Hasil untuk "Pediatrics"

M. Wolraich, J. Hagan, Carla C. Allan et al.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurobehavioral disorders of childhood and can profoundly affect children’s academic achievement, well-being, and social interactions. The American Academy of Pediatrics first published clinical recommendations for evaluation and diagnosis of pediatric ADHD in 2000; recommendations for treatment followed in 2001. The guidelines were revised in 2011 and published with an accompanying process of care algorithm (PoCA) providing discrete and manageable steps by which clinicians could fulfill the clinical guideline’s recommendations. Since the release of the 2011 guideline, the Diagnostic and Statistical Manual of Mental Disorders has been revised to the fifth edition, and new ADHD-related research has been published. These publications do not support dramatic changes to the previous recommendations. Therefore, only incremental updates have been made in this guideline revision, including the addition of a key action statement related to diagnosis and treatment of comorbid conditions in children and adolescents with ADHD. The accompanying process of care algorithm has also been updated to assist in implementing the guideline recommendations. Throughout the process of revising the guideline and algorithm, numerous systemic barriers were identified that restrict and/or hamper pediatric clinicians’ ability to adopt their recommendations. Therefore, the subcommittee created a companion article (available in the Supplemental Information) on systemic barriers to the care of children and adolescents with ADHD, which identifies the major systemic-level barriers and presents recommendations to address those barriers; in this article, we support the recommendations of the clinical practice guideline and accompanying process of care algorithm.

B. Marino, P. Lipkin, Jane W. Newburger et al.

Background— The goal of this statement was to review the available literature on surveillance, screening, evaluation, and management strategies and put forward a scientific statement that would comprehensively review the literature and create recommendations to optimize neurodevelopmental outcome in the pediatric congenital heart disease (CHD) population. Methods and Results— A writing group appointed by the American Heart Association and American Academy of Pediatrics reviewed the available literature addressing developmental disorder and disability and developmental delay in the CHD population, with specific attention given to surveillance, screening, evaluation, and management strategies. MEDLINE and Google Scholar database searches from 1966 to 2011 were performed for English-language articles cross-referencing CHD with pertinent search terms. The reference lists of identified articles were also searched. The American College of Cardiology/American Heart Association classification of recommendations and levels of evidence for practice guidelines were used. A management algorithm was devised that stratified children with CHD on the basis of established risk factors. For those deemed to be at high risk for developmental disorder or disabilities or for developmental delay, formal, periodic developmental and medical evaluations are recommended. A CHD algorithm for surveillance, screening, evaluation, reevaluation, and management of developmental disorder or disability has been constructed to serve as a supplement to the 2006 American Academy of Pediatrics statement on developmental surveillance and screening. The proposed algorithm is designed to be carried out within the context of the medical home. This scientific statement is meant for medical providers within the medical home who care for patients with CHD. Conclusions— Children with CHD are at increased risk of developmental disorder or disabilities or developmental delay. Periodic developmental surveillance, screening, evaluation, and reevaluation throughout childhood may enhance identification of significant deficits, allowing for appropriate therapies and education to enhance later academic, behavioral, psychosocial, and adaptive functioning.

J. Bradley, C. Byington, Samir S Shah et al.

Abstract Evidenced-based guidelines for management of infants and children with community-acquired pneumonia (CAP) were prepared by an expert panel comprising clinicians and investigators representing community pediatrics, public health, and the pediatric specialties of critical care, emergency medicine, hospital medicine, infectious diseases, pulmonology, and surgery. These guidelines are intended for use by primary care and subspecialty providers responsible for the management of otherwise healthy infants and children with CAP in both outpatient and inpatient settings. Site-of-care management, diagnosis, antimicrobial and adjunctive surgical therapy, and prevention are discussed. Areas that warrant future investigations are also highlighted.

