserved sub-Saharan Africa. We employ a recent CNN architecture for medical image segmentation, namely MedNeXt, as our baseline, and we implement extensive model ensembling and postprocessing for inference. Our experiments show that our method performs well on the unseen validation set with an average DSC of 85.54% and HD95 of 27.88. The code is available on https://github.com/BioMedIA-MBZUAI/BraTS2024_BioMedIAMBZ.

Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez et al.

Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context of automated anatomy segmentation in whole-body Computed Tomography (CT). We evaluate the performance of CT organ segmentation algorithms trained on adult data when applied to pediatric CT volumes and identify substantial age-dependent underperformance. We subsequently propose and evaluate strategies, including data augmentation and continual learning approaches, to achieve good segmentation accuracy across all age groups. Our best-performing model, trained using continual learning, achieves high segmentation accuracy on both adult and pediatric data (Dice scores of 0.90 and 0.84 respectively).

Prabudh Goel, Prativa Choudhury, Vivek Verma et al.

Background: A discrepancy between the true and measured value of stretched penile length (SPL) may be a result of errors that can either be systematic or random. Hence, it becomes important to focus on the quality of measurements to prevent any iatrogenic harm to the patients. Objective: The objective of this study was to assess the magnitude of intra- and interobserver variations in the measurement of SPL with the SPLINT technique. Materials and Methods: SPL was measured prospectively in a cohort of 449 boys aged 0–14 years including 68 infants (substratified into Group I: >4 years, Group II: 4–8 years, and Group III: >8 years) with the SPLINT technique by expert (E: E1 and E2) and trainee (T: T1 and T2) surgeons after completing a three-tiered training module. Intra- and interobserver variability was assessed through descriptive statistics, intraclass correlation (ICC), relative technical error of measurement (rTEM), and reliability or R (%). Results: Intraobserver variability: the mean difference between the two readings (E1 and E2) is 0.08 cm (95% confidence interval [CI]: 0.073–0.087), ICC was 0.998 (95% CI: 0.997–0.998), and intraobserver variability ≤0.1 cm in 85% of the participants (n = 370 of 433). The rTEM and reliability (%) were 1.82% and 98.1% (Group I), 1.65% and 98.9% (Group II), and 1.09% and 99.7% (Group III), respectively. The intraobserver variability was observed to be inversely proportional to the age of the participants (correlation coefficient = −0.56). Interobserver variability was calculated separately for expert versus trainee and trainee versus trainee (T-vs-T) measurements. For expert versus expert, ICC, rTEM, and reliability (%) were 0.984, 2.4%, and 96.8% (Group 1), 0.992, 2.07%, and 98.3% (Group 2), and 0.997, 1.38%, and 99.05% (Group 3), respectively. A similar pattern of variability was observed for T-vs-T measurements. The reliability (%) of the SPL by experts is consistently more than that of trainees across all age groups; however, the difference ameliorates with the age of participant. Conclusions: The study has validated the SPLINT technique by demonstrating a high level of intra- and interobserver reliability. The adequacy of the training modules for SPL measurements described in this study has also been established. Evidence that the SPL can be used as an objective marker of penile dimensions is herewith furnished.