Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
JMIR research protocols
Evaluating the Effectiveness of an Enhanced Early Childhood Development Program Integrated Into Primary Health Care in China: Protocol for a Cluster Randomized Controlled Trial
BACKGROUND: Early childhood development (ECD) programs refer to policies and programs aimed at protecting young children's rights to achieve their full potential. Parenting interventions are effective at improving children's cognitive development and overall well-being. However, there is limited evidence on how to effectively implement and integrate such programs into routine service delivery at scale. The Government of China launched the ECD program in 2013 and the ECD scale-up program in 2023. OBJECTIVE: Our study aims to design, implement, and evaluate an enhanced ECD program embedded in the primary health care system in order to provide ECD policy recommendations for future national scale-up. METHODS: This study involves a multicenter cluster randomized controlled superiority trial in 3 counties with different levels of socioeconomic development in China. Fifty-eight clusters will be randomly assigned in a 1:1 ratio to intervention and control groups, stratified by county. In each cluster, 18 families with children aged 6-23 months will be recruited. Our intervention aims to implement an enhanced ECD program in primary care facilities. Intervention group participants will receive usual care alongside enhanced ECD services, including improved facilities, capacity building, and improved ECD services (age-adapted clinical consultations on nutrition and stimulation, age-specific parenting group sessions, home visits, and referrals for children in need). Moreover, an electronic data platform will be used. Control group participants will receive usual care. Our primary outcome is children's overall development, as measured by the Global Scale of Early Development combined form at 12 months of follow-up. Evaluation data will be collected in both arms. Additionally, a process evaluation and an incremental cost-effectiveness analysis will be conducted. Primary analysis will follow the intention-to-treat principle, and it will use generalized estimating equations to assess the population average intervention effect, with adjustment for corresponding baseline outcomes, selected individual variables, county, cluster variables, and variables differing at baseline. Qualitative data will be collected through semistructured interviews and focus groups, guided by the Capability, Opportunity, Motivation, and Behavior framework to explore implementation-related factors. Data will be transcribed for analysis using NVivo 14 (Lumivero). Ethical approval has been obtained from the Institutional Review Board of Shanghai Children's Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS: This study was funded in 2023 and registered in April 2024. The recruitment of 1044 participants was completed on September 14, 2024. Data collection was completed on January 31, 2026. CONCLUSIONS: We aim to design an enhanced ECD service delivery model that can be embedded into the routine primary health care system. The study findings will guide government policies and reforms for strengthening the health system to scale up ECD services. Furthermore, this project may provide policy evidence to support countries in establishing or expanding their ECD services.
29 May 2026
Read appraisal →Brain topography
rTMS Modulates Static and Dynamic Brain Functional Networks in Children with Autism Spectrum Disorder: An EEG Microstate Study
Repetitive Transcranial Magnetic Stimulation (rTMS) shows promise for treating Autism Spectrum Disorder (ASD), but its impact on the temporal dynamics of large-scale brain networks remains unclear. This study investigated the modulatory effects of rTMS on static and dynamic brain functional networks in children with ASD. Thirty-two children were randomized into an active rTMS group (1 Hz over the dorsolateral prefrontal cortex) and a sham control group. Resting-state EEG and behavioral assessments were conducted before and after a 9-week intervention. We employed a multi-dimensional analysis approach, combining microstate temporal parameters, static functional connectivity based on the weighted Phase Lag Index (wPLI), and dynamic complexity measured by Fuzzy Entropy. Results indicated that intrinsic features of Microstate B were significantly correlated with social relating deficits. Although rTMS did not induce significant interaction effects in standard microstate temporal parameters, it significantly enhanced static functional connectivity strength and increased the dynamic complexity of brain networks across all microstates. These findings suggest that rTMS exerts its therapeutic effects by strengthening network integration and restoring neural flexibility rather than simply altering the duration of brain states. The study underscores the value of network-based EEG metrics in elucidating the neuroplastic changes induced by neuromodulation in ASD.
12 May 2026
Read appraisal →Developmental cognitive neuroscience
Deep learning in fetal, infant, and toddler neuroimaging research.
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analysis has progressed more slowly due to high clinical stakes, limited availability of labeled data, and substantial variability in imaging protocols and population. These challenges are further pronounced in the field of fetal, infant, and toddler (FIT) neuroimaging, where datasets are especially scarce and subject to large amounts of anatomical variability. However, deep learning (DL), a specific method within machine learning, which is itself a subfield of AI, has emerged as a powerful framework to adapt to the challenges of medical image analysis. This review is written for the broad FIT research community, including clinicians, neuroscientists, and develop mental scientists who may not have formal training in AI. To make the material accessible, we provide a concise overview of DL concepts before reviewing a selected, and non-exhaustive, list of applications of DL in FIT neuroimaging, including structural image analysis, enhancement of data acquisition, modeling of cognitive and perceptual processes, and automated video tagging. In closing, we discuss best practices for data curation, ongoing challenges, and opportunities for future research.
4 Apr 2026
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