N. Glaser, M. Fritsch, L. Priyambada et al.

Department of Pediatrics, Section of Endocrinology, University of California, Davis School of Medicine, Sacramento, California, USA Department of Pediatric and Adolescent Medicine, Division of General Pediatrics, Medical University of Graz, Austria Medical University of Graz, Graz, Austria Division of Pediatric Endocrinology, Rainbow Children's Hospital, Hyderabad, India Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado, USA Department of Women's and Children's Health, G. Salesi Hospital, Ancona, Italy Department of Pediatrics, Division of Endocrinology and Metabolism, University of the Philippines, College of Medicine, Manila, Philippines Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts, USA Institute of Maternal and Child Research, School of Medicine, University of Chile, Santiago, Chile

Megan H. Bair-Merritt

1. Megan H. Bair-Merritt, MD, MSCE* 1. *Assistant Professor of Pediatrics, Associate Director, Primary Care Fellowship Program, Johns Hopkins School of Medicine, Baltimore, Md. After completing this article, readers should be able to: 1. Describe the prevalence of intimate partner violence and childhood exposure to intimate partner violence. 2. Identify risk factors associated with intimate partner violence. 3. Explain the relationship of child maltreatment in the setting of intimate partner violence. 4. Recognize the impact of intimate partner violence exposure on children's social-emotional and physical health and health-care use. 5. Discuss strategies for screening and responding to intimate partner violence in the pediatric setting. You are seeing a healthy 4-month-old infant for a health supervision visit. As part of a routine social history, you inquire about intimate partner violence (IPV). The infant's mother discloses that her partner frequently yells at her, pushes her, and makes her feel afraid. On additional questioning, she describes the infant as “fussy.” The baby's physical examination findings are unremarkable, but you note that he missed his 2-month visit and is behind on his immunizations. How do you proceed? Family violence and domestic violence often are used synonymously and refer to violence occurring between any family member dyad, including parent-child, intimate partner-intimate partner, or sibling-sibling. IPV refers specifically to violence perpetrated between romantic partners and has been defined by the Family Violence Prevention Fund as “a pattern of purposeful coercive behaviors that may include inflicted physical injury, psychological abuse, sexual assault, progressive social isolation, stalking, deprivation, intimidation and threats. These behaviors are perpetrated by someone who is, was, or wishes to be involved in an intimate or dating relationship with an adult or adolescent victim and are aimed at establishing control of one partner over the other.” (1) Over the course of a lifetime, between one fourth …

Karoline Barreto da Silva Rocha, Samanta Barbosa Feitosa, Rildo de Souza Wanderley Junior et al.

ABSTRACT Objective: To analyze the association between the Social Ecological Model and meeting the physical activity, sedentary behavior, and sleep recommendations in a combined and integrated manner among adolescents. Methods: This is a cross-sectional study conducted in public schools in the Metropolitan Region of Recife, state of Pernambuco, Brazil, with adolescents aged 14 to 17 years. An adapted version of the Global School-based Student Health Survey was used as the instrument. Robust Poisson regressions were performed. Results: Approximately 1.8% of the 576 adolescents met all three recommendations simultaneously. Enjoying physical activity (prevalence ratio [PR] 11.62; 95% confidence interval [CI] 1.50–89.75) was associated with the combined adherence to the physical activity and sedentary behavior recommendations. Having two or more friends (PR 0.38; 95%CI 0.18–0.76) and participating in one (PR 0.39; 95%CI 0.20–0.78) or two physical education classes per week (PR 0.43; 95%CI 0.20–0.92) were associated with lower probabilities of non-compliance with these recommendations. Self-rated sleep quality as good (PR 2.49; 95%CI 1.09–5.67) was associated with higher prevalence of meeting the combined recommendations for sedentary behavior and sleep. Being male (PR 0.48; 95%CI 0.23–0.97) and participating in one physical education class per week (PR 0.40; 95%CI 0.16– 0.99) were associated with lower prevalence of not meeting the recommendations for sedentary behavior and sleep. Self-reported sleep quality as good (PR 3.11; 95%CI 1.36–7.10) as well as being male (PR 2.10; 95%CI 1.14–3.87) were associated with a higher likelihood of meeting the combined recommendations for physical activity and sleep. Actively commuting to school (PR 0.48; 95%CI 0.27–0.83) was associated with a lower likelihood of not meeting these recommendations. Conclusions: Intrapersonal, interpersonal, and community factors are associated with adherence to physical activity, sedentary behavior, and sleep recommendations in adolescents.

Aditya Nagori, Ayush Gautam, Matthew O. Wiens et al.

Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimensional, heterogeneous healthcare data and lack contextual understanding. This study evaluates Large Language Model (LLM) based clustering against classical methods using a pediatric sepsis dataset from a low-income country (LIC), containing 2,686 records with 28 numerical and 119 categorical variables. Patient records were serialized into text with and without a clustering objective. Embeddings were generated using quantized LLAMA 3.1 8B, DeepSeek-R1-Distill-Llama-8B with low-rank adaptation(LoRA), and Stella-En-400M-V5 models. K-means clustering was applied to these embeddings. Classical comparisons included K-Medoids clustering on UMAP and FAMD-reduced mixed data. Silhouette scores and statistical tests evaluated cluster quality and distinctiveness. Stella-En-400M-V5 achieved the highest Silhouette Score (0.86). LLAMA 3.1 8B with the clustering objective performed better with higher number of clusters, identifying subgroups with distinct nutritional, clinical, and socioeconomic profiles. LLM-based methods outperformed classical techniques by capturing richer context and prioritizing key features. These results highlight potential of LLMs for contextual phenotyping and informed decision-making in resource-limited settings.

Wenjun Yang, Chia-Ho Hua, Tina Davis et al.

Purpose: We developed an artificial neural network (ANN) combining radiomics with clinical and dosimetric features to predict the extent of body mass index (BMI) increase after surgery and proton therapy, with advantage of improved accuracy and integrated key feature selection. Methods and Materials: Uniform treatment protocol composing of limited surgery and proton radiotherapy was given to 84 pediatric craniopharyngioma patients (aged 1-20 years). Post-treatment obesity was classified into 3 groups (<10%, 10-20%, and >20%) based on the normalized BMI increase during a 5-year follow-up. We developed a densely connected 4-layer ANN with radiomics calculated from pre-surgery MRI (T1w, T2w, and FLAIR), combining clinical and dosimetric features as input. Accuracy, area under operative curve (AUC), and confusion matrices were compared with random forest (RF) models in a 5-fold cross-validation. The Group lasso regularization optimized a sparse connection to input neurons to identify key features from high-dimensional input. Results: Classification accuracy of the ANN reached above 0.9 for T1w, T2w, and FLAIR MRI. Confusion matrices showed high true positive rates of above 0.9 while the false positive rates were below 0.2. Approximately 10 key features selected for T1w, T2w, and FLAIR MRI, respectively. The ANN improved classification accuracy by 10% or 5% when compared to RF models without or with radiomic features. Conclusion: The ANN model improved classification accuracy on post-treatment obesity compared to conventional statistics models. The clinical features selected by Group lasso regularization confirmed our practical observation, while the additional radiomic and dosimetric features could serve as imaging markers and mitigation methods on post-treatment obesity for pediatric craniopharyngioma patients.

Peter Chang, John Kairalla, Arkaprava Roy

Heterogeneous treatment effect estimation is critical in oncology, particularly in multi-arm trials with overlapping therapeutic components and long-term survivors. These shared mechanisms pose a central challenge to identifying causal effects in precision medicine. We propose a novel covariate-dependent nonparametric Bayesian multi-treatment cure survival model that jointly accounts for common structures among treatments and cure fractions. Through latent link functions, our model leverages sharing among treatments through a flexible modeling approach, enabling individualized survival inference. We adopt a Bayesian route for inference and implement an efficient MCMC algorithm for approximating the posterior. Simulation studies demonstrate the method's robustness and superiority in various specification scenarios. Finally, application to the AALL0434 trial reveals clinically meaningful differences in survival across methotrexate-based regimens and their associations with different covariates, underscoring its practical utility for learning treatment effects in real-world pediatric oncology data.

Yunzhi Xu, Yushuang Ding, Hu Sun et al.

Evaluation of hydrocephalus in children is challenging, and the related research is limited by a lack of publicly available, expert-annotated datasets, particularly those with segmentation of the choroid plexus. To address this, we present HyKid, an open-source dataset from 48 pediatric patients with hydrocephalus. 3D MRIs were provided with 1mm isotropic resolution, which was reconstructed from routine low-resolution images using a slice-to-volume algorithm. Manually corrected segmentations of brain tissues, including white matter, grey matter, lateral ventricle, external CSF, and the choroid plexus, were provided by an experienced neurologist. Additionally, structured data was extracted from clinical radiology reports using a Retrieval-Augmented Generation framework. The strong correlation between choroid plexus volume and total CSF volume provided a potential biomarker for hydrocephalus evaluation, achieving excellent performance in a predictive model (AUC = 0.87). The proposed HyKid dataset provided a high-quality benchmark for neuroimaging algorithms development, and it revealed the choroid plexus-related features in hydrocephalus assessments. Our datasets are publicly available at https://www.synapse.org/Synapse:syn68544889.

Shashwat Kumar, Arafat Rahman, Robert Gutierrez et al.

Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders.

Juming Xiong, Muyang Li, Ruining Deng et al.

Video endoscopy represents a major advance in the investigation of gastrointestinal diseases. Reviewing endoscopy videos often involves frequent adjustments and reorientations to piece together a complete view, which can be both time-consuming and prone to errors. Image stitching techniques address this issue by providing a continuous and complete visualization of the examined area. However, endoscopic images, particularly those of the esophagus, present unique challenges. The smooth surface, lack of distinct feature points, and non-horizontal orientation complicate the stitching process, rendering traditional feature-based methods often ineffective for these types of images. In this paper, we propose a novel preprocessing pipeline designed to enhance endoscopic image stitching through advanced computational techniques. Our approach converts endoscopic video data into continuous 2D images by following four key steps: (1) keyframe selection, (2) image rotation adjustment to correct distortions, (3) surface unwrapping using polar coordinate transformation to generate a flat image, and (4) feature point matching enhanced by Adaptive Histogram Equalization for improved feature detection. We evaluate stitching quality through the assessment of valid feature point match pairs. Experiments conducted on 20 pediatric endoscopy videos demonstrate that our method significantly improves image alignment and stitching quality compared to traditional techniques, laying a robust foundation for more effective panoramic image creation.

Helena Coggan, Anne Bischops, Pradip Chaudhari et al.

Persistent demographic disparities have been identified in the treatment of patients seeking care in the emergency department (ED). These may be driven in part by subconscious biases, which providers themselves may struggle to identify. To better understand the operation of these biases, we performed a retrospective cross-sectional analysis using electronic health records describing 339,400 visits to the ED of a single US pediatric medical center between 2019-2024. Odds ratios were calculated using propensity-score matching. Analyses were adjusted for confounding variables, including chief complaint, insurance type, socio-economic deprivation, and patient comorbidities. We also trained a machine learning [ML] model on this dataset to identify predictors of admission. We found significant demographic disparities in admission (Non-Hispanic Black [NHB] relative to Non-Hispanic White [NHW]: OR 0.77, 95\% CI 0.73-0.81; Hispanic relative to NHW: OR 0.80, 95\% CI 0.76-0.83). We also identified disparities in individual decisions taken during the ED stay. For example, NHB patients were significantly less likely than NHW patients to be assigned an `emergent' triage acuity score of (OR 0.70, 95\% CI 0.67-0.72), but emergent NHB patients were also significantly less likely to be admitted than NHW patients with the same triage acuity (OR 0.86, 95\% CI 0.80-0.93). Demographic disparities were particularly acute wherever patients had normal vital signs, public insurance, moderate socio-economic deprivation, or a home address distant from the hospital. An ML model assigned higher importance to triage score for NHB than NHW patients when predicting admission, reflecting these disparities in assignment. We conclude that many visit characteristics, clinical and otherwise, may influence the operation of subconscious biases and affect ML-driven decision support tools.

Julia Ive, Paulina Bondaronek, Vishal Yadav et al.

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.

Ammar Ahmed, Abdul Manaf

Wrist fractures are highly prevalent among children and can significantly impact their daily activities, such as attending school, participating in sports, and performing basic self-care tasks. If not treated properly, these fractures can result in chronic pain, reduced wrist functionality, and other long-term complications. Recently, advancements in object detection have shown promise in enhancing fracture detection, with systems achieving accuracy comparable to, or even surpassing, that of human radiologists. The YOLO series, in particular, has demonstrated notable success in this domain. This study is the first to provide a thorough evaluation of various YOLOv10 variants to assess their performance in detecting pediatric wrist fractures using the GRAZPEDWRI-DX dataset. It investigates how changes in model complexity, scaling the architecture, and implementing a dual-label assignment strategy can enhance detection performance. Experimental results indicate that our trained model achieved mean average precision (mAP@50-95) of 51.9\% surpassing the current YOLOv9 benchmark of 43.3\% on this dataset. This represents an improvement of 8.6\%. The implementation code is publicly available at https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection

Ling Lin, Yihang Zhou, Zhanqi Hu et al.

Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification models tailored to TSC in pediatric patients, introducing QResNet,a novel deep learning model seamlessly integrating conventional convolutional neural networks with quantum neural networks. The model incorporates a two-layer quantum layer (QL), comprising ZZFeatureMap and Ansatz layers, strategically designed for processing classical data within a quantum framework. A comprehensive evaluation, demonstrates the superior performance of QResNet in TSC MRI image classification compared to conventional 3D-ResNet models. These compelling findings underscore the potential of quantum computing to revolutionize medical imaging and diagnostics.Remarkably, this method surpasses conventional CNNs in accuracy and Area Under the Curve (AUC) metrics with the current dataset. Future research endeavors may focus on exploring the scalability and practical implementation of quantum algorithms in real-world medical imaging scenarios.

Fadillah Maani, Anees Ur Rehman Hashmi, Mariam Aljuboory et al.

Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain tumors. The BraTS challenge serves as a platform for researchers to tackle this issue by participating in open challenges focused on tumor segmentation. This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based CNN models, namely SegResNet and MedNeXt, for segmenting three distinct subregions of tumors. We further introduce a set of robust postprocessing to improve the segmentation, especially for the newly introduced BraTS 2023 metrics. The specifics of our approach and comprehensive performance analyses are expounded upon in this work. Our proposed approach achieves third place in the BraTS 2023 Adult Glioma Segmentation Challenges with an average of 0.8313 and 36.38 Dice and HD95 scores on the test set, respectively.

Meilinah Hidayat, Janice Natalia, Ardo Sanjaya et al.

High sodium intake infuences the development of chronic kidney disease (CKD). Various factors can infuence sodium consumption, one of which is impaired taste perception. This study aims to evaluate factors infuencing taste disorders and the impact of high intake of sodium, saliva, and zinc, especially in CKD patients. The method used involved searching for articles using Google Scholar, PubMed, EBSCO, and ProQuest search engines. The inclusion, exclusion criteria, and journal selection method, using Problem/Population, Intervention, Comparison, Outcome form and Prisma Flow Diagram, focused on experimental studies in the last ten years (2013-2023) with specifc search keywords. A total of 28 suitable articles matched the criteria. The results revealed three sub-themes: (A) Factors afecting sodium intake: Taste disorder/dysgeusia in CKD, (B) Efect of zinc on sodium intake or CKD, and (C) Efect of sodium on CKD. This study discusses the three most signifcant factors that infuence taste distortion: salt intake, saliva quality, and zinc defciency, besides old age. Taste disorders due to old age can be overcome with education and behavior planning. The habit of high sodium intake and saliva quality can be improved by reducing sodium intake, while the management of zinc defciency is addressed through supplementation. In summary, tasting disorders in CKD are strongly infuenced by high intake of sodium, saliva, and zinc defciency.

Jorgen Thorup, Jorgen Thorup, Simone Hildorf et al.

Cryptorchidism in males constitutes a notable risk factor for both infertility and testicular cancer. Infertility in adulthood is closely linked to the germ cell status in childhood. Furthermore, the significance of germ cell status is important as more than 95% of all reported testicular malignancies are germ cell tumors. The review aims to elucidate the pathogenesis of germ cells in cryptorchid testes concerning their association with infertility and testicular malignancies. Impaired germ cell numbers are evident in cryptorchid testes even during antenatal and neonatal stages. In cryptorchidism there is a rapid decline in germ cell number within the first year of life, partially attributed to physiologic gonocyte apoptosis. Additionally, germ cells fail to differentiate normally during mini-puberty leading to reduced germ cell proliferation and delayed clearance of gonocytes from the seminiferous epithelium. Absence of germ cells in testicular biopsies occurs already 10 months of age and germ cell deterioration progressively worsens with approximately 50% of persisting cryptorchid testes lacking germ cells during puberty. The deficient germ cell maturation and proliferation leads to later infertility. Elevated temperature in the cryptorchid testes and also hormonal deficiency contribute to this phenomenon. Germ cell neoplasia in situ (GCNIS) originating during fetal development may manifest in rare cases associated with disorders of sexual development, chromosomal abnormalities in boys, specific syndromes, and teratomas that include cryptorchidism. In adults, the presence of GCNIS predominantly represents a new histology pattern before invasive germ cell cancer is demonstrated and is neither congenital nor related to abnormal gonocyte transformation.

Jenni Gårdstedt-Berghog, Jenni Gårdstedt-Berghog, Aimon Niklasson et al.

ObjectivesTo explore the timing of menarche, postmenarcheal growth, and to investigate the impact of various variables on menarcheal age and postmenarcheal and pubertal growth.Study DesignThis longitudinal community population-based study analyzed pubertal growth and menarcheal age in 793 healthy term-born Swedish girls, a subset of the GrowUp1990Gothenburg cohort. The timing of menarche and postmenarcheal growth was related to variables from the Quadratic-Exponential-Pubertal-Stop (QEPS) growth model, birth characteristics, and parental height. Multivariable models were constructed for clinical milestones; at birth, age 7 years, pubertal growth onset, and midpuberty.ResultsMenarche aligned with 71.6% (18.8) of the QEPS model's specific pubertal growth function, at a mean age of 13.0 (1.3) years, ranging from 8.2 to 17.2 years. Postmenarcheal growth averaged 8.0 (4.9) cm, varying widely from 0.2 to 31.1 cm, decreasing with later menarche. Significant factors associated with menarcheal age included height at 7 years, childhood body-mass index, parental height, and QEPS-derived pubertal growth variables. Multivariable models demonstrated increasing explanatory power for each milestone, explaining 1% of the variance in menarcheal age at birth, 8% at age 7 years, 44% at onset of pubertal growth, and 45% at midpuberty.ConclusionsThis study underscores the strong link between pubertal growth and age at menarche. Data available at start of puberty explain 44% of the variation in menarcheal age, apparent on average 3.2 years before menarche. In addition, the study shows a previously seldom noticed wide variation in postmenarcheal height gain from 0.2 to 31.1 cm.